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Prof. Dr. Mi Wang
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
School of Aeronautics and Astronautics, Central South University, Changsha 410083, China
School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China
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High-Resolution Earth Observation Systems, Technologies, and Applications

Abstract submission deadline
closed (31 October 2021)
Manuscript submission deadline
closed (20 June 2022)
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Topic Information

Dear Colleagues,

In the past 20 years, many countries have attached great importance to the high-resolution Earth observation system (EOS), technology, and application. In particular, recent China’s Gaofen series satellites, from Gaofen-1 to Gaofen-13, had been successfully launched into space from 2013 to 2020. Until now, the global high-resolution EOS has covered panchromatic, multispectral, hyperspectral, visible, and microwave wavebands. It is fair to say that various high-resolution EOSs constitute the Earth observation with high spatial resolution, high temporal resolution, and high spectral resolution, which have provided strong support for improving the Earth observation capability.

From the perspective of development trend, the application prospect of high-resolution EOS is very extensive. We believe that more and more new high-resolution EOSs will be launched in the near future. Moreover, the application achievements of high-resolution EOS have been very rich at this stage. Therefore, this multidisciplinary topic aims to invite scholars to publish articles on the latest progress and the development trends of high-resolution EOSs, technologies, and applications.

Potential topics for this Topic include, but are not limited to:

  • Current and future high-resolution EOS and missions
  • Innovative Earth observation sensors, concepts, and techniques
  • Artificial intelligence in EOS remote sensing applications
  • On-board real-time processing of EOS remote sensing images
  • EOS remote sensing image recognition and interpretation
  • Quality improvement of EOS remote sensing images
  • High-precision geometric positioning of EOS remote sensing image
  • Super-resolution processing of EOS remote sensing image
  • Multi-source EOS image fusion
  • Other related topics

Prof. Dr. Mi Wang
Prof. Dr. Hanwen Yu
Dr. Jianlai Chen
Dr. Ying Zhu
Topic Editors

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Sensors
sensors 		3.4 7.3 2001 18.6 Days CHF 2600
Remote Sensing
remotesensing 		4.2 8.3 2009 23.9 Days CHF 2700

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Published Papers (180 papers)

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21 pages, 15965 KiB  
Article
Range Spectral Filtering in SAR Interferometry: Methods and Limitations
by Alejandro Mestre-Quereda, Juan M. Lopez-Sanchez and Jordi J. Mallorqui
Sensors 2022, 22(22), 8696; https://doi.org/10.3390/s22228696 - 10 Nov 2022
Cited by 1 | Viewed by 2246
Abstract
A geometrical decorrelation constitutes one of the sources of noise present in Synthetic Aperture Radar (SAR) interferograms. It comes from the different incidence angles of the two images used to form the interferograms, which cause a spectral (frequency) shift between them. A geometrical [...] Read more.
A geometrical decorrelation constitutes one of the sources of noise present in Synthetic Aperture Radar (SAR) interferograms. It comes from the different incidence angles of the two images used to form the interferograms, which cause a spectral (frequency) shift between them. A geometrical decorrelation must be compensated by a specific filtering technique known as range filtering, the goal of which is to estimate this spectral displacement and retain only the common parts of the images’ spectra, reducing the noise and improving the quality of the interferograms. Multiple range filters have been proposed in the literature. The most widely used methods are an adaptive filter approach, which estimates the spectral shift directly from the data; a method based on orbital information, which assumes a constant-slope (or flat) terrain; and slope-adaptive algorithms, which consider both orbital information and auxiliary topographic data. Their advantages and limitations are analyzed in this manuscript and, additionally, a new, more refined approach is proposed. Its goal is to enhance the filtering process by automatically adapting the filter to all types of surface variations using a multi-scale strategy. A pair of RADARSAT-2 images that mapped the mountainous area around the Etna volcano (Italy) are used for the study. The results show that filtering accuracy is improved with the new method including the steepest areas and vegetation-covered regions in which the performance of the original methods is limited. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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17 pages, 4988 KiB  
Article
A Height Nonlinear Velocity Field Algorithm for CORS Station Based on GARCH Model
by Hengjing Zhang, Huanling Liu, Dongdong Cui and Fang Zhang
Sensors 2022, 22(19), 7589; https://doi.org/10.3390/s22197589 - 6 Oct 2022
Cited by 1 | Viewed by 1634
Abstract
In this study, the basic concept of height nonlinear velocity field modeling in the CORS station is described. The noise results in a large deviation between the observation and predicted height. An ARCH testing method for heteroscedasticity of CORS height residual square series [...] Read more.
In this study, the basic concept of height nonlinear velocity field modeling in the CORS station is described. The noise results in a large deviation between the observation and predicted height. An ARCH testing method for heteroscedasticity of CORS height residual square series was proposed and the non-stationary characteristic of CORS height residual square time series was proved. A CORS height nonlinear velocity field reconstruction method based on the GARCH model was proposed. First, a nonlinear LS periodic fitting model was established for CORS height series data. Then, a GARCH model was established for the fitted non-stationary residual series. Finally, the signal term, linear trend term, and GARCH model noise term of nonlinear LS modeling were combined to reconstruct the nonlinear velocity field of the CORS height. The RMSE of nonlinear LS cycle modeling for 25 CORS stations worldwide ranged from 5 to 10 mm. The differences between the velocity, approximate annual and semi-annual amplitudes, and SOPAC results were 0.73 mm/a, 0.94 mm, and 0.51 mm, respectively. Compared with the centimeter amplitude of the CORS station height, the accuracy of the nonlinear model established in this study met the requirements. The results of height nonlinear velocity field reconstruction at 25 CORS stations worldwide showed that the mean square error of prediction of the one-year height movement reached 9 mm, and the average prediction accuracy of the semi-annual was 7 mm. Compared with the calculation accuracy of the current global CORS elevation component of 3–5 mm, the prediction error in this study was about 3 mm. The expected goal was achieved regarding the accuracy of the CORS station height nonlinear velocity field model. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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20 pages, 18545 KiB  
Article
An Automatic Drift-Measurement-Data-Processing Method with Digital Ionosondes
by Xiaoya Ma, Zhaoqian Gong, Feng Zhang, Shun Wang, Xiaojun Liu and Guangyou Fang
Remote Sens. 2022, 14(19), 4710; https://doi.org/10.3390/rs14194710 - 21 Sep 2022
Cited by 1 | Viewed by 1797
Abstract
Drift detection is one of the important detection modes in a digital ionosonde system. In this paper, a new data processing method is presented for boosting the automatic and high-quality drift measurement, which is helpful for long-term ionospheric observation, and has been successfully [...] Read more.
Drift detection is one of the important detection modes in a digital ionosonde system. In this paper, a new data processing method is presented for boosting the automatic and high-quality drift measurement, which is helpful for long-term ionospheric observation, and has been successfully applied to the Chinese Academy of Sciences, Digital Ionosonde (CAS-DIS). Based on Doppler interferometry principle, this method can be successively divided into four constraint steps: extracting the stable echo data; restricting the ionospheric detection region; extracting the reliable reflection cluster, including Doppler filtering and coarse clustering analysis; and calculating the drift velocity. Ordinary wave (O-wave) data extraction, complementary code pulse compression and other data preprocessing techniques are used to improve the signal-to-noise ratio (SNR) of echo data. For the purpose of eliminating multiple echoes, the ionospheric region is determined by combining the optimal height range and detection frequencies obtained from the ionogram. Successively, Doppler filtering and coarse clustering analysis extract reliable reflection clusters. Finally, the weighting factor is brought in, and then weighted least-squares (WLS) is used to fit the drift velocity. The entire data processing process can be implemented automatically without constantly changing parameter settings due to changes in external conditions. This is the first time coarse clustering analysis has been used to extract the paracentral reflection cluster to eliminate scattered reflection points and outer reflection clusters, which further reduces the impacts of external conditions on parameter settings and improves the ability of automatic drift measurement. Compared with the previous method possessed by Digisonde Protable Sounder 4D (DPS4D), the new method can achieve comparable drift detection precision and results even with fewer reflection points. In 2021–2022, several experiments on F region drift detection were carried out in Hainan, China. Results indicate that drift velocities fitted by the new method have diurnal variation and change more gently; the trends of drift velocities fitted by the new method and the previous method are semblable; and this new method can be widely applied to digital ionosondes. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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19 pages, 11871 KiB  
Article
A Ship Detection and Imagery Scheme for Airborne Single-Channel SAR in Coastal Regions
by Zhenyu Li, Jianlai Chen, Yi Xiong, Hanwen Yu, Huaigen Zhang and Bing Gao
Remote Sens. 2022, 14(18), 4670; https://doi.org/10.3390/rs14184670 - 19 Sep 2022
Cited by 2 | Viewed by 2058
Abstract
Ship detection and management in coastal regions are challenging tasks due to the complex appearances of ships and the background. For further applications in the context of fisheries monitoring and vessel traffic services, a single-channel synthetic aperture radar (SAR) is mounted on a [...] Read more.
Ship detection and management in coastal regions are challenging tasks due to the complex appearances of ships and the background. For further applications in the context of fisheries monitoring and vessel traffic services, a single-channel synthetic aperture radar (SAR) is mounted on a number of maneuvering and inexpensive rotor platforms, which are utilized according to the consideration of flexible observation, cost savings, weight, and space constraints. In this paper, a hierarchical scheme of ship detection, ship imaging, and classification is proposed. It mainly includes three parts. First, a mixture statistical model of semi-parametric K-lognormal distribution based on adaptive background windows with a constant false alarm rate (CFAR) is proposed for ship prescreening in SAR imagery. Then, the discrimination stage, combined with ship imaging via the difference between the true ship targets and the false ones in the aspects of micro-Doppler motion properties, is performed. Finally, the simulation and field data processing results are presented to validate the proposed scheme. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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10 pages, 3250 KiB  
Communication
A Truck-Borne System Based on Cold Atom Gravimeter for Measuring the Absolute Gravity in the Field
by Helin Wang, Kainan Wang, Yunpeng Xu, Yituo Tang, Bin Wu, Bing Cheng, Leyuan Wu, Yin Zhou, Kanxing Weng, Dong Zhu, Peijun Chen, Kaijun Zhang and Qiang Lin
Sensors 2022, 22(16), 6172; https://doi.org/10.3390/s22166172 - 18 Aug 2022
Cited by 25 | Viewed by 3032
Abstract
The cold atom gravimeter (CAG) has proven to be a powerful quantum sensor for the high-precision measurement of gravity field, which can work stably for a long time in the laboratory. However, most CAGs cannot operate in the field due to their complex [...] Read more.
The cold atom gravimeter (CAG) has proven to be a powerful quantum sensor for the high-precision measurement of gravity field, which can work stably for a long time in the laboratory. However, most CAGs cannot operate in the field due to their complex structure, large volume and poor environmental adaptability. In this paper, a home-made, miniaturized CAG is developed and a truck-borne system based on it is integrated to measure the absolute gravity in the field. The measurement performance of this system is evaluated by applying it to measurements of the gravity field around the Xianlin reservoir in Hangzhou City of China. The internal and external coincidence accuracies of this measurement system were demonstrated to be 35.4 μGal and 76.7 μGal, respectively. Furthermore, the theoretical values of the measured eight points are calculated by using a forward modeling of a local high-resolution digital elevation model, and the calculated values are found to be in good agreement with the measured values. The results of this paper show that this home-made, truck-borne CAG system is reliable, and it is expected to improve the efficiency of gravity surveying in the field. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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18 pages, 3700 KiB  
Article
Crop Mapping Using the Historical Crop Data Layer and Deep Neural Networks: A Case Study in Jilin Province, China
by Deyang Jiang, Shengbo Chen, Juliana Useya, Lisai Cao and Tianqi Lu
Sensors 2022, 22(15), 5853; https://doi.org/10.3390/s22155853 - 5 Aug 2022
Cited by 8 | Viewed by 2479
Abstract
Machine learning combined with satellite image time series can quickly, and reliably be implemented to map crop distribution and growth monitoring necessary for food security. However, obtaining a large number of field survey samples for classifier training is often time-consuming and costly, which [...] Read more.
Machine learning combined with satellite image time series can quickly, and reliably be implemented to map crop distribution and growth monitoring necessary for food security. However, obtaining a large number of field survey samples for classifier training is often time-consuming and costly, which results in the very slow production of crop distribution maps. To overcome this challenge, we propose an ensemble learning approach from the existing historical crop data layer (CDL) to automatically create multitudes of samples according to the rules of spatiotemporal sample selection. Sentinel-2 monthly composite images from 2017 to 2019 for crop distribution mapping in Jilin Province were mosaicked and classified. Classification accuracies of four machine learning algorithms for a single-month and multi-month time series were compared. The results show that deep neural network (DNN) performed the best, followed by random forest (RF), then decision tree (DT), and support vector machine (SVM) the least. Compared with other months, July and August have higher classification accuracy, and the kappa coefficients of 0.78 and 0.79, respectively. Compared with a single phase, the kappa coefficient gradually increases with the growth of the time series, reaching 0.94 in August at the earliest, and then the increase is not obvious, and the highest in the whole growth cycle is 0.95. During the mapping process, time series of different lengths produced different classification results. Wetland types were misclassified as rice. In such cases, authors combined time series of two lengths to correct the misclassified rice types. By comparing with existing products and field points, rice has the highest consistency, followed by corn, whereas soybeans have the least consistency. This shows that the generated sample data set and trained model in this research can meet the crop mapping accuracy and simultaneously reduce the cost of field surveys. For further research, more years and types of crops should be considered for mapping and validation. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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15 pages, 4628 KiB  
Article
Real-Time Imaging Processing of Squint Spaceborne SAR with High-Resolution Based on Nonuniform PRI Design
by Yanghao Jin, Buge Liang, Jianlai Chen, Yi Xiong and Mingyao Xiong
Remote Sens. 2022, 14(15), 3725; https://doi.org/10.3390/rs14153725 - 3 Aug 2022
Cited by 2 | Viewed by 2048
Abstract
The real-time imaging research of squint spaceborne synthetic aperture radar (SAR) with high resolution has significant value in both military and civil fields, which makes it a hot issue in SAR research. It is necessary to solve the contradictory problems of nonlinear trajectory [...] Read more.
The real-time imaging research of squint spaceborne synthetic aperture radar (SAR) with high resolution has significant value in both military and civil fields, which makes it a hot issue in SAR research. It is necessary to solve the contradictory problems of nonlinear trajectory and efficient imaging at the same time in order to achieve the two goals, high-resolution and real-time imaging. A large number of complex operations are required in the accurate correction algorithms for nonlinear trajectory, which will reduce the imaging efficiency, and this problem becomes more prominent with the improvement of resolution. To solve the above problems, this paper proposes a new real-time imaging processing of squint high-resolution SAR, which eliminates the velocity–azimuth variation caused by nonlinear trajectory in the data acquisition stage through nonuniform pulse repetition interval (PRI) design. The imaging efficiency has been greatly improved because the new method avoids the complex azimuth resampling operation. Simulation experiments verify the effectiveness of the method. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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19 pages, 6043 KiB  
Article
Translational Compensation Algorithm for Ballistic Targets in Midcourse Based on Template Matching
by Buge Liang, Zhenghong Peng, Degui Yang, Xing Wang and Jin Li
Remote Sens. 2022, 14(15), 3678; https://doi.org/10.3390/rs14153678 - 1 Aug 2022
Viewed by 1828
Abstract
The high-speed movement of a ballistic target will cause folding and translation of the micro-Doppler, which will affect the extraction of micro-motion features. To address the adverse effects of high-speed movement of ballistic targets in midcourse on the extraction of micro-motion features, a [...] Read more.
The high-speed movement of a ballistic target will cause folding and translation of the micro-Doppler, which will affect the extraction of micro-motion features. To address the adverse effects of high-speed movement of ballistic targets in midcourse on the extraction of micro-motion features, a novel translational compensation algorithm based on template matching is proposed. Firstly, a 512 × 512 time-frequency map is obtained by binarization and down-sampling. The matching template then convolves the time-frequency map to obtain contour-like points. Then, the upper and lower contour points are preliminarily determined by the extreme value, and all actual contour points are screened out through structural similarity. Lastly, the upper and lower trend lines are determined and translation parameters for compensation by polynomial fitting are estimated. Simulation results show that the proposed algorithm has lower requirements for time-frequency resolution, higher precision and lower time complexity as a whole. Furthermore, it is also applicable to spectral aliasing. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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20 pages, 3876 KiB  
Article
Expandable On-Board Real-Time Edge Computing Architecture for Luojia3 Intelligent Remote Sensing Satellite
by Zhiqi Zhang, Zhuo Qu, Siyuan Liu, Dehua Li, Jinshan Cao and Guangqi Xie
Remote Sens. 2022, 14(15), 3596; https://doi.org/10.3390/rs14153596 - 27 Jul 2022
Cited by 26 | Viewed by 3314
Abstract
Since the data generation rate of high-resolution satellites is increasing rapidly, to relieve the stress of data downloading and processing systems while enhancing the time efficiency of information acquisition, it is important to deploy on-board edge computing on satellites. However, the volume, weight, [...] Read more.
Since the data generation rate of high-resolution satellites is increasing rapidly, to relieve the stress of data downloading and processing systems while enhancing the time efficiency of information acquisition, it is important to deploy on-board edge computing on satellites. However, the volume, weight, and computability of on-board systems are strictly limited by the harsh space environment. Therefore, it is very difficult to match the computability and the requirements of diversified intelligent applications. Currently, this problem has become the first challenge of the practical deployment of on-board edge computing. To match the actual requirements of the Luojia3 satellite of Wuhan University, this manuscript proposes a three-level edge computing architecture based on a System-on-Chip (SoC) for low power consumption and expandable on-board processing. First, a transfer level is designed to focus on hardware communications and Input/Output (I/O) works while maintaining a buffer to store image data for upper levels temporarily. Second, a processing framework that contains a series of libraries and Application Programming Interfaces (APIs) is designed for the algorithms to easily build parallel processing applications. Finally, an expandable level contains multiple intelligent remote sensing applications that perform data processing efficiently using base functions, such as instant geographic locating and data picking, stream computing balance model, and heterogeneous parallel processing strategy that are provided by the architecture. It is validated by the performance improvement experiment that following this architecture, using these base functions can help the Region of Interest (ROI) system geometric correction fusion algorithm to be 257.6 times faster than the traditional method that processes scene by scene. In the stream computing balance experiment, relying on this architecture, the time-consuming algorithm ROI stabilization production can maintain stream computing balance under the condition of insufficient computability. We predict that based on this architecture, with the continuous development of device computability, the future requirements of on-board computing could be better matched. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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19 pages, 47342 KiB  
Article
An Advanced Echo Separation Scheme for Space-Time Waveform-Encoding SAR Based on Digital Beamforming and Blind Source Separation
by Sheng Chang, Yunkai Deng, Yanyan Zhang, Rongxiang Wang, Jinsong Qiu, Wei Wang, Qingchao Zhao and Dacheng Liu
Remote Sens. 2022, 14(15), 3585; https://doi.org/10.3390/rs14153585 - 26 Jul 2022
Cited by 17 | Viewed by 2423
Abstract
To achieve high-resolution and wide-swath (HRWS) imaging, a space-time waveform-encoding (STWE) spaceborne synthetic aperture radar (SAR) system is adopted. In rugged terrain, the beam-pointing mismatch problem will appear when the traditional digital beamforming (DBF) technique is used to separate the received echoes. This [...] Read more.
To achieve high-resolution and wide-swath (HRWS) imaging, a space-time waveform-encoding (STWE) spaceborne synthetic aperture radar (SAR) system is adopted. In rugged terrain, the beam-pointing mismatch problem will appear when the traditional digital beamforming (DBF) technique is used to separate the received echoes. This problem leads to decreasing the received echo’s gain, deteriorating the quality of the image product and making the interpretation of SAR image difficult. To address this problem, an advanced echo separation scheme for STWE spaceborne SAR based on the DBF and blind source separation (BSS) is put forward in this paper. In the scheme, the echoes are transmitted within the adjacent pulse repetition intervals to simulate multiple beams, and the scattered echoes are received by the sixteen-channel antennas in elevation simultaneously. In post-processing, a detailed flow is adopted. In the method, the DBF is firstly performed on received echoes. Due to the error caused by terrain undulation, the degree of echo separation is not enough. Then, the BSS is performed on the multiple echoes obtained after the DBF processing. Finally, the highly separated echo signal can be obtained. In this scheme, there is no need to perform the direction of arrival (DOA) estimation before the DBF processing, which saves valuable computing resources. In addition, to verify the proposed scheme, point target and distributed target simulations based on the 16-channel data of an elevation X-band DBF-SAR system are carried out. The results of point targets indicate that the residual echo caused by rough terrain can be reduced by more than 14 dB using the proposed scheme. The proposed scheme can be directly implemented into existing SAR systems; thus, it does not increase the complexity of the system design. The scheme has the potential to be applied to future spaceborne SAR missions. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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17 pages, 4389 KiB  
Article
A Transmission Efficiency Evaluation Method of Adaptive Coding Modulation for Ka-Band Data-Transmission of LEO EO Satellites
by Zhongguo Wang, Fan Lu, Dabao Wang, Xiao Zhang, Jionghui Li and Jindong Li
Sensors 2022, 22(14), 5423; https://doi.org/10.3390/s22145423 - 20 Jul 2022
Cited by 2 | Viewed by 2134
Abstract
Nowadays low Earth orbit (LEO) Earth observation (EO) satellites commonly use constant coding modulation (CCM) or variable coding modulation (VCM) schemes for data transmission to ground stations (G/S). Compared with CCM and VCM, the adaptive coding modulation (ACM) could further improve the data [...] Read more.
Nowadays low Earth orbit (LEO) Earth observation (EO) satellites commonly use constant coding modulation (CCM) or variable coding modulation (VCM) schemes for data transmission to ground stations (G/S). Compared with CCM and VCM, the adaptive coding modulation (ACM) could further improve the data throughput of the link by making full use of link resource and the time-varying characteristics of atmospheric attenuation. In order to comprehensively study the data transmission performance, one new index which could be utilized as a quantitative index for the satellite-to-ground data transmission scheme selection, the transmission efficiency factor (TEF) of LEO satellites is proposed and defined as “the product of the link availability and the average useful data rate”. Then, the transmission efficiency of CCM, VCM and ACM at typical G/S with different weather characteristics at Ka-band is compared and analyzed. The results show that ACM is more suitable for the G/S with moderate and abundant rainfall. Compared with the CCM of MCS 28, for Beijing G/S and Sanya G/S, ACM not only improves the transmission efficiency with the TEF increased by 3.62% and 24.51%, respectively, but also improves the link availability with the outage period reduced by 82.47% and 75.18%, respectively. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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15 pages, 3750 KiB  
Article
Generating Daily Soil Moisture at 16 m Spatial Resolution Using a Spatiotemporal Fusion Model and Modified Perpendicular Drought Index
by Xin Lu, Hongli Zhao, Yanyan Huang, Shuangmei Liu, Zelong Ma, Yunzhong Jiang, Wei Zhang and Chuan Zhao
Sensors 2022, 22(14), 5366; https://doi.org/10.3390/s22145366 - 19 Jul 2022
Cited by 4 | Viewed by 2003
Abstract
Soil moisture (SM) is an important parameter in land surface processes and the global water cycle. Remote sensing technologies are widely used to produce global-scale SM products (e.g., European Space Agency’s Climate Change Initiative (ESA CCI)). However, the current spatial resolutions of such [...] Read more.
Soil moisture (SM) is an important parameter in land surface processes and the global water cycle. Remote sensing technologies are widely used to produce global-scale SM products (e.g., European Space Agency’s Climate Change Initiative (ESA CCI)). However, the current spatial resolutions of such products are low (e.g., >3 km). In recent years, using auxiliary data to downscale the spatial resolutions of SM products has been a hot research topic in the remote sensing research area. A new method, which spatially downscalesan SM product to generate a daily SM dataset at a 16 m spatial resolution based on a spatiotemporal fusion model (STFM) and modified perpendicular drought index (MPDI), was proposed in this paper. (1) First, a daily surface reflectance dataset with a 16 m spatial resolution was produced based on an STFM. (2) Then, a spatial scale conversion factor (SSCF) dataset was obtained by an MPDI dataset, which was calculated based on the dataset fused in the first step. (3) Third, a downscaled daily SM product with a 16 m spatial resolution was generated by combining the SSCF dataset and the original SM product. Five cities in southern Hebei Province were selected as study areas. Two 16 m GF6 images and nine 500 m MOD09GA images were used as auxiliary data to downscale a timeseries 25 km CCI SM dataset for nine dates from May to June 2019. A total of 151 in situ SM observations collected on 1 May, 21 May, 1 June, and 11 June were used for verification. The results indicated that the downscaled SM data with a 16 m spatial resolution had higher correlation coefficients and lower RMSE values compared with the original CCI SM data. The correlation coefficients between the downscaled SM data and in situ data ranged from 0.45 to 0.67 versus 0.33 to 0.54 for the original CCI SM data; the RMSE values ranged from 0.023 to 0.031 cm3/cm3 versus 0.027 to 0.032 cm3/cm3 for the original CCI SM data. The findings described in this paper can ensure effective farmland management and other practical production applications. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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11 pages, 3254 KiB  
Article
GRACE Combined with WSD to Assess the Change in Drought Severity in Arid Asia
by Jiawei Liu, Guofeng Zhu, Kailiang Zhao, Yinying Jiao, Yuwei Liu, Mingyue Yang, Wenhao Zhang, Dongdong Qiu, Xinrui Lin and Linlin Ye
Remote Sens. 2022, 14(14), 3454; https://doi.org/10.3390/rs14143454 - 18 Jul 2022
Cited by 6 | Viewed by 2199
Abstract
Gravity Recovery and Climate Experiment (GRACE) satellite data are widely used in drought studies. In this study, we quantified drought severity based on land terrestrial water storage (TWS) changes in GRACE data. We used the water storage deficit (WSD) and water storage deficit [...] Read more.
Gravity Recovery and Climate Experiment (GRACE) satellite data are widely used in drought studies. In this study, we quantified drought severity based on land terrestrial water storage (TWS) changes in GRACE data. We used the water storage deficit (WSD) and water storage deficit index (WSDI) to identify the drought events and evaluate the drought severity. The WSDI calculated by GRACE provides an effective assessment method when assessing the extent of drought over large areas under a lack of site data. The results show a total of 22 drought events in the central Asian dry zone during the study period. During spring and autumn, the droughts among these incidents occurred more frequently and severely. The longest and most severe drought occurred near the Caspian Sea. In the arid area of central Asia, the north of the region tended to be moist (the WSDI value was 0.04 year−1), and the south, east, and Caspian Sea area tended to be drier (the WSDI values were −0.07 year−1 in the south, −0.11 year−1 in the east, and −0.19 year−1 in the Caspian Sea). These study results can provide a key scientific basis for agricultural development, food security, and climate change response in the Asian arid zone. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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21 pages, 9641 KiB  
Article
Microseismic Monitoring and Analysis Using Cutting-Edge Technology: A Key Enabler for Reservoir Characterization
by Daniel Wamriew, Desmond Batsa Dorhjie, Daniil Bogoedov, Roman Pevzner, Evgenii Maltsev, Marwan Charara, Dimitri Pissarenko and Dmitry Koroteev
Remote Sens. 2022, 14(14), 3417; https://doi.org/10.3390/rs14143417 - 17 Jul 2022
Cited by 7 | Viewed by 3890
Abstract
Microseismic monitoring is a useful enabler for reservoir characterization without which the information on the effects of reservoir operations such as hydraulic fracturing, enhanced oil recovery, carbon dioxide, or natural gas geological storage would be obscured. This research provides a new breakthrough in [...] Read more.
Microseismic monitoring is a useful enabler for reservoir characterization without which the information on the effects of reservoir operations such as hydraulic fracturing, enhanced oil recovery, carbon dioxide, or natural gas geological storage would be obscured. This research provides a new breakthrough in the tracking of the reservoir fracture network and characterization by detecting the microseismic events and locating their sources in real-time during reservoir operations. The monitoring was conducted using fiber optic distributed acoustic sensors (DAS) and the data were analyzed by deep learning. The use of DAS for microseismic monitoring is a game changer due to its excellent temporal and spatial resolution as well as cost-effectiveness. The deep learning approach is well-suited to dealing in real-time with the large amounts of data recorded by DAS equipment due to its computational speed. Two convolutional neural network based models were evaluated and the best one was used to detect and locate microseismic events from the DAS recorded field microseismic data from the FORGE project in Milford, United States. The results indicate the capability of deep neural networks to simultaneously detect and locate microseismic events from the raw DAS measurements. The results showed a small percentage error. In addition to the high spatial and temporal resolution, fiber optic cables are durable and can be installed permanently in the field and be used for decades. They are also resistant to high pressure, can withstand considerably high temperature, and therefore can be used even during field operations such as a flooding or hydraulic fracture stimulation. Deep neural networks are very robust; need minimum data pre-processing, can handle large volumes of data, and are able to perform multiple computations in a time- and cost-effective way. Once trained, the network can be easily adopted to new conditions through transfer learning. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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20 pages, 13868 KiB  
Article
A Multi-Rotor Drone Micro-Motion Parameter Estimation Method Based on CVMD and SVD
by Degui Yang, Jin Li, Buge Liang, Xing Wang and Zhenghong Peng
Remote Sens. 2022, 14(14), 3326; https://doi.org/10.3390/rs14143326 - 10 Jul 2022
Cited by 8 | Viewed by 2506
Abstract
It is of great significance to detect drones in airspace due to the substantial increase and regrettable misuse in the consumer market. In this paper, we establish a micro-motion theoretical model of a drone and analyze the micro-Doppler signature of rotor targets and [...] Read more.
It is of great significance to detect drones in airspace due to the substantial increase and regrettable misuse in the consumer market. In this paper, we establish a micro-motion theoretical model of a drone and analyze the micro-Doppler signature of rotor targets and the flicker mechanisms of the multi-rotor targets. Hence, for the target recognition problem of multi-rotor drones, a multi-rotor target micro-Doppler parameter estimation method is proposed. Firstly, a signal frequency domain segmentation method is proposed based on the complex variational mode decomposition (CVMD) to separate the high-frequency part of the high-frequency flicker in the frequency domain. Secondly, for the signal after frequency domain segmentation, a flicker time domain position method based on singular value decomposition (SVD) is proposed. Finally, by integrating CVMD frequency domain segmentation and SVD time domain positioning, the reconstruction of multi-rotor target scintillation at different speeds is realized, and the micro-motion parameters of rotor blades are successfully estimated. The simulation results show that the method has high accuracy in estimating the micro-motion parameters of a multi-rotor, which makes up for the shortage of the traditional method in estimating the micro-motion parameters of the multi-rotor target. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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19 pages, 4650 KiB  
Article
Using InSAR and PolSAR to Assess Ground Displacement and Building Damage after a Seismic Event: Case Study of the 2021 Baicheng Earthquake
by Xiaolin Sun, Xi Chen, Liao Yang, Weisheng Wang, Xixuan Zhou, Lili Wang and Yuan Yao
Remote Sens. 2022, 14(13), 3009; https://doi.org/10.3390/rs14133009 - 23 Jun 2022
Cited by 13 | Viewed by 3229
Abstract
During unexpected earthquake catastrophes, timely identification of damaged areas is critical for disaster management. On the 24 March 2021, Baicheng county was afflicted by a Mw 5.3 earthquake. The disaster resulted in three deaths and many human injuries. As an active remote sensing [...] Read more.
During unexpected earthquake catastrophes, timely identification of damaged areas is critical for disaster management. On the 24 March 2021, Baicheng county was afflicted by a Mw 5.3 earthquake. The disaster resulted in three deaths and many human injuries. As an active remote sensing technology independent of light and weather, the increasingly accessible Synthetic Aperture Radar (SAR) is an attractive data for assessing building damage. This paper aims to use Sentinel-1A radar images to rapidly assess seismic damage in the early phases after the disaster. A simple and robust method is used to complete the task of surface displacement analysis and building disaster monitoring. In order to obtain the coseismic deformation field, differential interferometry, filtering and phase unwrapping are performed on images before and after the earthquake. In order to detect the damage area of buildings, the Interferometric Synthetic Aperture Radar (InSAR) and Polarimetric Synthetic Aperture Radar (PolSAR) techniques are used. A simple and fast method combining coherent change detection and polarimetric decomposition is proposed, and the complete workflow is introduced in detail. In our experiment, we compare the detection results with the ground survey data using an unmanned aerial vehicle (UAV) after the earthquake to verify the performance of the proposed method. The results indicate that the experiment can accurately obtain the coseismic deformation field and identify the damaged and undamaged areas of the buildings. The correct identification accuracy of collapsed and severely damaged areas is 86%, and that of slightly damaged and undamaged areas is 84%. Therefore, the proposed method is extremely effective in monitoring seismic-affected areas and immediately assessing post-earthquake building damage. It provides a considerable prospect for the application of SAR technology. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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22 pages, 6525 KiB  
Article
PolSAR Ship Detection Based on a SIFT-like PolSAR Keypoint Detector
by Mingfei Gu, Yinghua Wang, Hongwei Liu and Penghui Wang
Remote Sens. 2022, 14(12), 2900; https://doi.org/10.3390/rs14122900 - 17 Jun 2022
Cited by 9 | Viewed by 2221
Abstract
The detection of ships on the open sea is an important issue for both military and civilian fields. As an active microwave imaging sensor, synthetic aperture radar (SAR) is a useful device in marine supervision. To extract small and weak ships precisely in [...] Read more.
The detection of ships on the open sea is an important issue for both military and civilian fields. As an active microwave imaging sensor, synthetic aperture radar (SAR) is a useful device in marine supervision. To extract small and weak ships precisely in the marine areas, polarimetric synthetic aperture radar (PolSAR) data have been used more and more widely. We propose a new PolSAR ship detection method which is based on a keypoint detector, referred to as a PolSAR-SIFT keypoint detector, and a patch variation indicator in this paper. The PolSAR-SIFT keypoint detector proposed in this paper is inspired by the SAR-SIFT keypoint detector. We improve the gradient definition in the SAR-SIFT keypoint detector to adapt to the properties of PolSAR data by defining a new gradient based on the distance measurement of polarimetric covariance matrices. We present the application of PolSAR-SIFT keypoint detector to the detection of ship targets in PolSAR data by combining the PolSAR-SIFT keypoint detector with the patch variation indicator we proposed before. The keypoints extracted by the PolSAR-SIFT keypoint detector are usually located in regions with corner structures, which are likely to be ship regions. Then, the patch variation indicator is used to characterize the context information of the extracted keypoints, and the keypoints located on the sea area are filtered out by setting a constant false alarm rate threshold for the patch variation indicator. Finally, a patch centered on each filtered keypoint is selected. Then, the detection statistics in the patch are calculated. The detection statistics are binarized according to the local threshold set by the detection statistic value of the keypoint to complete the ship detection. Experiments on three data sets obtained from the RADARSAT-2 and AIRSAR quad-polarization data demonstrate that the proposed detector is effective for ship detection. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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21 pages, 3620 KiB  
Article
Hyperspectral Image Classification Based on Class-Incremental Learning with Knowledge Distillation
by Meng Xu, Yuanyuan Zhao, Yajun Liang and Xiaorui Ma
Remote Sens. 2022, 14(11), 2556; https://doi.org/10.3390/rs14112556 - 26 May 2022
Cited by 16 | Viewed by 3038
Abstract
By virtue of its large-covered spatial information and high-resolution spectral information, hyperspectral images make lots of mapping-based fine-grained remote sensing applications possible. However, due to the inconsistency of land-cover types between different images, most hyperspectral image classification methods keep their effectiveness by training [...] Read more.
By virtue of its large-covered spatial information and high-resolution spectral information, hyperspectral images make lots of mapping-based fine-grained remote sensing applications possible. However, due to the inconsistency of land-cover types between different images, most hyperspectral image classification methods keep their effectiveness by training on every image and saving all classification models and training samples, which limits the promotion of related remote sensing tasks. To deal with the aforementioned issues, this paper proposes a hyperspectral image classification method based on class-incremental learning to learn new land-cover types without forgetting the old ones, which enables the classification method to classify all land-cover types with one final model. Specially, when learning new classes, a knowledge distillation strategy is designed to recall the information of old classes by transferring knowledge to the newly trained network, and a linear correction layer is proposed to relax the heavy bias towards the new class by reapportioning information between different classes. Additionally, the proposed method introduces a channel attention mechanism to effectively utilize spatial–spectral information by a recalibration strategy. Experimental results on the three widely used hyperspectral images demonstrate that the proposed method can identify both new and old land-cover types with high accuracy, which proves the proposed method is more practical in large-coverage remote sensing tasks. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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16 pages, 7538 KiB  
Article
High-Precision Joint Magnetization Vector Inversion Method of Airborne Magnetic and Gradient Data with Structure and Data Double Constraints
by Guoqing Ma, Yanan Zhao, Bowen Xu, Lili Li and Taihan Wang
Remote Sens. 2022, 14(10), 2508; https://doi.org/10.3390/rs14102508 - 23 May 2022
Cited by 8 | Viewed by 2364
Abstract
Airborne magnetic and gradient measurements are commonly used geophysical remote sensing tools to obtain the distribution features of ore mineral bodies. It is known that ore mineral bodies generally contain remanent magnetization, and magnetization vector inversion (MVI) can produce the magnetization intensity and [...] Read more.
Airborne magnetic and gradient measurements are commonly used geophysical remote sensing tools to obtain the distribution features of ore mineral bodies. It is known that ore mineral bodies generally contain remanent magnetization, and magnetization vector inversion (MVI) can produce the magnetization intensity and direction of the source, which is more suitably used to interpret measured airborne magnetic and gradient data. To accurately reveal the underground magnetization vector distribution, we proposed a high-precision method with double constraints on the data and physical structure, and we used the cross-gradient inversion of airborne magnetic anomalies and the combination matrix of airborne magnetic and gradient (CMG) data to recover the physical parameters of the sources with different depths. We used the combination matrix to produce the different component data constraints and the cross-gradient function to finish the inversion to provide structural constraints. For anomaly sources at similar depths, joint inversion based on the cross-gradient of magnetic gradient data and CMG data is more suitably used. The superiority of the double constraints method is proven by theoretical model tests. We apply the proposed method to interpret airborne magnetic and gradient data in Shandong Province to detect iron mineral resources, and we select the cross-gradient inversion of airborne magnetic anomalies and CMG data depending on the nonlinear features of the power spectrum. The main ore bodies have a northeast distribution with a depth range of 1048–1800 m, successfully giving the distribution range of the high-magnetic bodies; a better mineral potential is in the northern part of the survey area. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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21 pages, 12969 KiB  
Article
A Morphological Feature-Oriented Algorithm for Extracting Impervious Surface Areas Obscured by Vegetation in Collaboration with OSM Road Networks in Urban Areas
by Taomin Mao, Yewen Fan, Shuang Zhi and Jinshan Tang
Remote Sens. 2022, 14(10), 2493; https://doi.org/10.3390/rs14102493 - 23 May 2022
Cited by 4 | Viewed by 2416
Abstract
Remote sensing is the primary way to extract the impervious surface areas (ISAs). However, the obstruction of vegetation is a long-standing challenge that prevents the accurate extraction of urban ISAs. Currently, there are no general and systematic methods to solve the problem. In [...] Read more.
Remote sensing is the primary way to extract the impervious surface areas (ISAs). However, the obstruction of vegetation is a long-standing challenge that prevents the accurate extraction of urban ISAs. Currently, there are no general and systematic methods to solve the problem. In this paper, we present a morphological feature-oriented algorithm, which can make use of the OSM road network information to remove the obscuring effects when the ISAs are extracted. Very high resolution (VHR) images of Wuhan, China, were used in experiments to verify the effectiveness of the proposed algorithm. Experimental results show that the proposed algorithm can improve the accuracy and completeness of ISA extraction by our previous deep learning-based algorithm. In the proposed algorithm, the overall accuracy (OA) is 86.64%. The results show that the proposed algorithm is feasible and can extract the vegetation-obscured ISAs effectively and precisely. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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20 pages, 13332 KiB  
Article
A Real-Time GNSS-R System for Monitoring Sea Surface Wind Speed and Significant Wave Height
by Jin Xing, Baoguo Yu, Dongkai Yang, Jie Li, Zhejia Shi, Guodong Zhang and Feng Wang
Sensors 2022, 22(10), 3795; https://doi.org/10.3390/s22103795 - 17 May 2022
Cited by 10 | Viewed by 3389
Abstract
This paper presents a monitoring system based on Global Navigation Satellite System (GNSS) reflected signals to provide real-time observations of sea conditions. Instead of a computer, the system uses a custom-built hardware platform that incorporates Radio Frequency (RF), Field Programmable Gate Array (FPGA), [...] Read more.
This paper presents a monitoring system based on Global Navigation Satellite System (GNSS) reflected signals to provide real-time observations of sea conditions. Instead of a computer, the system uses a custom-built hardware platform that incorporates Radio Frequency (RF), Field Programmable Gate Array (FPGA), Digital Signal Processing (DSP), and Raspberry Pi for real-time signal processing. The suggested structure completes the navigation signal’s positioning as well as the reflected signal’s feature extraction. Field tests are conducted to confirm the effectiveness of the system and the retrieval algorithm described in this research. The entire system collects and analyzes signals at a coastal site in the field experiment, producing sea surface wind speed and significant wave height (SWH) that are compared to local weather station data, demonstrating the system’s practicality. The system can allow the centralized monitoring of many sites, as well as field experiments and real-time early warning at sea. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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19 pages, 2747 KiB  
Article
Challenges in Diurnal Humidity Analysis from Cellular Microwave Links (CML) over Germany
by Yoav Rubin, Dorita Rostkier-Edelstein, Christian Chwala and Pinhas Alpert
Remote Sens. 2022, 14(10), 2353; https://doi.org/10.3390/rs14102353 - 12 May 2022
Cited by 9 | Viewed by 2179
Abstract
Near-surface humidity is a crucial variable in many atmospheric processes, mostly related to the development of clouds and rain. The humidity at the height of a few tens of meters above ground level is highly influenced by surface characteristics. Measuring the near-surface humidity [...] Read more.
Near-surface humidity is a crucial variable in many atmospheric processes, mostly related to the development of clouds and rain. The humidity at the height of a few tens of meters above ground level is highly influenced by surface characteristics. Measuring the near-surface humidity at high resolution, where most of the humidity’s sinks and sources are found, is a challenging task using classical tools. A novel approach for measuring the humidity is based on commercial microwave links (CML), which provide a large part of the cellular networks backhaul. This study focuses on employing humidity measurements with high spatio–temporal resolution in Germany. One major goal is to assess the errors and the environmental influence by comparing the CML-derived humidity to in-situ humidity measurements at weather stations and reanalysis (COSMO-Rea6) products. The method of retrieving humidity from the CML has been improved as compared to previous studies due to the use of new data at high temporal resolution. The results show a similar correlation on average and generally good agreement between both the CML retrievals and the reanalysis, and 32 weather stations near Siegen, West Germany (CML—0.84, Rea6—0.85). Higher correlations are observed for CML-derived humidity during the daytime (0.85), especially between 9–17 LT (0.87) and a maximum at 12 LT (0.90). During the night, the correlations are lower on average (0.81), with a minimum at 3 LT (0.74). These results are discussed with attention to the diurnal boundary layer (BL) height variation which has a strong effect on the BL humidity temporal profile. Further metrics including root mean square errors, mean values and standard deviations, were also calculated. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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15 pages, 5637 KiB  
Article
Influence of Open-Pit Coal Mining on Ground Surface Deformation of Permafrost in the Muli Region in the Qinghai-Tibet Plateau, China
by Hongwei Wang, Yuan Qi, Juan Zhang, Jinlong Zhang, Rui Yang, Junyu Guo, Dongliang Luo, Jichun Wu and Shengming Zhou
Remote Sens. 2022, 14(10), 2352; https://doi.org/10.3390/rs14102352 - 12 May 2022
Cited by 8 | Viewed by 2877
Abstract
The Qinghai-Tibet Plateau (QTP) is the largest mid-to low latitude and high-altitude permafrost. Open-pit coal mining and other activities have caused serious damage to the alpine ecological environment and have accelerated the degradation of permafrost on the QTP. In this study, the influence [...] Read more.
The Qinghai-Tibet Plateau (QTP) is the largest mid-to low latitude and high-altitude permafrost. Open-pit coal mining and other activities have caused serious damage to the alpine ecological environment and have accelerated the degradation of permafrost on the QTP. In this study, the influence of open-pit coal mining on the time series ground surface deformation of the permafrost in the Muli region of the QTP was analyzed from 19 January 2018 to 22 December 2020 based on Landsat, Gaofen, and Sentinel remote sensing data. The primary methods include human-computer interactive visual interpretation and the small baseline subsets interferometric synthetic aperture radar (SBAS-InSAR) method. The results showed that the spatial distribution of displacement velocity exhibits a considerably different pattern in the Muli region. Alpine meadow is the main land use/land cover (LULC) in the Muli region, and the surface displacement was mainly subsidence. The surface subsidence trend in alpine marsh meadows was obvious, with a subsidence displacement velocity of 10–30 mm/a. Under the influence of changes in temperature, the permafrost surface displacement was characteristics of regular thaw subsidence and freeze uplift. Surface deformation of the mining area is relatively severe, with maximum uplift displacement velocity of 74.31 mm/a and maximum subsidence displacement velocity of 167.51 mm/a. Open-pit coal mining had resulted in the destruction of 48.73 km2 of natural landscape in the Muli region. Mining development in the Muli region had increased the soil moisture of the alpine marsh meadow around the mining area, resulting in considerable cumulative displacement near the mining area and the acceleration of permafrost degradation. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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18 pages, 44999 KiB  
Technical Note
Investigating the Magnetotelluric Responses in Electrical Anisotropic Media
by Tianya Luo, Xiangyun Hu, Longwei Chen and Guilin Xu
Remote Sens. 2022, 14(10), 2328; https://doi.org/10.3390/rs14102328 - 11 May 2022
Cited by 2 | Viewed by 2336
Abstract
When interpreting magnetotelluric (MT) data, because of the inherent anisotropy of the earth, considering electrical anisotropy is crucial. Accordingly, using the edge-based finite element method, we calculated the responses of MT data for electrical isotropic and anisotropic models, and subsequently used the anisotropy [...] Read more.
When interpreting magnetotelluric (MT) data, because of the inherent anisotropy of the earth, considering electrical anisotropy is crucial. Accordingly, using the edge-based finite element method, we calculated the responses of MT data for electrical isotropic and anisotropic models, and subsequently used the anisotropy index and polar plot to depict MT responses. High values of the anisotropy index were mainly yielded at the boundary domains of anomalous bodies for isotropy cases because the conductive differences among isotropic anomalous bodies or among anomalous bodies and background earth can be regarded as macro-anisotropy. However, they only appeared across anomalous bodies in the anisotropy cases. The anisotropy index can directly differentiate isotropy from anisotropy but exhibits difficulty in reflecting the azimuth of the principal conductivities. For the isotropy cases, polar plots are approximately circular and become curves with a big ratio of the major axis to minor axis, such as an 8-shaped curve for the anisotropic earth. Furthermore, the polar plot can reveal the directions of principal conductivities. However, distorted by anomalous bodies, polar plots with a large ratio of the major axis to minor axis occur in isotropic domains around the anomalous bodies, which may lead to the misinterpretation of these domains as anisotropic earth. Therefore, combining the anisotropy index with a polar plot facilitates the identification of the electrical anisotropy. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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20 pages, 16130 KiB  
Article
Application of Gaofen-6 Images in the Downscaling of Land Surface Temperatures
by Xiaoyuan Li, Xiufeng He and Xin Pan
Remote Sens. 2022, 14(10), 2307; https://doi.org/10.3390/rs14102307 - 10 May 2022
Cited by 7 | Viewed by 2129
Abstract
The coarse resolution of land surface temperatures (LSTs) retrieved from thermal-infrared (TIR) satellite images restricts their usage. One way to improve the resolution of such LSTs is downscaling using high-resolution remote sensing images. Herein, Gaofen-6 (GF-6) and Landsat-8 images were used to obtain [...] Read more.
The coarse resolution of land surface temperatures (LSTs) retrieved from thermal-infrared (TIR) satellite images restricts their usage. One way to improve the resolution of such LSTs is downscaling using high-resolution remote sensing images. Herein, Gaofen-6 (GF-6) and Landsat-8 images were used to obtain original and retrieved LSTs (Landsat-8- and GF-6-retrieved-LSTs) to perform LST downscaling in the Ebinur Lake Watershed. Downscaling model was constructed, and the regression kernel was explored. The results of downscaling LST using the GF-6 normalized difference vegetation index with red-edge band 2, ratio built-up index, normalized difference sand index, and normalized difference water index as multi-remote sensing indices with multiple remote sensing indices with random forest regression method provided optimal downscaling results, with R2 of 0.836, 0.918, and 0.941, root mean square difference of 1.04 K, 2.06 K, and 1.80 K, and the number of pixels with LST errors between −1 K and +1 K of 87.2%, 76.4%, and 81.9%, respectively. The expression of spatial distribution of 16 m-LST downscaling results corresponded with that of Landsat-8- and GF-6-retrieved-LST, and provided additional details spatial description of LST variations, which was absent in the Landsat-8- and GF-6-retrieved LSTs. The results of downscaling LST could satisfy the application requirements of LST spatial resolution. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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19 pages, 16862 KiB  
Article
OpenHSI: A Complete Open-Source Hyperspectral Imaging Solution for Everyone
by Yiwei Mao, Christopher H. Betters, Bradley Evans, Christopher P. Artlett, Sergio G. Leon-Saval, Samuel Garske, Iver H. Cairns, Terry Cocks, Robert Winter and Timothy Dell
Remote Sens. 2022, 14(9), 2244; https://doi.org/10.3390/rs14092244 - 7 May 2022
Cited by 14 | Viewed by 8355
Abstract
OpenHSI is an initiative to lower the barriers of entry and bring compact pushbroom hyperspectral imaging spectrometers to a wider audience. We present an open-source optical design that can be replicated with readily available commercial-off-the-shelf components, and an open-source software platform openhsi that [...] Read more.
OpenHSI is an initiative to lower the barriers of entry and bring compact pushbroom hyperspectral imaging spectrometers to a wider audience. We present an open-source optical design that can be replicated with readily available commercial-off-the-shelf components, and an open-source software platform openhsi that simplifies the process of capturing calibrated hyperspectral datacubes. Some of the features that the software stack provides include: an ISO 19115-2 metadata editor, wavelength calibration, a fast smile correction method, radiance conversion, atmospheric correction using 6SV (an open-source radiative transfer code), and empirical line calibration. A pipeline was developed to customise the desired processing and make openhsi practical for real-time use. We used the OpenHSI optical design and software stack successfully in the field and verified the performance using calibration tarpaulins. By providing all the tools needed to collect documented hyperspectral datasets, our work empowers practitioners who may not have the financial or technical capability to operate commercial hyperspectral imagers, and opens the door for applications in new problem domains. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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21 pages, 26006 KiB  
Article
A Parallel Method for Texture Reconstruction in Large-Scale 3D Automatic Modeling Based on Oblique Photography
by Fei Wang, Hongchun Zhu, Haolin Cai, Wenhu Qu, Shuaizhe Zhang and Zhendong Liu
Remote Sens. 2022, 14(9), 2160; https://doi.org/10.3390/rs14092160 - 30 Apr 2022
Cited by 3 | Viewed by 2450
Abstract
Common methods of texture reconstruction first build a visual list for each triangular face, and then select the best image for each triangular face based on the graph-cut method. These methods have problems such as high memory consumption, and difficulties in large-area texture [...] Read more.
Common methods of texture reconstruction first build a visual list for each triangular face, and then select the best image for each triangular face based on the graph-cut method. These methods have problems such as high memory consumption, and difficulties in large-area texture reconstruction. Hence, this paper proposes a parallel method for texture reconstruction in large-scale 3D automatic modeling. First, the hierarchical relationships between the texture reconstruction are calculated in accordance with the adjacency relationships between partitioning cells. Second, building contours are extracted based on the 3D mesh model, the tiles are divided into two categories (occlusion and non-occlusion), and the incorrect occlusion relationship is restored based on the occluded tiles. Then, the graph-cut algorithm is constructed to select the best-view label. Finally, the jagged labels between adjacent labels are smoothed to alleviate the problem of texture seams. Oblique photography data from an area of 10 km2 in Dongying, Shandong were used for validation. The experimental results reveal the following: (i) concerning reconstruction efficiency, the Waechter method can perform texture reconstruction only in a small area, whereas with the proposed method, the size of the reconstruction area is not restricted. The memory consumption is improved by factors of approximately 2–13. (ii) Concerning reconstruction results, the Waechter method incorrectly reconstructs the textures of partially occluded regions at the tile edges, while the proposed method can reconstruct the textures correctly. (iii) Compared to the Waechter method, the proposed approach has a 30% lower reduction in the number of texture fragments. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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19 pages, 1507 KiB  
Article
Online Sparse DOA Estimation Based on Sub–Aperture Recursive LASSO for TDM–MIMO Radar
by Jiawei Luo, Yongwei Zhang, Jianyu Yang, Donghui Zhang, Yongchao Zhang, Yin Zhang, Yulin Huang and Andreas Jakobsson
Remote Sens. 2022, 14(9), 2133; https://doi.org/10.3390/rs14092133 - 29 Apr 2022
Cited by 15 | Viewed by 2821
Abstract
The least absolute shrinkage and selection operator (LASSO) algorithm is a promising method for sparse source location in time–division multiplexing (TDM) multiple–input, multiple–output (MIMO) radar systems, with notable performance gains in regard to resolution enhancement and side lobe suppression. However, the current batch [...] Read more.
The least absolute shrinkage and selection operator (LASSO) algorithm is a promising method for sparse source location in time–division multiplexing (TDM) multiple–input, multiple–output (MIMO) radar systems, with notable performance gains in regard to resolution enhancement and side lobe suppression. However, the current batch LASSO algorithm suffers from high–computational complexity when dealing with massive TDM–MIMO observations, due to high–dimensional matrix operations and the large number of iterations. In this paper, an online LASSO method is proposed for efficient direction–of–arrival (DOA) estimation of the TDM–MIMO radar based on the receiving features of the sub–aperture data blocks. This method recursively refines the location parameters for each receive (RX) block observation that becomes available sequentially in time. Compared with the conventional batch LASSO method, the proposed online DOA method makes full use of the TDM–MIMO reception time to improve the real–time performance. Additionally, it allows for much less iterations, avoiding high–dimensional matrix operations, allowing the computational complexity to be reduced from OK3 to OK2. Simulated and real–data results demonstrate the superiority and effectiveness of the proposed method. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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16 pages, 5353 KiB  
Article
QUantitative and Automatic Atmospheric Correction (QUAAC): Application and Validation
by Shumin Liu, Yunli Zhang, Limin Zhao, Xingfeng Chen, Ruoxuan Zhou, Fengjie Zheng, Zhiliang Li, Jiaguo Li, Hang Yang, Huafu Li, Jian Yang, Hailiang Gao and Xingfa Gu
Sensors 2022, 22(9), 3280; https://doi.org/10.3390/s22093280 - 25 Apr 2022
Cited by 8 | Viewed by 2783
Abstract
The difficulty of atmospheric correction based on a radiative transfer model lies in the acquisition of synchronized atmospheric parameters, especially the aerosol optical depth (AOD). At the moment, there is no fully automatic and high-efficiency atmospheric correction method to make full use of [...] Read more.
The difficulty of atmospheric correction based on a radiative transfer model lies in the acquisition of synchronized atmospheric parameters, especially the aerosol optical depth (AOD). At the moment, there is no fully automatic and high-efficiency atmospheric correction method to make full use of the advantages of geostationary meteorological satellites in large-scale and efficient atmospheric monitoring. Therefore, a QUantitative and Automatic Atmospheric Correction (QUAAC) method is proposed which can efficiently correct high-spatial-resolution (HSR) satellite images. QUAAC uses the atmospheric aerosol products of geostationary satellites to match the synchronized AOD according to the temporal and spatial information of HSR satellite images. This method solves the problem that the AOD is difficult to obtain or the accuracy is not high enough to meet the demand of atmospheric correction. By using the obtained atmospheric parameters, atmospheric correction is performed to obtain the surface reflectance (SR). The whole process can achieve fully automatic operation without manual intervention. After QUAAC applied to Gaofen-2 (GF-2) HSR satellite and Himawari-8 (H-8) geostationary satellite, the results show that the effect of QUAAC correction is slightly better than that of the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) correction, and the QUAAC−corrected surface spectral curves have good coherence to that of the synchronously measured by field experiments. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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17 pages, 3907 KiB  
Article
Optimization of Numerical Methods for Transforming UTM Plane Coordinates to Lambert Plane Coordinates
by Kuangxu Wang, Sijing Ye, Peichao Gao, Xiaochuang Yao and Zuliang Zhao
Remote Sens. 2022, 14(9), 2056; https://doi.org/10.3390/rs14092056 - 25 Apr 2022
Cited by 8 | Viewed by 3104
Abstract
The rapid transformation from UTM (Universal Transverse Mecator) projection to Lambert projection helps to realize timely merging, inversion, and analysis of high-frequency partitioned remote sensing images. In this study, the transformation error and the efficiency of the linear rule approximation method, the improved [...] Read more.
The rapid transformation from UTM (Universal Transverse Mecator) projection to Lambert projection helps to realize timely merging, inversion, and analysis of high-frequency partitioned remote sensing images. In this study, the transformation error and the efficiency of the linear rule approximation method, the improved linear rule approximation method, the hyperbolic transformation method, and the conformal transformation method were compared in transforming the coordinates of sample points on WGS84 (The World Geodetic System 1984)-UTM zonal projections to WGS84-Lambert projection coordinates. The effect of the grid aspect ratio on the coordinate transformation error of the conformal transformation method was examined. In addition, the conformal transformation method-based error spatial pattern of the sample points was analyzed. The results show that the conformal transformation method can better balance error and efficiency than other numerical methods. The error of the conformal transformation method is less affected by grid size. The maximum x-error is less than 0.36 m and the maximum y-error is less than 1.22 m when the grid size reaches 300 km × 300 km. The x- and y-error values decrease when square grids are used; namely, setting the grid aspect ratio close to 1 helps to weaken the effect of increasing grid area on the error. The dispersion of the error distribution and the maximum error of sample points both decrease relative to their minimum distance to the grid edge and stabilize at a minimum distance equal to 70 km. This study can support the rapid integration of massive remote sensing data over large areas. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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19 pages, 61170 KiB  
Article
Combining Spectral, Spatial-Contextual, and Structural Information in Multispectral UAV Data for Spruce Crown Delineation
by Aravind Harikumar, Petra D’Odorico and Ingo Ensminger
Remote Sens. 2022, 14(9), 2044; https://doi.org/10.3390/rs14092044 - 24 Apr 2022
Cited by 3 | Viewed by 3352
Abstract
Precise delineation of individual tree crowns is critical for accurate forest biophysical parameter estimation, species classification, and ecosystem modelling. Multispectral optical remote sensors mounted on low-flying unmanned aerial vehicles (UAVs) can rapidly collect very-high-resolution (VHR) photogrammetric optical data that contain the spectral, spatial, [...] Read more.
Precise delineation of individual tree crowns is critical for accurate forest biophysical parameter estimation, species classification, and ecosystem modelling. Multispectral optical remote sensors mounted on low-flying unmanned aerial vehicles (UAVs) can rapidly collect very-high-resolution (VHR) photogrammetric optical data that contain the spectral, spatial, and structural information of trees. State-of-the-art tree crown delineation approaches rely mostly on spectral information and underexploit the spatial-contextual and structural information in VHR photogrammetric multispectral data, resulting in crown delineation errors. Here, we propose the spectral, spatial-contextual, and structural information-based individual tree crown delineation (S3-ITD) method, which accurately delineates individual spruce crowns by minimizing the undesirable effects due to intracrown spectral variance and nonuniform illumination/shadowing in VHR multispectral data. We evaluate the performance of the S3-ITD crown delineation method over a white spruce forest in Quebec, Canada. The highest mean intersection over union (IoU) index of 0.83, and the lowest mean crown-area difference (CAD) of 0.14 m2, proves the superior crown delineation performance of the S3-ITD method over state-of-the-art methods. The reduction in error by 2.4 cm and 1.0 cm for the allometrically derived diameter at breast height (DBH) estimates compared with those from the WS-ITD and the BF-ITD approaches, respectively, demonstrates the effectiveness of the S3-ITD method to accurately estimate biophysical parameters. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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26 pages, 20005 KiB  
Article
Magnetometric Surveys for the Non-Invasive Surface and Subsurface Interpretation of Volcanic Structures in Planetary Exploration, a Case Study of Several Volcanoes in the Iberian Peninsula
by Marina Díaz Michelena, Rolf Kilian, Miguel Ángel Rivero, Sergio Fernández Romero, Francisco Ríos, José Luis Mesa and Andrés Oyarzún
Remote Sens. 2022, 14(9), 2039; https://doi.org/10.3390/rs14092039 - 24 Apr 2022
Cited by 3 | Viewed by 2710
Abstract
Volcanoes are typical features of the solar system that offer a window into the interior of planets. Thus, their study can improve the understanding of the interiors and evolution of planets. On Earth, volcanoes are monitored by multiple sensors during their dormant and [...] Read more.
Volcanoes are typical features of the solar system that offer a window into the interior of planets. Thus, their study can improve the understanding of the interiors and evolution of planets. On Earth, volcanoes are monitored by multiple sensors during their dormant and active phases. Presently, this is not feasible for other planets’ volcanoes. However, robotic vehicles and the recent technological demonstration of Ingenuity on Mars open up the possibility of using the powerful and non-destructive geophysical tool of magnetic surveys at different heights, for the investigation of surfaces and subsurfaces. We propose a methodology with a view to extract information from planetary volcanoes in the short and medium term, which comprises an analysis of the morphology using images, magnetic field surveys at different heights, in situ measurements of magnetic susceptibility, and simplified models for the interpretation of geological structures. This methodology is applied successfully to the study of different examples of the main volcanic zones of the Iberian Peninsula, representative of the Martian intraplate volcanism and similar to Venus domes, as a preparatory action prior to the exploration of the rocky planets’ surfaces. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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21 pages, 5693 KiB  
Article
Field Campaign Evaluation of Sensors Lufft GMX500 and MaxiMet WS100 in Peruvian Central Andes
by Jairo M. Valdivia, David A. Guizado, José L. Flores-Rojas, Delia P. Gamarra, Yamina F. Silva-Vidal and Edith R. Huamán
Sensors 2022, 22(9), 3219; https://doi.org/10.3390/s22093219 - 22 Apr 2022
Cited by 3 | Viewed by 2669
Abstract
The research presents the inter-comparison of atmospheric variables measured by 9 automatic weather stations. This set of data was compared with the measurements of other weather stations in order to standardize the values that must be adjusted when taken to different areas. The [...] Read more.
The research presents the inter-comparison of atmospheric variables measured by 9 automatic weather stations. This set of data was compared with the measurements of other weather stations in order to standardize the values that must be adjusted when taken to different areas. The data of a set of a total of 9 GMX500, which measures conventional meteorological variables, and 10 WS100 sensors, which measures precipitation parameters. The automatic stations were set up at the Huancayo Observatory (Geophysical Institute of Peru) for a period of 5 months. The data set of GMX500 were evaluated comparing with the average of the 9 sensors and the WS100 was compared with an optical disdrometer Parsivel2. The temperature, pressure, relative humidity, wind speed, rainfall rate, and drop size distribution were evaluated. A pair of GMX500 sensors presented high data dispersion; it was found found that the errors came from a bad configuration; once this problem was solved, good agreement was archived, with low RMSE and high correlation. It was found that the WS100 sensors overestimate the precipitation with a percentage bias close to 100% and the differences increase with the greater intensity of rain. The drop size distribution retrieved by WS100 have unrealistic behavior with higher concentrations in diameters of 1 mm and 5 mm, in addition to a flattened curve. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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24 pages, 5133 KiB  
Article
A Real-Time Effectiveness Evaluation Method for Remote Sensing Satellite Clusters on Moving Targets
by Zhi Li, Yunfeng Dong, Peiyun Li, Hongjue Li and Yingjia Liew
Sensors 2022, 22(8), 2993; https://doi.org/10.3390/s22082993 - 13 Apr 2022
Cited by 6 | Viewed by 2260
Abstract
Recently, remote sensing satellites have become increasingly important in the Earth observation field as their temporal, spatial, and spectral resolutions have improved. Subsequently, the quantitative evaluation of remote sensing satellites has received considerable attention. The quantitative evaluation method is conventionally based on simulation, [...] Read more.
Recently, remote sensing satellites have become increasingly important in the Earth observation field as their temporal, spatial, and spectral resolutions have improved. Subsequently, the quantitative evaluation of remote sensing satellites has received considerable attention. The quantitative evaluation method is conventionally based on simulation, but it has a speed-accuracy trade-off. In this paper, a real-time evaluation model architecture for remote sensing satellite clusters is proposed. Firstly, a multi-physical field coupling simulation model of the satellite cluster to observe moving targets is established. Aside from considering the repercussions of on-board resource constraints, it also considers the consequences of the imaging’s uncertainty effects on observation results. Secondly, a moving target observation indicator system is developed, which reflects the satellite cluster’s actual effectiveness in orbit. Meanwhile, an indicator screening method using correlation analysis is proposed to improve the independence of the indicator system. Thirdly, a neural network is designed and trained for stakeholders to realize a rapid evaluation. Different network structures and parameters are comprehensively studied to determine the optimized neural network model. Finally, based on the experiments carried out, the proposed neural network evaluation model can generate real-time, high-quality evaluation results. Hence, the validity of our proposed approach is substantiated. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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19 pages, 8607 KiB  
Article
SAR Target Recognition Using cGAN-Based SAR-to-Optical Image Translation
by Yuchuang Sun, Wen Jiang, Jiyao Yang and Wangzhe Li
Remote Sens. 2022, 14(8), 1793; https://doi.org/10.3390/rs14081793 - 8 Apr 2022
Cited by 29 | Viewed by 5077
Abstract
Target recognition in synthetic aperture radar (SAR) imagery suffers from speckle noise and geometric distortion brought by the range-based coherent imaging mechanism. A new SAR target recognition system is proposed, using a SAR-to-optical translation network as pre-processing to enhance both automatic and manual [...] Read more.
Target recognition in synthetic aperture radar (SAR) imagery suffers from speckle noise and geometric distortion brought by the range-based coherent imaging mechanism. A new SAR target recognition system is proposed, using a SAR-to-optical translation network as pre-processing to enhance both automatic and manual target recognition. In the system, SAR images of targets are translated into optical by a modified conditional generative adversarial network (cGAN) whose generator with a symmetric architecture and inhomogeneous convolution kernels is designed to reduce the background clutter and edge blur of the output. After the translation, a typical convolutional neural network (CNN) classifier is exploited to recognize the target types in translated optical images automatically. For training and testing the system, a new multi-view SAR-optical dataset of aircraft targets is created. Evaluations of the translation results based on human vision and image quality assessment (IQA) methods verify the improvement of image interpretability and quality, and translated images obtain higher average accuracy than original SAR data in manual and CNN classification experiments. The good expansibility and robustness of the system shown in extending experiments indicate the promising potential for practical applications of SAR target recognition. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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10 pages, 2005 KiB  
Technical Note
Resolution Enhancement of SMAP Passive Soil Moisture Estimates
by Jordan P. Brown and David G. Long
Remote Sens. 2022, 14(7), 1761; https://doi.org/10.3390/rs14071761 - 6 Apr 2022
Cited by 4 | Viewed by 2328
Abstract
The Soil Moisture Active Passive (SMAP) mission includes a unique combination of instruments intended to provide daily global soil moisture data with high accuracy and resolution. Due to radar instrument failure, the default resolution of the data product decreased from the intended 9 [...] Read more.
The Soil Moisture Active Passive (SMAP) mission includes a unique combination of instruments intended to provide daily global soil moisture data with high accuracy and resolution. Due to radar instrument failure, the default resolution of the data product decreased from the intended 9 km to 36 km shortly after the mission started to return data. To improve this, we employed the Scatterometer Image Reconstruction algorithm in its radiometer form (rSIR) to enhance the resolution of the radiometer brightness temperature measurements from which the soil moisture was derived. This paper compares the soil moisture estimates created from the rSIR-enhanced brightness temperatures with SMAP project radiometer L2_SM_SP and SMAP-Sentinel L2_SM_P products reported on 9 km and 3 km grids, respectively. We find that the difference of the rSIR-enhanced passive soil moisture product is generally within 0.020 cm3 cm−3 RMS of the 9 km SMAP radiometer L2_SM_SP and 0.045 cm3 cm−3 RMS of the 3 km SMAP-Sentinel L2_SM_P soil moisture products. The accuracy of the rSIR soil moisture can be improved by including better antenna pattern correction methods applied to the input TB measurements. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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17 pages, 2751 KiB  
Article
Analysis of Land Use Change and Driving Mechanisms in Vietnam during the Period 2000–2020
by Xuan Guo, Junzhi Ye and Yunfeng Hu
Remote Sens. 2022, 14(7), 1600; https://doi.org/10.3390/rs14071600 - 26 Mar 2022
Cited by 23 | Viewed by 4333
Abstract
High-accuracy, long-time-series and large-scale land classification mapping are essential for assessing the evolutionary patterns of land systems and developing sustainability studies. In this paper, using Google Earth Engine (GEE) and Landsat satellite remote sensing images, based on the Random Forest (RF) algorithm, we [...] Read more.
High-accuracy, long-time-series and large-scale land classification mapping are essential for assessing the evolutionary patterns of land systems and developing sustainability studies. In this paper, using Google Earth Engine (GEE) and Landsat satellite remote sensing images, based on the Random Forest (RF) algorithm, we carried out remote sensing classification to obtain a year-by-year land use/cover data set in Vietnam over the past 21 years (2000–2020). Further applying principal component analysis and multiple linear regression methods, we examined the spatio-temporal characteristics, dynamic changes and driving mechanisms of land use change. The results show the following: (1) The RF classification algorithm supported by the GEE can quickly and accurately obtain a land use/cover data set. The overall classification accuracy is 0.91 ± 0.01. (2) The land cover types in Vietnam are dominated by woodland and cropland, with an area share of 54.62% and 37.90%, respectively. In the past 20 years, the area of built-up land has increased the most (+93.49%), followed by the area of water bodies (+54.19%), while the area of woodland has remained almost unchanged. (3) The expansion of built-up land is driven by regional economic development; the area changes in cropland, water bodies and woodland are influenced by both national economic development and climate change. The results of the study provide a basis for assessing land use policies in Vietnam and a reference methodological framework for rapid land mapping and analysis in other countries in the China–Indochina Peninsula. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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23 pages, 5645 KiB  
Article
NDSRGAN: A Novel Dense Generative Adversarial Network for Real Aerial Imagery Super-Resolution Reconstruction
by Mingqiang Guo, Zeyuan Zhang, Heng Liu and Ying Huang
Remote Sens. 2022, 14(7), 1574; https://doi.org/10.3390/rs14071574 - 24 Mar 2022
Cited by 26 | Viewed by 3654
Abstract
In recent years, more and more researchers have used deep learning methods for super-resolution reconstruction and have made good progress. However, most of the existing super-resolution reconstruction models generate low-resolution images for training by downsampling high-resolution images through bicubic interpolation, and the models [...] Read more.
In recent years, more and more researchers have used deep learning methods for super-resolution reconstruction and have made good progress. However, most of the existing super-resolution reconstruction models generate low-resolution images for training by downsampling high-resolution images through bicubic interpolation, and the models trained from these data have poor reconstruction results on real-world low-resolution images. In the field of unmanned aerial vehicle (UAV) aerial photography, the use of existing super-resolution reconstruction models in reconstructing real-world low-resolution aerial images captured by UAVs is prone to producing some artifacts, texture detail distortion and other problems, due to compression and fusion processing of the aerial images, thereby resulting in serious loss of texture detail in the obtained low-resolution aerial images. To address this problem, this paper proposes a novel dense generative adversarial network for real aerial imagery super-resolution reconstruction (NDSRGAN), and we produce image datasets with paired high- and low-resolution real aerial remote sensing images. In the generative network, we use a multilevel dense network to connect the dense connections in a residual dense block. In the discriminative network, we use a matrix mean discriminator that can discriminate the generated images locally, no longer discriminating the whole input image using a single value but instead in chunks of regions. We also use smoothL1 loss instead of the L1 loss used in most existing super-resolution models, to accelerate the model convergence and reach the global optimum faster. Compared with traditional models, our model can better utilise the feature information in the original image and discriminate the image in patches. A series of experiments is conducted with real aerial imagery datasets, and the results show that our model achieves good performance on quantitative metrics and visual perception. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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28 pages, 70523 KiB  
Article
Robust Clutter Suppression and Radial Velocity Estimation for High-Resolution Wide-Swath SAR-GMTI
by Zhenning Zhang, Weidong Yu, Mingjie Zheng, Liangbo Zhao and Zi-Xuan Zhou
Remote Sens. 2022, 14(7), 1555; https://doi.org/10.3390/rs14071555 - 23 Mar 2022
Cited by 1 | Viewed by 2279
Abstract
Moving targets are usually smeared and imaged at incorrect positions in synthetic aperture radar (SAR) images due to the target motions during the illumination time. Moreover, a moving target will cause multiple artifacts in the reconstructed image since pulse repetition frequency (PRF) operated [...] Read more.
Moving targets are usually smeared and imaged at incorrect positions in synthetic aperture radar (SAR) images due to the target motions during the illumination time. Moreover, a moving target will cause multiple artifacts in the reconstructed image since pulse repetition frequency (PRF) operated in high-resolution wide-swath (HRWS) SAR is very low. In order to reliably indicate moving targets, a robust cancellation algorithm is derived in this paper for clutter suppression in multichannel HRWS SAR, which is free by velocity searching and covariance matrix estimation of clutter plus noise. The proposed multilayer channel-cancellation combined with the deramp processing is designed to sequentially suppress the seriously aliased clutter in HRWS SAR. Experimental results show that the proposed algorithm is efficient and robust in tough situations, and have a superior detection ability in weak targets and low-velocity targets. In addition, the radial velocity estimation algorithm combined with the channel cancellation is exploited to relocate moving targets. The effectiveness of the proposed algorithms is validated by actual spaceborne SAR data acquired by a coordination experiment with two controllable vehicles. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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19 pages, 2891 KiB  
Article
Cycle and Self-Supervised Consistency Training for Adapting Semantic Segmentation of Aerial Images
by Han Gao, Yang Zhao, Peng Guo, Zihao Sun, Xiuwan Chen and Yunwei Tang
Remote Sens. 2022, 14(7), 1527; https://doi.org/10.3390/rs14071527 - 22 Mar 2022
Cited by 12 | Viewed by 3167
Abstract
Semantic segmentation is a critical problem for many remote sensing (RS) image applications. Benefiting from large-scale pixel-level labeled data and the continuous evolution of deep neural network architectures, the performance of semantic segmentation approaches has been constantly improved. However, deploying a well-trained model [...] Read more.
Semantic segmentation is a critical problem for many remote sensing (RS) image applications. Benefiting from large-scale pixel-level labeled data and the continuous evolution of deep neural network architectures, the performance of semantic segmentation approaches has been constantly improved. However, deploying a well-trained model on unseen and diverse testing environments remains a major challenge: a large gap between data distributions in train and test domains results in severe performance loss, while manual dense labeling is costly and not scalable. To this end, we proposed an unsupervised domain adaptation framework for RS image semantic segmentation that is both practical and effective. The framework is supported by the consistency principle, including the cycle consistency in the input space and self-supervised consistency in the training stage. Specifically, we introduce cycle-consistent generative adversarial networks to reduce the discrepancy between source and target distributions by translating one into the other. The translated source data then drive a pipeline of supervised semantic segmentation model training. We enforce consistency of model predictions across target image transformations in order to provide self-supervision for the unlabeled target data. Experiments and extensive ablation studies demonstrate the effectiveness of the proposed approach on two challenging benchmarks, on which we achieve up to 9.95% and 7.53% improvements in accuracy over the state-of-the-art methods, respectively. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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18 pages, 14990 KiB  
Article
Automated Delineation of Microstands in Hemiboreal Mixed Forests Using Stereo GeoEye-1 Data
by Linda Gulbe, Juris Zarins and Ints Mednieks
Remote Sens. 2022, 14(6), 1471; https://doi.org/10.3390/rs14061471 - 18 Mar 2022
Cited by 2 | Viewed by 1967
Abstract
A microstand is a small forest area with a homogeneous tree species, height, and density composition. High-spatial-resolution GeoEye-1 multispectral (MS) images and GeoEye-1-based canopy height models (CHMs) allow delineating microstands automatically. This paper studied the potential benefits of two microstand segmentation workflows: (1) [...] Read more.
A microstand is a small forest area with a homogeneous tree species, height, and density composition. High-spatial-resolution GeoEye-1 multispectral (MS) images and GeoEye-1-based canopy height models (CHMs) allow delineating microstands automatically. This paper studied the potential benefits of two microstand segmentation workflows: (1) our modification of JSEG and (2) generic region merging (GRM) of the Orfeo Toolbox, both intended for the microstand border refinement and automated stand volume estimation in hemiboreal forests. Our modification of JSEG uses a CHM as the primary data source for segmentation by refining the results using MS data. Meanwhile, the CHM and multispectral data fusion were achieved as multiband segmentation for the GRM workflow. The accuracy was evaluated using several sets of metrics (unsupervised, supervised direct assessment, and system-level assessment). Metrics were calculated for a regular segment grid to check the benefits compared with the simple image patches. The metrics showed very similar results for both workflows. The most successful combinations in the workflow parameters retrieved over 75 % of the boundaries selected by a human interpreter. However, the impact of data fusion and parameter combinations on stand volume estimation accuracy was minimal, causing variations of the RMSE within approximately 7 m3/ha. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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24 pages, 4083 KiB  
Article
Assessing Obukhov Length and Friction Velocity from Floating Lidar Observations: A Data Screening and Sensitivity Computation Approach
by Marcos Paulo Araújo da Silva, Francesc Rocadenbosch, Joan Farré-Guarné, Andreu Salcedo-Bosch, Daniel González-Marco and Alfredo Peña
Remote Sens. 2022, 14(6), 1394; https://doi.org/10.3390/rs14061394 - 14 Mar 2022
Cited by 11 | Viewed by 3885
Abstract
This work presents a parametric-solver algorithm for estimating atmospheric stability and friction velocity from floating Doppler wind lidar (FDWL) observations close to the mast of IJmuiden in the North Sea. The focus of the study was two-fold: (i) to examine the sensitivity of [...] Read more.
This work presents a parametric-solver algorithm for estimating atmospheric stability and friction velocity from floating Doppler wind lidar (FDWL) observations close to the mast of IJmuiden in the North Sea. The focus of the study was two-fold: (i) to examine the sensitivity of the computational algorithm to the retrieved variables and derived stability classes (the latter through confusion-matrix theory), and (ii) to present data screening procedures for FDWLs and fixed reference instrumentation. The performance of the stability estimation algorithm was assessed with reference to wind speed and temperature observations from the mast. A fixed-to-mast Doppler wind lidar (DWL) was also available, which provides a reference for wind-speed observations free from sea-motion perturbations. When comparing FDWL- and mast-derived mean wind speeds, the obtained determination coefficient was as high as that of the fixed-to-mast DWL against the mast (?2=0.996) with a root mean square error (RMSE) of 0.25 m/s. From the 82-day measurement campaign at IJmuiden (10,833 10 min records), the parametric algorithm showed that the atmosphere was neutral (31% of the cases), stable (28%), or near-neutral stable (19%) during most of the campaign. These figures satisfactorily agree with values estimated from the mast measurements (31%, 27%, and 19%, respectively). Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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12 pages, 7331 KiB  
Article
The Campo de Dalias GNSS Network Unveils the Interaction between Roll-Back and Indentation Tectonics in the Gibraltar Arc
by Jesús Galindo-Zaldivar, Antonio J. Gil, Víctor Tendero-Salmerón, María J. Borque, Gemma Ercilla, Lourdes González-Castillo, Alberto Sánchez-Alzola, María C. Lacy, Ferran Estrada, Manuel Avilés, Pedro Alfaro, Asier Madarieta-Txurruka and Fernando Chacón
Sensors 2022, 22(6), 2128; https://doi.org/10.3390/s22062128 - 9 Mar 2022
Cited by 9 | Viewed by 3268
Abstract
The Gibraltar Arc includes the Betic and Rif Cordilleras surrounding the Alboran Sea; it is formed at the northwest–southeast Eurasia–Nubia convergent plate boundary in the westernmost Mediterranean. Since 2006, the Campo de Dalias GNSS network has monitored active tectonic deformation of the most [...] Read more.
The Gibraltar Arc includes the Betic and Rif Cordilleras surrounding the Alboran Sea; it is formed at the northwest–southeast Eurasia–Nubia convergent plate boundary in the westernmost Mediterranean. Since 2006, the Campo de Dalias GNSS network has monitored active tectonic deformation of the most seismically active area on the north coast of the Alboran Sea. Our results show that the residual deformation rates with respect to Eurasia range from 1.7 to 3.0 mm/year; roughly homogenous west-southwestward displacements of the northern sites occur, while the southern sites evidence irregular displacements towards the west and northwest. This deformation pattern supports simultaneous east-northeast–west-southwest extension, accommodated by normal and oblique faults, and north-northwest–south-southeast shortening that develops east-northeast–west-southwest folds. Moreover, the GNSS results point to dextral creep of the main northwest–southeast Balanegra Fault. These GNNS results thus reveal, for the first time, present-day interaction of the roll-back tectonics of the Rif–Gibraltar–Betic slab in the western part of the Gibraltar Arc with the indentation tectonics affecting the eastern and southern areas, providing new insights for improving tectonic models of arcuate orogens. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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18 pages, 5296 KiB  
Article
Efficient Depth Fusion Transformer for Aerial Image Semantic Segmentation
by Li Yan, Jianming Huang, Hong Xie, Pengcheng Wei and Zhao Gao
Remote Sens. 2022, 14(5), 1294; https://doi.org/10.3390/rs14051294 - 7 Mar 2022
Cited by 22 | Viewed by 7428
Abstract
Taking depth into consideration has been proven to improve the performance of semantic segmentation through providing additional geometry information. Most existing works adopt a two-stream network, extracting features from color images and depth images separately using two branches of the same structure, which [...] Read more.
Taking depth into consideration has been proven to improve the performance of semantic segmentation through providing additional geometry information. Most existing works adopt a two-stream network, extracting features from color images and depth images separately using two branches of the same structure, which suffer from high memory and computation costs. We find that depth features acquired by simple downsampling can also play a complementary part in the semantic segmentation task, sometimes even better than the two-stream scheme with the same two branches. In this paper, a novel and efficient depth fusion transformer network for aerial image segmentation is proposed. The presented network utilizes patch merging to downsample depth input and a depth-aware self-attention (DSA) module is designed to mitigate the gap caused by difference between two branches and two modalities. Concretely, the DSA fuses depth features and color features by computing depth similarity and impact on self-attention map calculated by color feature. Extensive experiments on the ISPRS 2D semantic segmentation dataset validate the efficiency and effectiveness of our method. With nearly half the parameters of traditional two-stream scheme, our method acquires 83.82% mIoU on Vaihingen dataset outperforming other state-of-the-art methods and 87.43% mIoU on Potsdam dataset comparable to the state-of-the-art. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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17 pages, 10647 KiB  
Article
LSTNet: A Reference-Based Learning Spectral Transformer Network for Spectral Super-Resolution
by Debao Yuan, Ling Wu, Huinan Jiang, Bingrui Zhang and Jian Li
Sensors 2022, 22(5), 1978; https://doi.org/10.3390/s22051978 - 3 Mar 2022
Cited by 4 | Viewed by 2906
Abstract
Hyperspectral images (HSIs) are data cubes containing rich spectral information, making them beneficial to many Earth observation missions. However, due to the limitations of the associated imaging systems and their sensors, such as the swath width and revisit period, hyperspectral imagery over a [...] Read more.
Hyperspectral images (HSIs) are data cubes containing rich spectral information, making them beneficial to many Earth observation missions. However, due to the limitations of the associated imaging systems and their sensors, such as the swath width and revisit period, hyperspectral imagery over a large coverage area cannot be acquired in a short amount of time. Spectral super-resolution (SSR) is a method that involves learning the relationship between a multispectral image (MSI) and an HSI, based on the overlap region, followed by reconstruction of the HSI by making full use of the large swath width of the MSI, thereby improving its coverage. Much research has been conducted recently to address this issue, but most existing methods mainly learn the prior spectral information from training data, lacking constraints on the resulting spectral fidelity. To address this problem, a novel learning spectral transformer network (LSTNet) is proposed in this paper, utilizing a reference-based learning strategy to transfer the spectral structure knowledge of a reference HSI to create a reasonable reconstruction spectrum. More specifically, a spectral transformer module (STM) and a spectral reconstruction module (SRM) are designed, in order to exploit the prior and reference spectral information. Experimental results demonstrate that the proposed method has the ability to produce high-fidelity reconstructed spectra. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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23 pages, 8015 KiB  
Article
Remote Sensing Image Denoising Based on Deep and Shallow Feature Fusion and Attention Mechanism
by Lintao Han, Yuchen Zhao, Hengyi Lv, Yisa Zhang, Hailong Liu and Guoling Bi
Remote Sens. 2022, 14(5), 1243; https://doi.org/10.3390/rs14051243 - 3 Mar 2022
Cited by 35 | Viewed by 5758
Abstract
Optical remote sensing images are widely used in the fields of feature recognition, scene semantic segmentation, and others. However, the quality of remote sensing images is degraded due to the influence of various noises, which seriously affects the practical use of remote sensing [...] Read more.
Optical remote sensing images are widely used in the fields of feature recognition, scene semantic segmentation, and others. However, the quality of remote sensing images is degraded due to the influence of various noises, which seriously affects the practical use of remote sensing images. As remote sensing images have more complex texture features than ordinary images, this will lead to the previous denoising algorithm failing to achieve the desired result. Therefore, we propose a novel remote sensing image denoising network (RSIDNet) based on a deep learning approach, which mainly consists of a multi-scale feature extraction module (MFE), multiple local skip-connected enhanced attention blocks (ECA), a global feature fusion block (GFF), and a noisy image reconstruction block (NR). The combination of these modules greatly improves the model’s use of the extracted features and increases the model’s denoising capability. Extensive experiments on synthetic Gaussian noise datasets and real noise datasets have shown that RSIDNet achieves satisfactory results. RSIDNet can improve the loss of detail information in denoised images in traditional denoising methods, retaining more of the higher-frequency components, which can have performance improvements for subsequent image processing. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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21 pages, 4913 KiB  
Article
Analytical Models for Multipath and Switch Leakage for the SWOT Interferometer
by Razi Ahmed, Daniel Esteban-Fernández and Scott Hensley
Sensors 2022, 22(5), 1931; https://doi.org/10.3390/s22051931 - 1 Mar 2022
Cited by 2 | Viewed by 2127
Abstract
The Ka-Band Radar Interferometer (KaRIn) instrument on the Surface Water and Ocean Topography (SWOT) mission is a single-pass synthetic aperture radar (SAR) interferometer tasked with, among others, measuring ocean topography to within a few centimeters over kilometer scale resolutions. A SAR interferometer relies [...] Read more.
The Ka-Band Radar Interferometer (KaRIn) instrument on the Surface Water and Ocean Topography (SWOT) mission is a single-pass synthetic aperture radar (SAR) interferometer tasked with, among others, measuring ocean topography to within a few centimeters over kilometer scale resolutions. A SAR interferometer relies on very precise phase difference measurements between two spatially distant antennas to estimate topography. Multipath phase caused by unintended scattering off the spacecraft structure is a known error source for radar interferometers and takes up a significant portion of the KaRIn error budget. This paper outlines some analytical multipath models that were used for instrument design, performance analysis and mitigation of the multipath signal. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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24 pages, 19443 KiB  
Article
Precipitation and Soil Moisture Spatio-Temporal Variability and Extremes over Vietnam (1981–2019): Understanding Their Links to Rice Yield
by Luyen K. Bui, Joseph Awange and Dinh Toan Vu
Sensors 2022, 22(5), 1906; https://doi.org/10.3390/s22051906 - 1 Mar 2022
Cited by 3 | Viewed by 2900
Abstract
Vietnam, one of the three leading rice producers globally, has recently seen an increased threat to its rice production emanating from climate extremes (floods and droughts). Understanding spatio-temporal variability in precipitation and soil moisture is essential for policy formulations to adapt and cope [...] Read more.
Vietnam, one of the three leading rice producers globally, has recently seen an increased threat to its rice production emanating from climate extremes (floods and droughts). Understanding spatio-temporal variability in precipitation and soil moisture is essential for policy formulations to adapt and cope with the impacts of climate extremes on rice production in Vietnam. Adopting a higher-order statistical method of independent component analysis (ICA), this study explores the spatio-temporal variability in the Climate Hazards Group InfraRed Precipitation Station’s (CHIRPS) precipitation and the Global Land Data Assimilation System’s (GLDAS) soil moisture products. The results indicate an agreement between monthly CHIRPS precipitation and monthly GLDAS soil moisture with the wetter period over the southern and South Central Coast areas that is latter than that over the northern and North Central Coast areas. However, the spatial patterns of annual mean precipitation and soil moisture disagree, likely due to factors other than precipitation affecting the amount of moisture in the soil layers, e.g., temperature, irrigation, and drainage systems, which are inconsistent between areas. The CHIRPS Standardized Precipitation Index (SPI) is useful in capturing climate extremes, and the GLDAS Standardized Soil Moisture Index (SSI) is useful in identifying the influences of climate extremes on rice production in Vietnam. During the 2016–2018 period, there existed a reduction in the residual rice yield that was consistent with a decrease in soil moisture during the same time period. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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19 pages, 31757 KiB  
Article
In-Flight Relative Radiometric Calibration of a Wide Field of View Directional Polarimetric Camera Based on the Rayleigh Scattering over Ocean
by Sifeng Zhu, Zhengqiang Li, Lili Qie, Hua Xu, Bangyu Ge, Yisong Xie, Rui Qiao, Yanqing Xie, Jin Hong, Binghuan Meng, Bihai Tu and Feinan Chen
Remote Sens. 2022, 14(5), 1211; https://doi.org/10.3390/rs14051211 - 1 Mar 2022
Cited by 13 | Viewed by 2796
Abstract
The directional polarimetric camera (DPC) is a Chinese satellite sensor with a large field of view (FOV) (±50° both along-track and cross-track) and a high spatial resolution (about 3.3 km at nadir) that operates in a sun-synchronous orbit. It is a difficult task [...] Read more.
The directional polarimetric camera (DPC) is a Chinese satellite sensor with a large field of view (FOV) (±50° both along-track and cross-track) and a high spatial resolution (about 3.3 km at nadir) that operates in a sun-synchronous orbit. It is a difficult task to calibrate the in-flight relative radiometric variation of the sensors with such a wide FOV. In this study, a new method based on Rayleigh scattering over the ocean is developed to estimate the radiometric sensitivity variation over the whole FOV of DPC. Firstly, the theoretical uncertainty of the method is analyzed to calibrate the relative radiometric response. The calibration uncertainties are about 2–6.9% (depending on the wavelength) when the view zenith angle (VZA) is 0° and decrease to about 1–3.8% when VZA increases to 70°. Then, the method is applied to evaluate the long-term radiometric drift of the DPC. It is found that the radiometric response of DPC/GaoFen-5 over the whole FOV is progressively drifting over time. The sensitivity at shorter bands decreases more strongly than longer bands, and at the central part of the optics decreases more strongly than the marginal part. During the 14 months (from March 2019 to April 2020) of operational running in-orbit, the DPC radiometric responses of 443 nm, 490 nm, 565 nm, and 670 nm bands drifted by about 4.44–23.08%, 4.75–16.22%, 3.86–9.81%, and 4.7–16.86%, respectively, from the marginal to the central part of the FOV. The radiometric sensitivity has become more stable since January 2020. The monthly radiometric drift is separated into the relative radiometric part and the absolute radiometric part. The relative radiometric drift of DPC is found to be smoothly varying with VZA, which can be parameterized as a polynomial function via VZA. At last, the temporal radiometric drift of DPC/GaoFen-5 is corrected by combining the relative and absolute radiometric coefficients. The correction is convincing by cross calibration with MODIS/Aqua observation over the desert sites and improving the aerosol retrievals. The Rayleigh method in this study is efficient for the radiometric sensitivity calibration of wide FOV satellite sensors. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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17 pages, 5099 KiB  
Article
Long-Periodic Analysis of Boresight Misalignment of Ziyuan3-01 Three-Line Camera
by Xiaoyong Zhu, Xinming Tang, Guo Zhang, Bin Liu, Wenmin Hu and Hongbo Pan
Remote Sens. 2022, 14(5), 1157; https://doi.org/10.3390/rs14051157 - 26 Feb 2022
Cited by 2 | Viewed by 2143
Abstract
The Ziyuan3-01 (ZY3-01) satellite is China’s first civilian stereo surveying and mapping satellite to meet the 1:50,000 scale mapping requirements, and has been operated in orbit for 10 years. The boresight misalignment of the three-line camera (TLC) is an essential factor affecting the [...] Read more.
The Ziyuan3-01 (ZY3-01) satellite is China’s first civilian stereo surveying and mapping satellite to meet the 1:50,000 scale mapping requirements, and has been operated in orbit for 10 years. The boresight misalignment of the three-line camera (TLC) is an essential factor affecting the geolocation accuracy, which is a principal concern for stereo mapping satellites. However, the relative relationships of TLC are often regarded as fixed for the same ground scene in most traditional geometric calibrations, without considering the on-orbit long-periodic changes. In this paper, we propose a long-periodic method to analyze and estimate the boresight misalignments between three cameras, with the attitude estimation of a nadir (NAD) camera as the benchmark. Offsets and drifts of the three cameras were calculated and calibrated with different compensation models using scale invariant feature transform (SIFT) points as the ground control. Ten simultaneous NAD–Forward (FWD)–Backward (BWD) imagery of the ZY3-01 satellite acquired from 2012 to 2020 were selected to verify the long-periodic changes in TLC boresight misalignments. The results indicate that the boresight alignment angles of ZY3-01 TLC are dynamic during the long-periodic flight, but the structure of TLC is stable for the misalignments of both FWD and BWD within only 7 arc seconds, which can provide a positive reference for subsequent satellite design and long-periodic on-orbit geometric calibration. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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23 pages, 9598 KiB  
Article
Varying Amplitude Vibration Phase Suppression Algorithm in ISAL Imaging
by Hongfei Yin, Liang Guo, Yachao Li, Liang Han, Mengdao Xing and Xiaodong Zeng
Remote Sens. 2022, 14(5), 1122; https://doi.org/10.3390/rs14051122 - 24 Feb 2022
Cited by 5 | Viewed by 1766
Abstract
Platform vibration introduces sinusoidal modulation in inverse synthetic aperture lidar (ISAL) imaging, which causes paired echoes in ISAL imaging. In this paper, a varying amplitude vibration phase suppression algorithm is proposed. Working without prior knowledge, the proposed algorithm can suppress paired echoes under [...] Read more.
Platform vibration introduces sinusoidal modulation in inverse synthetic aperture lidar (ISAL) imaging, which causes paired echoes in ISAL imaging. In this paper, a varying amplitude vibration phase suppression algorithm is proposed. Working without prior knowledge, the proposed algorithm can suppress paired echoes under the condition of varying vibration amplitude and will not introduce new phase errors. Furthermore, the method is suitable for the imaging scene without isolated points. Both the simulated and real experiment results of ISAL turntable data demonstrate the effectiveness of the proposed algorithm. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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16 pages, 10480 KiB  
Technical Note
A Novel Time-Domain Frequency Diverse Array HRWS Imaging Scheme for Spotlight SAR
by Yuhao Wen, Zhimin Zhang, Zhen Chen, Jinsong Qiu, Mingshan Ren and Xiangrui Meng
Remote Sens. 2022, 14(5), 1085; https://doi.org/10.3390/rs14051085 - 23 Feb 2022
Cited by 4 | Viewed by 2101
Abstract
The frequency diverse array (FDA) technique is a novel scheme for high resolution wide swath synthetic aperture radar (SAR) imaging, which employs a frequency increment across the array elements. This introduces a range-angle-dependence to the transmission steering vector, which is exploited for range [...] Read more.
The frequency diverse array (FDA) technique is a novel scheme for high resolution wide swath synthetic aperture radar (SAR) imaging, which employs a frequency increment across the array elements. This introduces a range-angle-dependence to the transmission steering vector, which is exploited for range ambiguity resolution in strip-map SAR. Generally in spotlight mode, scatterers dispersively distributed in azimuth have different Doppler histories, in which the range ambiguity resolution for strip-map SAR fails. To extend the flexibility of FDA, in this paper a novel FDA imaging scheme for spotlight mode SAR is proposed. Exploiting the property of the same illuminated period of all scatterers in spotlight mode, the proposed scheme is carried out entirely in the azimuth time domain, which allows for higher processing efficiency and real-time implementations. Still, excessive Doppler history differences among scatterers deteriorate the scheme performance for azimuth-edge scatterers. Aiming at this situation, a residual angle phase compensation in time domain is considered for the cases of a large azimuth beam width, improving the applicability of the proposed scheme. Compared with existing methods, the proposed spotlight FDA-SAR offers the possibility of achieving simultaneously high azimuth resolution and wide swath performance with high efficiency. Simulations and analyses are performed to demonstrate the effectiveness of the proposed spotlight FDA-SAR scheme. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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21 pages, 7507 KiB  
Article
An Improved Algorithm for the Retrieval of the Antarctic Sea Ice Freeboard and Thickness from ICESat-2 Altimeter Data
by Xiaoping Pang, Yizhuo Chen, Qing Ji, Guoyuan Li, Lijian Shi, Musheng Lan and Zeyu Liang
Remote Sens. 2022, 14(5), 1069; https://doi.org/10.3390/rs14051069 - 22 Feb 2022
Cited by 5 | Viewed by 3006
Abstract
ICESat-2 altimeter data could be used to estimate sea ice freeboard and thickness values with a higher measuring accuracy than that achievable with data provided by previous altimeter satellites. This study developed an improved algorithm considering variable lead proportions based on the lowest [...] Read more.
ICESat-2 altimeter data could be used to estimate sea ice freeboard and thickness values with a higher measuring accuracy than that achievable with data provided by previous altimeter satellites. This study developed an improved algorithm considering variable lead proportions based on the lowest point method to derive the sea surface height for the retrieval of Antarctic sea ice freeboard and thickness values from ICESat-2 ATL-07 data. We first collocated ICESat-2 tracks to corresponding Sentinel-1 SAR images and calculated lead (seawater) proportions along each track to estimate the sea surface height in the Antarctic Ocean. Then, the Antarctic sea ice freeboard and thickness were estimated based on a local sea surface height reference and a static equilibrium equation. Finally, we assessed the accuracy of our improved algorithm and ICESat-2 data product in the retrieval of the Antarctic sea ice thickness by comparing the calculated values to ship-based observational sea ice thickness values acquired during the 35th Chinese Antarctic Research Expedition (CHINARE-35). The results indicate that the Antarctic sea ice freeboard estimated with the improved lowest point method was slightly larger than that estimated with the ICESat-2 data product algorithm. The root mean squared error (RMSE) of the improved lowest point method was 35 cm with the CHINARE-35 measured sea ice thickness, which was smaller than that determined with the ICESat-2 data product algorithm (65 cm). Our improved algorithm could provide more accurate data on the Antarctic sea ice freeboard and thickness, thus supporting Antarctic sea ice monitoring and the evaluation of its change under global effects. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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30 pages, 21712 KiB  
Article
An Integrated Platform for Ground-Motion Mapping, Local to Regional Scale; Examples from SE Europe
by Valentin Poncoş, Irina Stanciu, Delia Teleagă, Liviu Maţenco, István Bozsó, Alexandru Szakács, Dan Birtas, Ştefan-Adrian Toma, Adrian Stănică and Vlad Rădulescu
Remote Sens. 2022, 14(4), 1046; https://doi.org/10.3390/rs14041046 - 21 Feb 2022
Cited by 4 | Viewed by 3049
Abstract
Ground and infrastructure stability are important for our technologically based civilization. Infrastructure projects take into consideration the risk posed by ground displacement (e.g., seismicity, geological conditions and geomorphology). To address this risk, earth scientists and civil engineers employ a range of measurement technologies, [...] Read more.
Ground and infrastructure stability are important for our technologically based civilization. Infrastructure projects take into consideration the risk posed by ground displacement (e.g., seismicity, geological conditions and geomorphology). To address this risk, earth scientists and civil engineers employ a range of measurement technologies, such as optical/laser leveling, GNSS and, lately, SAR interferometry. Currently there is a rich source of measurement information provided in various formats that covers most of the industrialized world. Integration of this information becomes an issue that will only increase in importance in the future. This work describes a practical approach to address and validate integrated stability measurements through the development of a platform that could be easily used by a variety of groups, from geoscientists to civil engineers and also private citizens with no training in this field. The platform enables quick cross-validation between different data sources, easy detection of critical areas at all scales (from large-scale individual buildings to small-scale tectonics) and can be linked to end-users from various monitoring fields and countries for automated notifications. This work is closing the gap between the specialized monitoring work and the general public, delivering the full value of technology for societal benefits in a free and open manner. The platform is calibrated and validated by an application of SAR interferometry data to specific situations in the general area of the Romanian Carpathians and their foreland. The results demonstrate an interplay between anthropogenically induced changes and high-amplitude active tectono–sedimentary processes creating rapid regional and local topographic variations. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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27 pages, 515 KiB  
Review
Mapping of Urban Vegetation with High-Resolution Remote Sensing: A Review
by Robbe Neyns and Frank Canters
Remote Sens. 2022, 14(4), 1031; https://doi.org/10.3390/rs14041031 - 21 Feb 2022
Cited by 82 | Viewed by 11504
Abstract
Green space is increasingly recognized as an important component of the urban environment. Adequate management and planning of urban green space is crucial to maximize its benefits for urban inhabitants and for the urban ecosystem in general. Inventorying urban vegetation is a costly [...] Read more.
Green space is increasingly recognized as an important component of the urban environment. Adequate management and planning of urban green space is crucial to maximize its benefits for urban inhabitants and for the urban ecosystem in general. Inventorying urban vegetation is a costly and time-consuming process. The development of new remote sensing techniques to map and monitor vegetation has therefore become an important topic of interest to many scholars. Based on a comprehensive survey of the literature, this review article provides an overview of the main approaches proposed to map urban vegetation from high-resolution remotely sensed data. Studies are reviewed from three perspectives: (a) the vegetation typology, (b) the remote sensing data used and (c) the mapping approach applied. With regard to vegetation typology, a distinction is made between studies focusing on the mapping of functional vegetation types and studies performing mapping of lower-level taxonomic ranks, with the latter mainly focusing on urban trees. A wide variety of high-resolution imagery has been used by researchers for both types of mapping. The fusion of various types of remote sensing data, as well as the inclusion of phenological information through the use of multi-temporal imagery, prove to be the most promising avenues to improve mapping accuracy. With regard to mapping approaches, the use of deep learning is becoming more established, mostly for the mapping of tree species. Through this survey, several research gaps could be identified. Interest in the mapping of non-tree species in urban environments is still limited. The same holds for the mapping of understory species. Most studies focus on the mapping of public green spaces, while interest in the mapping of private green space is less common. The use of imagery with a high spatial and temporal resolution, enabling the retrieval of phenological information for mapping and monitoring vegetation at the species level, still proves to be limited in urban contexts. Hence, mapping approaches specifically tailored towards time-series analysis and the use of new data sources seem to hold great promise for advancing the field. Finally, unsupervised learning techniques and active learning, so far rarely applied in urban vegetation mapping, are also areas where significant progress can be expected. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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22 pages, 2296 KiB  
Article
The Use of C-Band and X-Band SAR with Machine Learning for Detecting Small-Scale Mining
by Gabrielle Janse van Rensburg and Jaco Kemp
Remote Sens. 2022, 14(4), 977; https://doi.org/10.3390/rs14040977 - 17 Feb 2022
Cited by 12 | Viewed by 4013
Abstract
Illicit small-scale mining occurs in many tropical regions and is both environmentally and socially hazardous. The aim of this study was to determine whether the classification of Synthetic Aperture Radar (SAR) imagery could detect and map small-scale mining in Ghana by analyzing multi-temporal [...] Read more.
Illicit small-scale mining occurs in many tropical regions and is both environmentally and socially hazardous. The aim of this study was to determine whether the classification of Synthetic Aperture Radar (SAR) imagery could detect and map small-scale mining in Ghana by analyzing multi-temporal filtering applied to three SAR datasets and testing five machine-learning classifiers. Using an object-based image analysis approach, we were successful in classifying water bodies associated with small-scale mining. The multi-temporally filtered Sentinel-1 dataset was the most reliable, with kappa coefficients at 0.65 and 0.82 for the multi-class classification scheme and binary-water classification scheme, respectively. The single-date Sentinel-1 dataset has the highest overall accuracy, at 90.93% for the binary water classification scheme. The KompSAT-5 dataset achieved the lowest accuracy at an overall accuracy of 80.61% and a kappa coefficient of 0.61 for a binary-water classification scheme. The experimental results demonstrated that it is possible to classify water as a proxy to identify illegal mining activities and that SAR is a potentially accurate and reliable solution for the detection of SSM in tropical regions such as Ghana. Therefore, using SAR can assist local governments in regulating small-scale mining activities by providing specific spatial information on the whereabouts of small-scale mining locations. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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19 pages, 2177 KiB  
Article
Learning Pairwise Potential CRFs in Deep Siamese Network for Change Detection
by Dalong Zheng, Zhihui Wei, Zebin Wu and Jia Liu
Remote Sens. 2022, 14(4), 841; https://doi.org/10.3390/rs14040841 - 10 Feb 2022
Cited by 10 | Viewed by 2761
Abstract
Very high resolution (VHR) images change detection plays an important role in many remote sensing applications, such as military reconnaissance, urban planning and natural resource monitoring. Recently, fully connected conditional random field (FCCRF)-facilitated deep convolutional neural networks have shown promising results in change [...] Read more.
Very high resolution (VHR) images change detection plays an important role in many remote sensing applications, such as military reconnaissance, urban planning and natural resource monitoring. Recently, fully connected conditional random field (FCCRF)-facilitated deep convolutional neural networks have shown promising results in change detection. However, the FCCRF in change detection currently is still postprocessing based on the output of the front-end network, which is not a convenient end-to-end network model and cannot combine front-end network knowledge with the knowledge of pairwise potential. Therefore, we propose a new end-to-end deep Siamese pairwise potential CRFs network (PPNet) for VHR images change detection. Specifically, this method adds a conditional random field recurrent neural network (CRF-RNN) unit into the convolutional neural network and integrates the knowledge of unary potential and pairwise potential in the end-to-end training process, aiming to refine the edges of changed areas and to remove the distant noise. In order to correct the front-end network identification errors, the method uses effective channel attention (ECA) to further effectively distinguish the change areas. Our experimental results on two data sets verify that the proposed method has more advanced capability with almost no increase in the number of parameters and effectively avoids the overfitting phenomenon in the training process. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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28 pages, 4810 KiB  
Article
Non-Cooperative SAR Automatic Target Recognition Based on Scattering Centers Models
by Gustavo F. Araujo, Renato Machado and Mats I. Pettersson
Sensors 2022, 22(3), 1293; https://doi.org/10.3390/s22031293 - 8 Feb 2022
Cited by 7 | Viewed by 3294
Abstract
This article proposes an Automatic Target Recognition (ATR) algorithm to classify non-cooperative targets in Synthetic Aperture Radar (SAR) images. The scarcity or nonexistence of measured SAR data demands that classification algorithms rely only on synthetic data for training purposes. Based on a model [...] Read more.
This article proposes an Automatic Target Recognition (ATR) algorithm to classify non-cooperative targets in Synthetic Aperture Radar (SAR) images. The scarcity or nonexistence of measured SAR data demands that classification algorithms rely only on synthetic data for training purposes. Based on a model represented by the set of scattering centers extracted from purely synthetic data, the proposed algorithm generates hypotheses for the set of scattering centers extracted from the target under test belonging to each class. A Goodness of Fit test is considered to verify each hypothesis, where the Likelihood Ratio Test is modified by a scattering center-weighting function common to both the model and target. Some algorithm variations are assessed for scattering center extraction and hypothesis generation and verification. The proposed solution is the first model-based classification algorithm to address the recently released Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset on a 100% synthetic training data basis. As a result, an accuracy of 91.30% in a 10-target test within a class experiment under Standard Operating Conditions (SOCs) was obtained. The algorithm was also pioneered in testing the SAMPLE dataset in Extend Operating Conditions (EOCs), assuming noise contamination and different target configurations. The proposed algorithm was shown to be robust for SNRs greater than −5 dB. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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25 pages, 9033 KiB  
Article
A Spatial Downscaling Approach for WindSat Satellite Sea Surface Wind Based on Generative Adversarial Networks and Dual Learning Scheme
by Jia Liu, Yongjian Sun, Kaijun Ren, Yanlai Zhao, Kefeng Deng and Lizhe Wang
Remote Sens. 2022, 14(3), 769; https://doi.org/10.3390/rs14030769 - 7 Feb 2022
Cited by 8 | Viewed by 3080
Abstract
Sea surface wind (SSW) is a crucial parameter for meteorological and oceanographic research, and accurate observation of SSW is valuable for a wide range of applications. However, most existing SSW data products are at a coarse spatial resolution, which is insufficient, especially for [...] Read more.
Sea surface wind (SSW) is a crucial parameter for meteorological and oceanographic research, and accurate observation of SSW is valuable for a wide range of applications. However, most existing SSW data products are at a coarse spatial resolution, which is insufficient, especially for regional or local studies. Therefore, in this paper, to derive finer-resolution estimates of SSW, we present a novel statistical downscaling approach for satellite SSW based on generative adversarial networks and dual learning scheme, taking WindSat as a typical example. The dual learning scheme performs a primal task to reconstruct high resolution SSW, and a dual task to estimate the degradation kernels, which form a closed loop and are simultaneously learned, thus introducing an additional constraint to reduce the solution space. The integration of a dual learning scheme as the generator into the generative adversarial network structure further yield better downscaling performance by fine-tuning the generated SSW closer to high-resolution SSW. Besides, a model adaptation strategy was exploited to enhance the capacity for downscaling from low-resolution SSW without high-resolution ground truth. Comprehensive experiments were conducted on both the synthetic paired and unpaired SSW data. In the study areas of the East Coast of North America and the North Indian Ocean, in this work, the downscaling results to 0.25° (high resolution on the synthetic dataset), 0.03125° (8× downscaling), and 0.015625° (16× downscaling) of the proposed approach achieve the highest accuracy in terms of root mean square error and R-Square. The downscaling resolution can be enhanced by increasing the basic blocks in the generator. The highest downscaling reconstruction quality in terms of peak signal-to-noise ratio and structural similarity index was also achieved on the synthetic dataset with high-resolution ground truth. The experimental results demonstrate the effectiveness of the proposed downscaling network and the superior performance compared with the other typical advanced downscaling methods, including bicubic interpolation, DeepSD, dual regression networks, and adversarial DeepSD. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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21 pages, 6892 KiB  
Article
SSDBN: A Single-Side Dual-Branch Network with Encoder–Decoder for Building Extraction
by Yang Li, Hui Lu, Qi Liu, Yonghong Zhang and Xiaodong Liu
Remote Sens. 2022, 14(3), 768; https://doi.org/10.3390/rs14030768 - 7 Feb 2022
Cited by 14 | Viewed by 2819
Abstract
In the field of building detection research, an accurate, state-of-the-art semantic segmentation model must be constructed to classify each pixel of the image, which has an important reference value for the statistical work of a building area. Recent research efforts have been devoted [...] Read more.
In the field of building detection research, an accurate, state-of-the-art semantic segmentation model must be constructed to classify each pixel of the image, which has an important reference value for the statistical work of a building area. Recent research efforts have been devoted to semantic segmentation using deep learning approaches, which can be further divided into two aspects. In this paper, we propose a single-side dual-branch network (SSDBN) based on an encoder–decoder structure, where an improved Res2Net model is used at the encoder stage to extract the basic feature information of prepared images while a dual-branch module is deployed at the decoder stage. An intermediate framework was designed using a new feature information fusion methods to capture more semantic information in a small area. The dual-branch decoding module contains a deconvolution branch and a feature enhancement branch, which are responsible for capturing multi-scale information and enhancing high-level semantic details, respectively. All experiments were conducted using the Massachusetts Buildings Dataset and WHU Satellite Dataset I (global cities). The proposed model showed better performance than other recent approaches, achieving an F1-score of 87.69% and an IoU of 75.83% with a low network size volume (5.11 M), internal parameters (19.8 MB), and GFLOPs (22.54), on the Massachusetts Buildings Dataset. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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19 pages, 32926 KiB  
Article
Bamboo Forest Mapping in China Using the Dense Landsat 8 Image Archive and Google Earth Engine
by Shuhua Qi, Bin Song, Chong Liu, Peng Gong, Jin Luo, Meinan Zhang and Tianwei Xiong
Remote Sens. 2022, 14(3), 762; https://doi.org/10.3390/rs14030762 - 7 Feb 2022
Cited by 31 | Viewed by 12534
Abstract
It is of great significance to understand the extent and distribution of bamboo for its valuable ecological services and economic benefits. However, it is challenging to map bamboo using remote sensing images over a large area because of the similarity between bamboo and [...] Read more.
It is of great significance to understand the extent and distribution of bamboo for its valuable ecological services and economic benefits. However, it is challenging to map bamboo using remote sensing images over a large area because of the similarity between bamboo and other vegetation types, the availability of clear optical images, huge workload of image processing, and sample collection. In this study, we use the Landsat 8 times series images archive to map bamboo forests in China via the Google Earth engine. Several spectral indices were calculated and used as classification features, including the normalized difference vegetation index (NDVI), the normalized difference moisture index (NDMI) and textural features of the gray-level co-occurrence matrix (GLCM). We found that the bamboo forest covered an area of 709.92 × 104 hectares, with the provinces of Fujian, Jiangxi, and Zhejiang containing the largest area concentrations. The bamboo forest map was accurate and reliable with an average producer’s accuracy of 89.97%, user’s accuracy of 78.45% and kappa coefficient of 0.7789. In addition, bamboo was mainly distributed in forests with an elevation of 300–1200 m above sea level, average annual precipitation of 1200–1500 mm and average day land surface temperature of 19–25 °C. The NDMI is particularly useful in differentiating bamboo from other vegetation because of the clear difference in canopy moisture content, whilst NDVI and elevation are also helpful to improve the bamboo classification accuracy. The bamboo forest map will be helpful for bamboo forest industry planning and could be used for evaluating the ecological service of the bamboo forest. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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15 pages, 3454 KiB  
Technical Note
An In-Orbit Measurement Method for Elevation Antenna Pattern of MEO Synthetic Aperture Radar Based on Nano Calibration Satellite
by Tian Qiu, Yu Wang, Jun Hong, Kaichu Xing, Shaoyan Du and Jingwen Mu
Remote Sens. 2022, 14(3), 741; https://doi.org/10.3390/rs14030741 - 5 Feb 2022
Cited by 2 | Viewed by 3099
Abstract
The medium-Earth-orbit synthetic aperture radar (MEO-SAR) is deployed at orbit altitude above low-Earth-orbit synthetic aperture radar (LEO-SAR, around 2000 km) and below the geosynchronous orbit SAR (GEO-SAR, near 35786 km) to cover a wide swath, which is four to five times larger than [...] Read more.
The medium-Earth-orbit synthetic aperture radar (MEO-SAR) is deployed at orbit altitude above low-Earth-orbit synthetic aperture radar (LEO-SAR, around 2000 km) and below the geosynchronous orbit SAR (GEO-SAR, near 35786 km) to cover a wide swath, which is four to five times larger than LEO-SAR. Therefore, the measurement method for the LEO-SAR elevation antenna pattern using the SAR data acquired over the Amazon tropical rainforest (ground-based method), where the typical width of rainforest area is approximately 150 km, can hardly meet the requirement of a wide swath to determine the MEO-SAR antenna elevation pattern. Moreover, several new MEO-SAR systems are now proposed that will use low frequency, and the low frequency penetration characteristics may affect the elevation antenna pattern determination using homogenous distributed targets such as the Amazon rainforest. This paper proposes a novel space-based method for the in-orbit measurement of the elevation antenna pattern of MEO-SAR based on one nano calibration satellite mounted with a receiver. Through appropriate orbit design, the nano calibration satellite can fly across the entire MEO-SAR swath along the range direction, and the elevation antenna pattern envelope can be extracted from the data recorded by the receiver. Simulation work is performed to verify the feasibility of the proposed space-based method, and the measurement accuracy of this method is analyzed. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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24 pages, 42547 KiB  
Article
A Robust 3D Density Descriptor Based on Histogram of Oriented Primary Edge Structure for SAR and Optical Image Co-Registration
by Shuo Li, Xiaolei Lv, Jian Ren and Jian Li
Remote Sens. 2022, 14(3), 630; https://doi.org/10.3390/rs14030630 - 28 Jan 2022
Cited by 10 | Viewed by 3099
Abstract
The co-registration between SAR and optical images is a challenging task because of the speckle noise of SAR and the nonlinear radiation distortions (NRD), particularly in the one-look situation. In this paper, we propose a novel density descriptor based on the histogram of [...] Read more.
The co-registration between SAR and optical images is a challenging task because of the speckle noise of SAR and the nonlinear radiation distortions (NRD), particularly in the one-look situation. In this paper, we propose a novel density descriptor based on the histogram of oriented primary edge structure (HOPES) for the co-registration of SAR and optical images, aiming to describe the shape structure of patches more firm. In order to extract the primary edge structure, we develop the novel multi-scale sigmoid Gabor (MSG) detector and a primary edge fusion algorithm. Based on the HOPES, we propose the co-registration method. To obtain stable and uniform keypoints, the non-maximum suppressed SAR-Harris (NMS-SAR-Harris) and deviding grids methods are used. NMS-SSD fast template matching and fast sample consensus (FSC) algorithm are used to further complete and optimize matching. We use two one-look simulated SAR images to demonstrate that the signal-to-noise ratio (SNR) of MSG is more than 10 dB higher than other state-of-the-stage detectors; the binary edge maps and F-score show that MSG has more accurate positioning performance. Compared with the other state-of-the-stage co-registration methods, the image co-registration results obtained on seven pairs of test images show that, the correct match rate (CMR) and the root mean squared error (RMSE) improve by more than 25% and 15% on average, respectively. It is experimentally demonstrated that the HOPES is robust against speckle noise and NRD, which can effectively improve the matching success rate and accuracy. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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26 pages, 31762 KiB  
Article
Building Change Detection Based on 3D Co-Segmentation Using Satellite Stereo Imagery
by Hao Wang, Xiaolei Lv, Kaiyu Zhang and Bin Guo
Remote Sens. 2022, 14(3), 628; https://doi.org/10.3390/rs14030628 - 28 Jan 2022
Cited by 8 | Viewed by 3966
Abstract
Building change detection using remote sensing images is significant to urban planning and city monitoring. The height information extracted from very high resolution (VHR) satellite stereo images provides valuable information for the detection of 3D changes in urban buildings. However, most existing 3D [...] Read more.
Building change detection using remote sensing images is significant to urban planning and city monitoring. The height information extracted from very high resolution (VHR) satellite stereo images provides valuable information for the detection of 3D changes in urban buildings. However, most existing 3D change detection algorithms are based on the independent segmentation of two-temporal images and the feature fusion of spectral change and height change. These methods do not consider 3D change information and spatial context information simultaneously. In this paper, we propose a novel building change detection algorithm based on 3D Co-segmentation, which makes full use of the 3D change information contained in the stereoscope data. An energy function containing spectral change information, height change information, and spatial context information is constructed. Image change feature is extracted using morphological building index (MBI), and height change feature is obtained by robust normalized digital surface models (nDSM) difference. 3D Co-segmentation divides the two-temporal images into the changed foreground and unchanged background through the graph-cut-based energy minimization method. The object-to-object detection results are obtained through overlay analysis, and the quantitative height change values are calculated according to this correspondence. The superiority of the proposed algorithm is that it can obtain the changes of buildings in planar and vertical simultaneously. The performance of the algorithm is evaluated in detail using six groups of satellite datasets. The experimental results prove the effectiveness of the proposed building change detection algorithm. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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14 pages, 13889 KiB  
Technical Note
Phase Mismatch Calibration for Dual-Channel Sliding Spotlight SAR-GMTI
by Zhenning Zhang, Weidong Yu, Mingjie Zheng, Liangbo Zhao and Zi-Xuan Zhou
Remote Sens. 2022, 14(3), 617; https://doi.org/10.3390/rs14030617 - 27 Jan 2022
Cited by 3 | Viewed by 2791
Abstract
This article investigates channel phase mismatch calibration during the application of displaced-phase-center antenna (DPCA) in dual-channel sliding spotlight synthetic aperture radar (SAR) for ground moving target indication (GMTI). In sliding spotlight SAR, the utilization of beam progressive sweeping in azimuth causes antenna phase [...] Read more.
This article investigates channel phase mismatch calibration during the application of displaced-phase-center antenna (DPCA) in dual-channel sliding spotlight synthetic aperture radar (SAR) for ground moving target indication (GMTI). In sliding spotlight SAR, the utilization of beam progressive sweeping in azimuth causes antenna phase centers to be misaligned from the sensor path, resulting in the phase mismatch between channels. Then, spatial channel co-registration required in the DPCA cannot be achieved directly by an azimuth time shift. In this study, a calibration method based on scanning geometry of the dual-channel sliding spotlight SAR is developed to address this issue. Moreover, the effect of the phase mismatch calibration on the estimation of azimuth time difference between the two channels is derived and analyzed in depth. The clutter suppression results processed from experimental data acquired by a C-band dual-channel SAR system (Gaofen-3) operated in sliding spotlight mode are shown for the first time to demonstrate the effective phase mismatch calibration. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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20 pages, 6927 KiB  
Article
Predicting the Chlorophyll Content of Maize over Phenotyping as a Proxy for Crop Health in Smallholder Farming Systems
by Kiara Brewer, Alistair Clulow, Mbulisi Sibanda, Shaeden Gokool, Vivek Naiken and Tafadzwanashe Mabhaudhi
Remote Sens. 2022, 14(3), 518; https://doi.org/10.3390/rs14030518 - 21 Jan 2022
Cited by 46 | Viewed by 7244
Abstract
Smallholder farmers depend on healthy and productive crop yields to sustain their socio-economic status and ensure livelihood security. Advances in South African precision agriculture in the form of unmanned aerial vehicles (UAVs) provide spatially explicit near-real-time information that can be used to assess [...] Read more.
Smallholder farmers depend on healthy and productive crop yields to sustain their socio-economic status and ensure livelihood security. Advances in South African precision agriculture in the form of unmanned aerial vehicles (UAVs) provide spatially explicit near-real-time information that can be used to assess crop dynamics and inform smallholder farmers. The use of UAVs with remote-sensing techniques allows for the acquisition of high spatial resolution data at various spatio-temporal planes, which is particularly useful at the scale of fields and farms. Specifically, crop chlorophyll content is assessed as it is one of the best known and reliable indicators of crop health, due to its biophysical pigment and biochemical processes that indicate plant productivity. In this regard, the study evaluated the utility of multispectral UAV imagery using the random forest machine learning algorithm to estimate the chlorophyll content of maize through the various growth stages. The results showed that the near-infrared and red-edge wavelength bands and vegetation indices derived from these wavelengths were essential for estimating chlorophyll content during the phenotyping of maize. Furthermore, the random forest model optimally estimated the chlorophyll content of maize over the various phenological stages. Particularly, maize chlorophyll was best predicted during the early reproductive, late vegetative, and early vegetative growth stages to RMSE accuracies of 40.4 µmol/m−2, 39 µmol/m−2, and 61.6 µmol/m−2, respectively. The least accurate chlorophyll content results were predicted during the mid-reproductive and late reproductive growth stages to RMSE accuracies of 66.6 µmol/m−2 and 69.6 µmol/m−2, respectively, as a consequence of a hailstorm. A resultant chlorophyll variation map of the maize growth stages captured the spatial heterogeneity of chlorophyll within the maize field. Therefore, the study’s findings demonstrate that the use of remotely sensed UAV imagery with a robust machine algorithm is a critical tool to support the decision-making and management in smallholder farms. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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22 pages, 9090 KiB  
Article
Comparison of DEM Super-Resolution Methods Based on Interpolation and Neural Networks
by Yifan Zhang and Wenhao Yu
Sensors 2022, 22(3), 745; https://doi.org/10.3390/s22030745 - 19 Jan 2022
Cited by 34 | Viewed by 5220
Abstract
High-resolution digital elevation models (DEMs) play a critical role in geospatial databases, which can be applied to many terrain-related studies such as facility siting, hydrological analysis, and urban design. However, due to the limitation of precision of equipment, there are big gaps to [...] Read more.
High-resolution digital elevation models (DEMs) play a critical role in geospatial databases, which can be applied to many terrain-related studies such as facility siting, hydrological analysis, and urban design. However, due to the limitation of precision of equipment, there are big gaps to collect high-resolution DEM data. A practical idea is to recover high-resolution DEMs from easily obtained low-resolution DEMs, and this process is termed DEM super-resolution (SR). However, traditional DEM SR methods (e.g., bicubic interpolation) tend to over-smooth high-frequency regions on account of the operation of averaging local variations. With the recent development of machine learning, image SR methods have made great progress. Nevertheless, due to the complexity of terrain characters (e.g., peak and valley) and the huge difference between elevation field and image RGB (Red, Green, and Blue) value field, there are few works that apply image SR methods to the task of DEM SR. Therefore, this paper investigates the question of whether the state-of-the-art image SR methods are appropriate for DEM SR. More specifically, the traditional interpolation method and three excellent SR methods based on neural networks are chosen for comparison. Experimental results suggest that SRGAN (Super-Resolution with Generative Adversarial Network) presents the best performance on accuracy evaluation over a series of DEM SR experiments. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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18 pages, 9761 KiB  
Article
Internal Geometric Quality Improvement of Optical Remote Sensing Satellite Images with Image Reorientation
by Jinshan Cao, Nan Zhou, Haixing Shang, Zhiwei Ye and Zhiqi Zhang
Remote Sens. 2022, 14(3), 471; https://doi.org/10.3390/rs14030471 - 19 Jan 2022
Cited by 2 | Viewed by 2130
Abstract
When the in-orbit geometric calibration of optical satellite cameras is not performed in a precise or timely manner, optical remote sensing satellite images (ORSSIs) are produced with inaccurate camera parameters. The internal orientation (IO) biases of ORSSIs caused by inaccurate camera parameters show [...] Read more.
When the in-orbit geometric calibration of optical satellite cameras is not performed in a precise or timely manner, optical remote sensing satellite images (ORSSIs) are produced with inaccurate camera parameters. The internal orientation (IO) biases of ORSSIs caused by inaccurate camera parameters show a discontinuous distorted characteristic and cannot be compensated by a simple orientation model. The internal geometric quality of ORSSIs will, therefore, be worse than expected. In this study, from the ORSSI users’ perspective, a feasible internal geometric quality improvement method is presented for ORSSIs with image reorientation. In the presented method, a sensor orientation model, an external orientation (EO) model, and an IO model are successively established. Then, the EO and IO model parameters are estimated with ground control points. Finally, the original image is reoriented with the estimated IO model parameters. Ten HaiYang-1C coastal zone imager (CZI) images, a ZiYuan-3 02 nadir image, a GaoFen-1B panchromatic image, and a GaoFen-1D panchromatic image, were tested. The experimental results showed that the IO biases of ORSSIs caused by inaccurate camera parameters could be effectively eliminated with the presented method. The IO accuracies of all the tested images were improved to better than 1.0 pixel. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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11 pages, 3824 KiB  
Article
Neural Network-Based Strong Motion Prediction for On-Site Earthquake Early Warning
by You-Jing Chiang, Tai-Lin Chin and Da-Yi Chen
Sensors 2022, 22(3), 704; https://doi.org/10.3390/s22030704 - 18 Jan 2022
Cited by 16 | Viewed by 3222
Abstract
Developing on-site earthquake early warning systems has been a challenging problem because of time limitations and the amount of information that can be collected before the warning needs to be issued. A potential solution that could prevent severe disasters is to predict the [...] Read more.
Developing on-site earthquake early warning systems has been a challenging problem because of time limitations and the amount of information that can be collected before the warning needs to be issued. A potential solution that could prevent severe disasters is to predict the potential strong motion using the initial P-wave signal and provide warnings before serious ground shaking starts. In practice, the accuracy of prediction is the most critical issue for earthquake early warning systems. Traditional methods use certain criteria, selected through intuition or experience, to make the prediction. However, the criteria thresholds are difficult to select and may significantly affect the prediction accuracy. This paper investigates methods based on artificial intelligence for predicting the greatest earthquake ground motion early, when the P-wave arrives at seismograph stations. A neural network model is built to make the predictions using a small window of the initial P-wave acceleration signal. The model is trained by seismic waves collected from 1991 to 2019 in Taiwan and is evaluated by events in 2020 and 2021. From these evaluations, the proposed scheme significantly outperforms the threshold-based method in terms of its accuracy and average leading time. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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15 pages, 5098 KiB  
Article
LEO-Based Satellite Constellation for Moving Target Detection
by Chongdi Duan, Yu Li, Weiwei Wang and Jianguo Li
Remote Sens. 2022, 14(2), 403; https://doi.org/10.3390/rs14020403 - 16 Jan 2022
Cited by 8 | Viewed by 3191
Abstract
With the rapid development of cooperative detection technology, target fusion detection with regard of LEO satellites can be realized by means of their diverse observation configurations. However, the existing constant false alarm ratio (CFAR) detection research rarely involves the space-based target fusion detection [...] Read more.
With the rapid development of cooperative detection technology, target fusion detection with regard of LEO satellites can be realized by means of their diverse observation configurations. However, the existing constant false alarm ratio (CFAR) detection research rarely involves the space-based target fusion detection theory. In this paper, a novel multi-source fusion detection method based on LEO satellites is presented. Firstly, the pre-compensation function is constructed by employing the range and Doppler history of the cell where the antenna beam center is pointed. As a result, not only is the Doppler band broadening problem caused by the high-speed movement of the satellite platform, but the Doppler frequency rate (DFR) offset issue resulted from different observation configurations are alleviated synchronously. Then, the theoretical upper and lower limits of DFR are designed to achieve the effective clutter suppression and the accurate target echo fusion. Finally, the CFAR detection threshold based on the exponential weighted likelihood ratio is derived, which effectively increases the contrast ratio between the target cell and other background cells, and thus to provide an effective multi-source fusion detection method for LEO-based satellite constellation. Simulation results verify the effectiveness of the proposed algorithm. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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31 pages, 191953 KiB  
Article
Multi-Layer Overlapped Subaperture Algorithm for Extremely-High-Squint High-Resolution Wide-Swath SAR Imaging with Continuously Time-Varying Radar Parameters
by Yan Wang, Rui Min, Zegang Ding, Tao Zeng and Linghao Li
Remote Sens. 2022, 14(2), 365; https://doi.org/10.3390/rs14020365 - 13 Jan 2022
Cited by 5 | Viewed by 2360
Abstract
Extremely-high-squint (EHS) geometry of the traditional constant-parameter synthetic aperture radar (SAR) induces non-orthogonal wavenumber spectrum and hence the distortion of point spread function (PSF) in focused images. The method invented to overcome this problem is referred to as new-concept parameter-adjusting SAR. It corrects [...] Read more.
Extremely-high-squint (EHS) geometry of the traditional constant-parameter synthetic aperture radar (SAR) induces non-orthogonal wavenumber spectrum and hence the distortion of point spread function (PSF) in focused images. The method invented to overcome this problem is referred to as new-concept parameter-adjusting SAR. It corrects the PSF distortion by adjusting radar parameters, such as carrier frequency and chirp rate, based on instant data acquisition geometry. In this case, the characteristic of signal is quite different from the constant-parameter SAR and therefore, the traditional imaging algorithms cannot be directly applied for parameter-adjusting SAR imaging. However, the existing imaging algorithm for EHS parameter-adjusting SAR suffers from insufficient accuracy if a high-resolution wide-swath (HRWS) performance is required. Thus, this paper proposes a multi-layer overlapped subaperture algorithm (ML-OSA) for EHS HRWS parameter-adjusting SAR imaging with three main contributions: First, a more accurate signal model with time-varying radar parameters in high-squint geometry is derived. Second, phase errors are compensated with much higher accuracy by implementing multiple layers of coarse-to-fine spatially variant filters. Third, the analytical swath limit of the ML-OSA is derived by considering both the residual errors of signal model and phase compensations. The presented approach is validated via both the point- and extended-target computer simulations. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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19 pages, 3765 KiB  
Article
An Analytic Solution to Precipitation Attenuation Expression with Spaceborne Synthetic Aperture Radar Based on Volterra Integral Equation
by Ting Luo, Yanan Xie, Rui Wang and Xueying Yu
Remote Sens. 2022, 14(2), 357; https://doi.org/10.3390/rs14020357 - 13 Jan 2022
Cited by 4 | Viewed by 1957
Abstract
Precipitation is closely related to the production and daily life of human beings, so accurate precipitation measurement is of great significance. Spaceborne synthetic aperture radar (SAR) is a microwave remote sensing technology with high resolution, which provides an opportunity to improve the accuracy [...] Read more.
Precipitation is closely related to the production and daily life of human beings, so accurate precipitation measurement is of great significance. Spaceborne synthetic aperture radar (SAR) is a microwave remote sensing technology with high resolution, which provides an opportunity to improve the accuracy of precipitation inversion. In this paper, the radar attenuation expression is analyzed according to the scattering characteristics of rain, snow and ground. Combined with the Volterra integral equation of the second kind, the solution to the expression, the precipitation horizontal variation of the double-layer model, can be obtained. The simulated result of this method is in good agreement with the given horizontal variation of precipitation. Compared with the original VIE method, which only considers the effect of rainfall, the method in this paper considers both rainfall and snowfall; compared with the Model Oriented Statistical (MOS) method, the method in this paper not only reduces the number of empirical coefficients used and thus reduces the workload in the early stage and retrieval process and its application limits, but it will also increase the accuracy of the inversion of the horizontal variation. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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21 pages, 9748 KiB  
Article
Jitter Detection Method Based on Sequence CMOS Images Captured by Rolling Shutter Mode for High-Resolution Remote Sensing Satellite
by Ying Zhu, Tingting Yang, Mi Wang, Hanyu Hong, Yaozong Zhang, Lei Wang and Qilong Rao
Remote Sens. 2022, 14(2), 342; https://doi.org/10.3390/rs14020342 - 12 Jan 2022
Cited by 10 | Viewed by 2654
Abstract
Satellite platform jitter is a non-negligible factor that affects the image quality of optical cameras. Considering the limitations of traditional platform jitter detection methods that are based on attitude sensors and remote sensing images, this paper proposed a jitter detection method using sequence [...] Read more.
Satellite platform jitter is a non-negligible factor that affects the image quality of optical cameras. Considering the limitations of traditional platform jitter detection methods that are based on attitude sensors and remote sensing images, this paper proposed a jitter detection method using sequence CMOS images captured by rolling shutter for high-resolution remote sensing satellite. Through the three main steps of dense matching, relative jitter error analysis, and absolute jitter error modeling using sequence CMOS images, the periodic jitter error on the imaging focal plane of the spaceborne camera was able to be measured accurately. The experiments using three datasets with different jitter frequencies simulated from real remote sensing data were conducted. The experimental results showed that the jitter detection method using sequence CMOS images proposed in this paper can accurately recover the frequency, amplitude, and initial phase information of satellite jitter at 100 Hz, 10 Hz, and 2 Hz. Additionally, the detection accuracy reached 0.02 pixels, which can provide a reliable data basis for remote sensing image jitter error compensation. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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23 pages, 4979 KiB  
Article
Rice Mapping in Training Sample Shortage Regions Using a Deep Semantic Segmentation Model Trained on Pseudo-Labels
by Pengliang Wei, Ran Huang, Tao Lin and Jingfeng Huang
Remote Sens. 2022, 14(2), 328; https://doi.org/10.3390/rs14020328 - 11 Jan 2022
Cited by 15 | Viewed by 2635
Abstract
A deep semantic segmentation model-based method can achieve state-of-the-art accuracy and high computational efficiency in large-scale crop mapping. However, the model cannot be widely used in actual large-scale crop mapping applications, mainly because the annotation of ground truth data for deep semantic segmentation [...] Read more.
A deep semantic segmentation model-based method can achieve state-of-the-art accuracy and high computational efficiency in large-scale crop mapping. However, the model cannot be widely used in actual large-scale crop mapping applications, mainly because the annotation of ground truth data for deep semantic segmentation model training is time-consuming. At the operational level, it is extremely difficult to obtain a large amount of ground reference data by photointerpretation for the model training. Consequently, in order to solve this problem, this study introduces a workflow that aims to extract rice distribution information in training sample shortage regions, using a deep semantic segmentation model (i.e., U-Net) trained on pseudo-labels. Based on the time series Sentinel-1 images, Cropland Data Layer (CDL) and U-Net model, the optimal multi-temporal datasets for rice mapping were summarized, using the global search method. Then, based on the optimal multi-temporal datasets, the proposed workflow (a combination of K-Means and random forest) was directly used to extract the rice-distribution information of Jiangsu (i.e., the K–RF pseudo-labels). For comparison, the optimal well-trained U-Net model acquired from Arkansas (i.e., the transfer model) was also transferred to Jiangsu to extract local rice-distribution information (i.e., the TF pseudo-labels). Finally, the pseudo-labels with high confidences generated from the two methods were further used to retrain the U-Net models, which were suitable for rice mapping in Jiangsu. For different rice planting pattern regions of Jiangsu, the final results showed that, compared with the U-Net model trained on the TF pseudo-labels, the rice area extraction errors of pseudo-labels could be further reduced by using the U-Net model trained on the K–RF pseudo-labels. In addition, compared with the existing rule-based rice mapping methods, he U-Net model trained on the K–RF pseudo-labels could robustly extract the spatial distribution information of rice. Generally, this study could provide new options for applying a deep semantic segmentation model to training sample shortage regions. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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22 pages, 7245 KiB  
Article
Evolution Assessment of Mining Subsidence Characteristics Using SBAS and PS Interferometry in Sanshandao Gold Mine, China
by Jia Liu, Fengshan Ma, Guang Li, Jie Guo, Yang Wan and Yewei Song
Remote Sens. 2022, 14(2), 290; https://doi.org/10.3390/rs14020290 - 9 Jan 2022
Cited by 18 | Viewed by 3453
Abstract
Ground subsidence is a common geological phenomenon occurring in mining areas. As an important Chinese gold mine, Sanshandao Gold Mine has a mining history of 25 years, with remarkable ground subsidence deformation. Mining development, life security, property security and ecological protection all require [...] Read more.
Ground subsidence is a common geological phenomenon occurring in mining areas. As an important Chinese gold mine, Sanshandao Gold Mine has a mining history of 25 years, with remarkable ground subsidence deformation. Mining development, life security, property security and ecological protection all require comprehension of the ground subsidence characteristics and evolution in the mining area. In this study, the mining subsidence phenomenon of the Sanshandao Gold Mine was investigated and analyzed based on Persistent Scatterer Interferometry (PSI) and small baseline subset (SBAS). The SAR (synthetic aperture radar) images covering the study area were acquired by the Sentinel-1A satellite between 2018 and 2021; 54 images (between 22 February 2018 and 25 May 2021) were processed using the PSI technique and 24 images (between 11 April 2018 and 12 July 2021) were processed using the SBAS technique. In addition, GACOS (generic atmospheric correction online service) data were adopted to eliminate the atmospheric error in both kinds of data processing. The interferometric synthetic aperture radar (InSAR) results showed a basically consistent subsidence area and a similar subsidence pattern. Both InSAR results indicated that the maximum LOS (line of sight) subsidence velocity is about 49 mm/year. The main subsidence zone is situated in the main mining area, extending in the northwest and southeast directions. According to the subsidence displacement of several representative sites in the mining area, we found that the PSI result has a higher subsidence displacement value compared to the SBAS result. Mining activities were accompanied by ground subsidence in the mining area: the ground subsidence phenomenon is exacerbated by the increasing mining quantity. Temporally, the mining subsidence lags behind the increase in mining quantity by about three months. In summary, the mining area has varying degrees of ground subsidence, monitored by two reliable time-series InSAR techniques. Further study of the subsidence mechanism is necessary to forecast ground subsidence and instruct mining activities. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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21 pages, 11211 KiB  
Article
Infrared and Visible Image Fusion Based on Co-Occurrence Analysis Shearlet Transform
by Biao Qi, Longxu Jin, Guoning Li, Yu Zhang, Qiang Li, Guoling Bi and Wenhua Wang
Remote Sens. 2022, 14(2), 283; https://doi.org/10.3390/rs14020283 - 8 Jan 2022
Cited by 11 | Viewed by 2466
Abstract
This study based on co-occurrence analysis shearlet transform (CAST) effectively combines the latent low rank representation (LatLRR) and the regularization of zero-crossing counting in differences to fuse the heterogeneous images. First, the source images are decomposed by CAST method into base-layer and detail-layer [...] Read more.
This study based on co-occurrence analysis shearlet transform (CAST) effectively combines the latent low rank representation (LatLRR) and the regularization of zero-crossing counting in differences to fuse the heterogeneous images. First, the source images are decomposed by CAST method into base-layer and detail-layer sub-images. Secondly, for the base-layer components with larger-scale intensity variation, the LatLRR, is a valid method to extract the salient information from image sources, and can be applied to generate saliency map to implement the weighted fusion of base-layer images adaptively. Meanwhile, the regularization term of zero crossings in differences, which is a classic method of optimization, is designed as the regularization term to construct the fusion of detail-layer images. By this method, the gradient information concealed in the source images can be extracted as much as possible, then the fusion image owns more abundant edge information. Compared with other state-of-the-art algorithms on publicly available datasets, the quantitative and qualitative analysis of experimental results demonstrate that the proposed method outperformed in enhancing the contrast and achieving close fusion result. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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28 pages, 10556 KiB  
Article
Mosaicking Weather Radar Retrievals from an Operational Heterogeneous Network at C and X Band for Precipitation Monitoring in Italian Central Apennines
by Stefano Barbieri, Saverio Di Fabio, Raffaele Lidori, Francesco L. Rossi, Frank S. Marzano and Errico Picciotti
Remote Sens. 2022, 14(2), 248; https://doi.org/10.3390/rs14020248 - 6 Jan 2022
Cited by 5 | Viewed by 3153
Abstract
Meteorological radar networks are suited to remotely provide atmospheric precipitation retrieval over a wide geographic area for severe weather monitoring and near-real-time nowcasting. However, blockage due to buildings, hills, and mountains can hamper the potential of an operational weather radar system. The Abruzzo [...] Read more.
Meteorological radar networks are suited to remotely provide atmospheric precipitation retrieval over a wide geographic area for severe weather monitoring and near-real-time nowcasting. However, blockage due to buildings, hills, and mountains can hamper the potential of an operational weather radar system. The Abruzzo region in central Italy’s Apennines, whose hydro-geological risks are further enhanced by its complex orography, is monitored by a heterogeneous system of three microwave radars at the C and X bands with different features. This work shows a systematic intercomparison of operational radar mosaicking methods, based on bi-dimensional rainfall products and dealing with both C and X bands as well as single- and dual-polarization systems. The considered mosaicking methods can take into account spatial radar-gauge adjustment as well as different spatial combination approaches. A data set of 16 precipitation events during the years 2018–2020 in the central Apennines is collected (with a total number of 32,750 samples) to show the potentials and limitations of the considered operational mosaicking approaches, using a geospatially-interpolated dense network of regional rain gauges as a benchmark. Results show that the radar-network pattern mosaicking, based on the anisotropic radar-gauge adjustment and spatial averaging of composite data, is better than the conventional maximum-value merging approach. The overall analysis confirms that heterogeneous weather radar mosaicking can overcome the issues of single-frequency fixed radars in mountainous areas, guaranteeing a better spatial coverage and a more uniform rainfall estimation accuracy over the area of interest. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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24 pages, 7008 KiB  
Article
A Novel Real-Time Echo Separation Processing Architecture for Space–Time Waveform-Encoding SAR Based on Elevation Digital Beamforming
by Jinsong Qiu, Zhimin Zhang, Zhen Chen, Shuo Han, Wei Wang, Yuhao Wen, Xiangrui Meng and Huaitao Fan
Remote Sens. 2022, 14(1), 213; https://doi.org/10.3390/rs14010213 - 4 Jan 2022
Cited by 7 | Viewed by 2689
Abstract
Space–time waveform-encoding (STWE) SAR can receive echoes from multiple sub-swaths simultaneously with a single receive window. The echoes overlap each other in the time domain. To separate the echoes from different directions, traditional schemes adapt single-null steering techniques for digital receive beam patterns. [...] Read more.
Space–time waveform-encoding (STWE) SAR can receive echoes from multiple sub-swaths simultaneously with a single receive window. The echoes overlap each other in the time domain. To separate the echoes from different directions, traditional schemes adapt single-null steering techniques for digital receive beam patterns. However, the problems of spaceborne DBF-SAR, in practice, such as null extension loss, terrain undulation, elevation angle of arrival extension, and spaceborne antenna beam control, make the conventional scheme unable to effectively separate the echoes from different sub-swaths, which overlap each other in the time domain.A novel multi-null constrained echo separation scheme is proposed to overcome the shortcomings of the conventional scheme. The proposed algorithm can flexibly adjust the width of the notch to track the time-varying pulse extension angle with less resource consumption. Moreover, the hardware implementation details of the corresponding real-time processing architecture are discussed. The two-dimensional simulation results indicate that the proposed scheme can effectively improve the performance of echo separation. The effectiveness of the proposed method is verified by raw data processing instance of an X-band 16-channel DBF-SAR airborne system. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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10 pages, 4503 KiB  
Communication
SAR Target Detection Based on Improved SSD with Saliency Map and Residual Network
by Fang Zhou, Fengjie He, Changchun Gui, Zhangyu Dong and Mengdao Xing
Remote Sens. 2022, 14(1), 180; https://doi.org/10.3390/rs14010180 - 1 Jan 2022
Cited by 10 | Viewed by 2559
Abstract
A target detection method based on an improved single shot multibox detector (SSD) is proposed to solve insufficient training samples for synthetic aperture radar (SAR) target detection. We propose two strategies to improve the SSD: model structure optimization and small sample augmentation. For [...] Read more.
A target detection method based on an improved single shot multibox detector (SSD) is proposed to solve insufficient training samples for synthetic aperture radar (SAR) target detection. We propose two strategies to improve the SSD: model structure optimization and small sample augmentation. For model structure optimization, the first approach is to extract deep features of the target with residual networks instead of with VGGNet. Then, the aspect ratios of the default boxes are redesigned to match the different targets’ sizes. For small sample augmentation, besides the routine image processing methods, such as rotating, translating, and mirroring, enough training samples are obtained based on the saliency map theory in machine vision. Lastly, a simulated SAR image dataset called Geometric Objects (GO) is constructed, which contains dihedral angles, surface plates and cylinders. The experimental results on the GO-simulated image dataset and the MSTAR real image dataset demonstrate that the proposed method has better performance in SAR target detection than other detection methods. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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26 pages, 11039 KiB  
Article
Improving Leaf Area Index Retrieval Using Multi-Sensor Images and Stacking Learning in Subtropical Forests of China
by Yang Chen, Lixia Ma, Dongsheng Yu, Kaiyue Feng, Xin Wang and Jie Song
Remote Sens. 2022, 14(1), 148; https://doi.org/10.3390/rs14010148 - 30 Dec 2021
Cited by 9 | Viewed by 3188
Abstract
The leaf area index (LAI) is a key indicator of the status of forest ecosystems that is important for understanding global carbon and water cycles as well as terrestrial surface energy balances and the impacts of climate change. Machine learning (ML) methods offer [...] Read more.
The leaf area index (LAI) is a key indicator of the status of forest ecosystems that is important for understanding global carbon and water cycles as well as terrestrial surface energy balances and the impacts of climate change. Machine learning (ML) methods offer promising ways of generating spatially explicit LAI data covering large regions based on optical images. However, there have been few efforts to analyze the LAI in heterogeneous subtropical forests with complex terrain by fusing high-resolution multi-sensor data from the Sentinel-1 Synthetic Aperture Radar (SAR), Sentinel-2 Multi Spectral Instrument (MSI), and Advanced Land Observing Satellite-1 digital elevation model (DEM). Here, forest LAI mapping was performed by integrating the MSI, SAR, and DEM data using a stacking learning (SL) approach that incorporates distinct predictions from a set of optimized individual ML algorithms. The method’s performance was evaluated by comparison to field forest LAI measurements acquired in Xingguo and Gandong of subtropical China. The results showed that the addition of the SAR and DEM images using the SL model compared to the inputs of only optical images reduced the mean absolute error (MAE) and root mean square error (RMSE) by 26% and 18%, respectively, in Xingguo, and by 12% and 8%, respectively, in Gandong. Furthermore, the combination of all images had the best prediction performance. SL was found to be more robust and accurate than conventional individual ML models, while the MAE and RMSE were decreased by 71% and 64%, respectively, in Xingguo, and by 68% and 59%, respectively, in Gandong. Therefore, the SL model using the three-source data combination produced satisfied prediction accuracy with the coefficients of determination (R2), MAE, and RMSE of 0.96, 0.17, and 0.28, respectively, in Xingguo and 0.94, 0.30, and 0.47, respectively, in Gandong. This study revealed the potential of the SL algorithm for retrieving the forest LAI using multi-sensor data in areas with complex terrain. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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19 pages, 4581 KiB  
Article
A Comparative Study of Interannual Oscillation Models for Determining Geophysical Polar Motion Excitations
by Małgorzata Wińska
Remote Sens. 2022, 14(1), 147; https://doi.org/10.3390/rs14010147 - 30 Dec 2021
Cited by 3 | Viewed by 3462
Abstract
Similar to seasonal and intraseasonal variations in polar motion (PM), interannual variations are also largely caused by changes in the angular momentum of the Earth’s geophysical fluid layers composed of the atmosphere, the oceans, and in-land hydrologic flows (AOH). Not only are inland [...] Read more.
Similar to seasonal and intraseasonal variations in polar motion (PM), interannual variations are also largely caused by changes in the angular momentum of the Earth’s geophysical fluid layers composed of the atmosphere, the oceans, and in-land hydrologic flows (AOH). Not only are inland freshwater systems crucial for interannual PM fluctuations, but so are atmospheric surface pressures and winds, oceanic currents, and ocean bottom pressures. However, the relationship between observed geodetic PM excitations and hydro-atmospheric models has not yet been determined. This is due to defects in geophysical models and the partial knowledge of atmosphere–ocean coupling and hydrological processes. Therefore, this study provides an analysis of the fluctuations of PM excitations for equatorial geophysical components χ1 and χ2 at interannual time scales. The geophysical excitations were determined from different sources, including atmospheric, ocean models, Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On data, as well as from the Land Surface Discharge Model. The Multi Singular Spectrum Analysis method was applied to retain interannual variations in χ1 and χ2 components. None of the considered mass and motion terms studied for the different atmospheric and ocean models were found to have a negligible effect on interannual PM. These variables, derived from different Atmospheric Angular Momentum (AAM) and Oceanic Angular Momentum (OAM) models, differ from each other. Adding hydrologic considerations to the coupling of AAM and OAM excitations was found to provide benefits for achieving more consistent interannual geodetic budgets, but none of the AOH combinations fully explained the total observed PM excitations. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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17 pages, 6619 KiB  
Article
High-Precision Digital Surface Model Extraction from Satellite Stereo Images Fused with ICESat-2 Data
by Jiang Ye, Yuxuan Qiang, Rui Zhang, Xinguo Liu, Yixin Deng and Jiawei Zhang
Remote Sens. 2022, 14(1), 142; https://doi.org/10.3390/rs14010142 - 29 Dec 2021
Cited by 15 | Viewed by 4035
Abstract
The lack of ground control points (GCPs) affects the elevation accuracy of digital surface models (DSMs) generated by optical satellite stereo images and limits the application of high-resolution DSMs. It is a feasible idea to use ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) [...] Read more.
The lack of ground control points (GCPs) affects the elevation accuracy of digital surface models (DSMs) generated by optical satellite stereo images and limits the application of high-resolution DSMs. It is a feasible idea to use ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) laser altimetry data to improve the elevation accuracy of optical stereo images, but it is necessary to accurately match the two types of data. This paper proposes a DSM registration strategy based on terrain similarity (BOTS), which integrates ICESat-2 laser altimetry data without GCPs and improves the DSM elevation accuracy generation from optical satellite stereo pairs. Under different terrain conditions, Worldview-2, SV-1, GF-7, and ZY-3 stereo pairs were used to verify the effectiveness of this method. The experimental results show that the BOTS method proposed in this paper is more robust when there are a large number of abnormal points in the ICESat-2 data or there is a large elevation gap between DSMs. After fusion of ICESat-2 data, the DSM elevation accuracy extracted from the satellite stereo pair is improved by 73~92%, and the root mean square error (RMSE) of Worldview-2 DSM reaches 0.71 m. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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29 pages, 7791 KiB  
Article
Hyperspectral Pansharpening in the Reflective Domain with a Second Panchromatic Channel in the SWIR II Spectral Domain
by Yohann Constans, Sophie Fabre, Michael Seymour, Vincent Crombez, Yannick Deville and Xavier Briottet
Remote Sens. 2022, 14(1), 113; https://doi.org/10.3390/rs14010113 - 28 Dec 2021
Cited by 4 | Viewed by 2221
Abstract
Hyperspectral pansharpening methods in the reflective domain are limited by the large difference between the visible panchromatic (PAN) and hyperspectral (HS) spectral ranges, which notably leads to poor representation of the SWIR (1.0–2.5 μm) spectral domain. A novel instrument concept is proposed in [...] Read more.
Hyperspectral pansharpening methods in the reflective domain are limited by the large difference between the visible panchromatic (PAN) and hyperspectral (HS) spectral ranges, which notably leads to poor representation of the SWIR (1.0–2.5 μm) spectral domain. A novel instrument concept is proposed in this study, by introducing a second PAN channel in the SWIR II (2.0–2.5 μm) spectral domain. Two extended fusion methods are proposed to process both PAN channels, namely, Gain-2P and CONDOR-2P: the first one is an extended version of the Brovey transform, whereas the second one adds mixed pixel preprocessing steps to Gain-2P. By following an exhaustive performance-assessment protocol including global, refined, and local numerical analyses supplemented by supervised classification, we evaluated the updated methods on peri-urban and urban datasets. The results confirm the significant contribution of the second PAN channel (up to 45% of improvement for both datasets with the mean normalised gap in the reflective domain and 60% in the SWIR domain only) and reveal a clear advantage for CONDOR-2P (as compared with Gain-2P) regarding the peri-urban dataset. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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14 pages, 1219 KiB  
Article
A Truncated Matched Filter Method for Interrupted Sampling Repeater Jamming Suppression Based on Jamming Reconstruction
by Lu Lu and Meiguo Gao
Remote Sens. 2022, 14(1), 97; https://doi.org/10.3390/rs14010097 - 25 Dec 2021
Cited by 20 | Viewed by 3196
Abstract
Interrupted sampling repeater jamming (ISRJ) is becoming more widely used in electronic countermeasures (ECM), thanks to the development of digital radio frequency memory (DRFM). Radar electronic counter-countermeasure (ECCM) is much more difficult when the jamming signal is coherent with the emitted signal. Due [...] Read more.
Interrupted sampling repeater jamming (ISRJ) is becoming more widely used in electronic countermeasures (ECM), thanks to the development of digital radio frequency memory (DRFM). Radar electronic counter-countermeasure (ECCM) is much more difficult when the jamming signal is coherent with the emitted signal. Due to the intermittent transmission feature of ISRJ, the energy accumulation of jamming on the matched filter shows a ‘ladder’ characteristic, whereas the real target signal is continuous. As a consequence, the time delay and distribution of the jamming slice can be obtained based on searching the truncated-matched-filter (TMF) matrix. That is composed of pulse compression (PC) results under matched filters with different lengths. Based on the above theory, this paper proposes a truncated matched filter method by the reconstruction of jamming slices to suppress ISRJ of linear frequency modulation (LFM) radars. The numerical simulations indicate the effectiveness of the proposed method and validate the theoretical analysis. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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20 pages, 7887 KiB  
Article
A Novel Flickering Multi-Target Joint Detection Method Based on a Biological Memory Model
by Qian Zhang, Weibo Huo, Jifang Pei, Yongchao Zhang, Jianyu Yang and Yulin Huang
Remote Sens. 2022, 14(1), 39; https://doi.org/10.3390/rs14010039 - 23 Dec 2021
Viewed by 2768
Abstract
The robust target detection ability of marine navigation radars is essential for safe shipping. However, time-varying river and sea surfaces will induce target scattering changes, known as fluctuating characteristics. Moreover, the targets exhibiting stronger fluctuation disappear in some frames of the radar images, [...] Read more.
The robust target detection ability of marine navigation radars is essential for safe shipping. However, time-varying river and sea surfaces will induce target scattering changes, known as fluctuating characteristics. Moreover, the targets exhibiting stronger fluctuation disappear in some frames of the radar images, which is known as flickering characteristics. This phenomenon causes a severe decline in the detection performance of traditional detection methods. A biological memory model-based dynamic programming multi-target joint detection method was proposed to address this issue in this paper. Firstly, a global detection operator is used to discretize the multi-target state into multiple single-target states, achieving the discretization of numerous targets. Meanwhile, updating the formula of the memory weight merit function can strengthen the joint frame correlation of the flickering characteristics target. The progressive loop integral is utilized to update the target states to optimize the candidate target set. Finally, a two-stage threshold criterion is utilized to detect the target at different amplitude levels accurately. Simulation and experimental results are given to validate the assertion that the detection performance of the proposed method is greatly improved under a low SCR of 3-8 dB for multiple flickering target detection. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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14 pages, 1036 KiB  
Technical Note
An Improved Phase-Derived Range Method Based on High-Order Multi-Frame Track-Before-Detect for Warhead Detection
by Nannan Zhu, Shiyou Xu, Congduan Li, Jun Hu, Xinlan Fan, Wenzhen Wu and Zengping Chen
Remote Sens. 2022, 14(1), 29; https://doi.org/10.3390/rs14010029 - 22 Dec 2021
Cited by 4 | Viewed by 3003
Abstract
It is crucial for a ballistic missile defense system to discriminate the true warhead from decoys. Although a decoy has a similar shape to the warhead, it is believed that the true warhead can be separated by its micro-Doppler features introduced by the [...] Read more.
It is crucial for a ballistic missile defense system to discriminate the true warhead from decoys. Although a decoy has a similar shape to the warhead, it is believed that the true warhead can be separated by its micro-Doppler features introduced by the precession and nutation. As is well known, the accuracy of the phase-derived range method, to extract micro-Doppler curves, can reach sub-wavelength. However, it suffers from an inefficiency of energy integration and high computational costs. In this paper, a novel phase-derived range method, using high-order multi-frame track-before-detect is proposed for micro-Doppler curve extraction under a low signal-to-noise ratio (SNR). First, the sinusoidal micro-Doppler range sequence is treated as the state, and the dynamic model is described as a Markov chain to obtain the envelopes and then the ambiguous phases. Instead of processing the whole frames, the proposed method only processes the latest frame at an arbitrary given time, which reduces the computational costs. Then, the correlation of all pairs of adjacent pulses is calculated along the slow time dimension to find the number of cells that the point scatterer crosses, which can be further used in phase unwrapping. Finally, the phase-derived range method is employed to get the micro-Doppler curves. Simulation results show that the proposed method is capable of extracting the micro-Doppler curves with sub-wavelength accuracy, even if SNR = −15 dB, with a lower computational cost. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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20 pages, 1121 KiB  
Article
Forward-Looking Super-Resolution Imaging for Sea-Surface Target with Multi-Prior Bayesian Method
by Weixin Li, Ming Li, Lei Zuo, Hao Sun, Hongmeng Chen and Yachao Li
Remote Sens. 2022, 14(1), 26; https://doi.org/10.3390/rs14010026 - 22 Dec 2021
Cited by 13 | Viewed by 2736
Abstract
Traditional forward-looking super-resolution methods mainly concentrate on enhancing the resolution with ground clutter or no clutter scenes. However, sea clutter exists in the sea-surface target imaging, as well as ground clutter when the imaging scene is a seacoast.Meanwhile, restoring the contour information of [...] Read more.
Traditional forward-looking super-resolution methods mainly concentrate on enhancing the resolution with ground clutter or no clutter scenes. However, sea clutter exists in the sea-surface target imaging, as well as ground clutter when the imaging scene is a seacoast.Meanwhile, restoring the contour information of the target has an important effect, for example, in the autonomous landing on a ship. This paper aims to realize the forward-looking imaging of a sea-surface target. In this paper, a multi-prior Bayesian method, which considers the environment and fuses the contour information and the sparsity of the sea-surface target, is proposed. Firstly, due to the imaging environment in which more than one kind of clutter exists, we introduce the Gaussian mixture model (GMM) as the prior information to describe the interference of the clutter and noise. Secondly, we fuse the total variation (TV) prior and Laplace prior, and propose a multi-prior to model the contour information and sparsity of the target. Third, we introduce the latent variable to simplify the logarithm likelihood function. Finally, to solve the optimal parameters, the maximum posterior-expectation maximization (MAP-EM) method is utilized. Experimental results illustrate that the multi-prior Bayesian method can enhance the azimuth resolution, and preserve the contour information of the sea-surface target. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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16 pages, 28707 KiB  
Article
Electromagnetic Scattering of Near-Field Turbulent Wake Generated by Accelerated Propeller
by Yuxin Deng, Min Zhang, Wangqiang Jiang and Letian Wang
Remote Sens. 2021, 13(24), 5178; https://doi.org/10.3390/rs13245178 - 20 Dec 2021
Cited by 3 | Viewed by 2882
Abstract
The electromagnetic scattering study of the turbulent wake of a moving ship has important application value in target recognition and tracking. However, to date, there has been insufficient research into the electromagnetic characteristics of near-field propeller turbulence. This study presents a new procedure [...] Read more.
The electromagnetic scattering study of the turbulent wake of a moving ship has important application value in target recognition and tracking. However, to date, there has been insufficient research into the electromagnetic characteristics of near-field propeller turbulence. This study presents a new procedure for evaluating the electromagnetic scattering coefficient and imaging characteristics of turbulent wakes in the near field. By controlling the different values of the net momenta, a turbulent wake was generated using the large-eddy simulation method. The results show that the net momentum transferred to the background flow field determines the development of the turbulent wake, which explains the formation mechanism of the turbulence. Combined with the turbulent energy attenuation spectrum, the electromagnetic scattering characteristics of the turbulent wake were calculated using the two-scale facet mode. Using this method, the impact of different parameters on the scattering coefficient and the electromagnetic image of the turbulence wake were investigated, to explain the modulation mechanism and electromagnetic imaging characteristics of the near-field turbulent wake. Moreover, an application for estimating a ship’s heading is proposed based on the electromagnetic imaging characteristics of the turbulent wake. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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23 pages, 21519 KiB  
Article
Analysis on Land-Use Change and Its Driving Mechanism in Xilingol, China, during 2000–2020 Using the Google Earth Engine
by Junzhi Ye, Yunfeng Hu, Lin Zhen, Hao Wang and Yuxin Zhang
Remote Sens. 2021, 13(24), 5134; https://doi.org/10.3390/rs13245134 - 17 Dec 2021
Cited by 32 | Viewed by 4850
Abstract
Large-scale, long time-series, and high-precision land-use mapping is the basis for assessing the evolution and sustainability of ecosystems in Xilingol, the Inner Mongolia Autonomous Region, China. Based on Google Earth Engine (GEE) and Landsat satellite remote-sensing images, the random forest (RF) classification algorithm [...] Read more.
Large-scale, long time-series, and high-precision land-use mapping is the basis for assessing the evolution and sustainability of ecosystems in Xilingol, the Inner Mongolia Autonomous Region, China. Based on Google Earth Engine (GEE) and Landsat satellite remote-sensing images, the random forest (RF) classification algorithm was applied to create a yearly land-use/land-cover change (LULC) dataset in Xilingol during the past 20 years (2000–2020) and to examine the spatiotemporal characteristics, dynamic changes, and driving mechanisms of LULC using principal component analysis and multiple linear stepwise regression methods. The main findings are summarized as follows. (1) The RF classification algorithm supported by the GEE platform enables fast and accurate acquisition of the LULC dataset, and the overall accuracy is 0.88 ± 0.01. (2) The ecological condition across Xilingol has improved significantly in the last 20 years (2000–2020), and the area of vegetation (grassland and woodland) has increased. Specifically, the area of high-coverage grass and woodland increases (+13.26%, +1.19%), while the area of water and moderate- and low-coverage grass decreases (−15.96%, −7.23%, and −3.27%). Cropland increases first and then decreases (−34.85%) and is mainly distributed in the southeast. The area of deserted land decreases in the south and increases in the center and north, but the total area still decreases (−13.74%). The built-up land expands rapidly (+108.45%). (3) In addition, our results suggest that regional socioeconomic development factors are the primary causes of changes in built-up land, and climate-related factors are the primary causes of water changes, but the correlations between other land-use types and relevant factors are not significant (cropland and grassland). We conclude that the GEE+RF method is capable of automated, long time-series, and high-accuracy land-use mapping, and further changes in climatic, environmental, and socioeconomic development factors, i.e., climate warming and rotational grazing, might have significant implications on regional land surface morphology and landscape dynamics. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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29 pages, 22377 KiB  
Article
An Elevation Ambiguity Resolution Method Based on Segmentation and Reorganization of TomoSAR Point Cloud in 3D Mountain Reconstruction
by Xiaowan Li, Fubo Zhang, Yanlei Li, Qichang Guo, Yangliang Wan, Xiangxi Bu, Yunlong Liu and Xingdong Liang
Remote Sens. 2021, 13(24), 5118; https://doi.org/10.3390/rs13245118 - 16 Dec 2021
Cited by 11 | Viewed by 3590
Abstract
Tomographic Synthetic Aperture Radar (TomoSAR) is a breakthrough of the traditional SAR, which has the three-dimentional (3D) observation ability of layover scenes such as buildings and high mountains. As an advanced system, the airborne array TomoSAR can effectively avoid temporal de-correlation caused by [...] Read more.
Tomographic Synthetic Aperture Radar (TomoSAR) is a breakthrough of the traditional SAR, which has the three-dimentional (3D) observation ability of layover scenes such as buildings and high mountains. As an advanced system, the airborne array TomoSAR can effectively avoid temporal de-correlation caused by long revisit time, which has great application in high-precision mountain surveying and mapping. The 3D reconstruction using TomoSAR has mainly focused on low targets, while there are few literatures on 3D mountain reconstruction. Due to the layover phenomenon, surveying in high mountain areas remains a difficult task. Consequently, it is meaningful to carry out the research on 3D mountain reconstruction using the airborne array TomoSAR. However, the original TomoSAR mountain point cloud faces the problem of elevation ambiguity. Furthermore, for mountains with complex terrain, the points located in different elevation periods may intersect. This phenomenon increases the difficulty of solving the problem. In this paper, a novel elevation ambiguity resolution method is proposed. First, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Gaussian Mixture Model (GMM) are combined for point cloud segmentation. The former ensures coarse segmentation based on density, and the latter allows fine segmentation of the abnormal categories caused by intersection. Subsequently, the segmentation results are reorganized in the elevation direction to reconstruct all possible point clouds. Finally, the real point cloud can be extracted automatically under the constraints of the boundary and elevation continuity. The performance of the proposed method is demonstrated by simulations and experiments. Based on the airborne array TomoSAR experiment in Leshan City, Sichuan Province, China in 2019, the 3D model of the surveyed mountain is presented. Moreover, three kinds of external data are applied to fully verify the validity of this method. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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20 pages, 63268 KiB  
Article
Karst Collapse Risk Zonation and Evaluation in Wuhan, China Based on Analytic Hierarchy Process, Logistic Regression, and InSAR Angular Distortion Approaches
by Jiyuan Hu, Mahdi Motagh, Jiayao Wang, Fen Qin, Jianchen Zhang, Wenhao Wu and Yakun Han
Remote Sens. 2021, 13(24), 5063; https://doi.org/10.3390/rs13245063 - 14 Dec 2021
Cited by 11 | Viewed by 3714
Abstract
The current study presents a detailed assessment of risk zones related to karst collapse in Wuhan by analytical hierarchy process (AHP) and logistic regression (LR) models. The results showed that the LR model was more accurate with an area under the receiver operating [...] Read more.
The current study presents a detailed assessment of risk zones related to karst collapse in Wuhan by analytical hierarchy process (AHP) and logistic regression (LR) models. The results showed that the LR model was more accurate with an area under the receiver operating characteristic (ROC) curve of 0.911 compared to 0.812 derived from the AHP model. Both models performed well in identifying high-risk zones with only a 3% discrepancy in area. However, for the medium- and low-risk classes, although the spatial distribution of risk zoning results were similar between two approaches, the spatial extent of the risk areas varied between final models. The reliability of both methods were reduced significantly by excluding the InSAR-based ground subsidence map from the analysis, with the karst collapse presence falling into the high-risk zone being reduced by approximately 14%, and karst collapse absence falling into the karst area being increased by approximately 6.5% on the training samples. To evaluate the practicality of using only results from ground subsidence maps for the risk zonation, the results of AHP and LR are compared with a weighted angular distortion (WAD) method for karst risk zoning in Wuhan. We find that the areas with relatively large subsidence horizontal gradient values within the karst belts are generally spatially consistent with high-risk class areas identified by the AHP- and LR-based approaches. However, the WAD-based approach cannot be used alone as an ideal karst collapse risk assessment model as it does not include geological and natural factors into the risk zonation. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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21 pages, 15027 KiB  
Article
Multimodal Data and Multiscale Kernel-Based Multistream CNN for Fine Classification of a Complex Surface-Mined Area
by Mingjie Qian, Song Sun and Xianju Li
Remote Sens. 2021, 13(24), 5052; https://doi.org/10.3390/rs13245052 - 13 Dec 2021
Cited by 7 | Viewed by 2929
Abstract
Fine land cover classification (FLCC) of complex landscapes is a popular and challenging task in the remote sensing community. In complex surface-mined areas (CSMAs), researchers have conducted FLCC using traditional machine learning methods and deep learning algorithms. However, convolutional neural network (CNN) algorithms [...] Read more.
Fine land cover classification (FLCC) of complex landscapes is a popular and challenging task in the remote sensing community. In complex surface-mined areas (CSMAs), researchers have conducted FLCC using traditional machine learning methods and deep learning algorithms. However, convolutional neural network (CNN) algorithms that may be useful for FLCC of CSMAs have not been fully investigated. This study proposes a multimodal remote sensing data and multiscale kernel-based multistream CNN (3M-CNN) model. Experiments based on two ZiYuan-3 (ZY-3) satellite imageries of different times and seasons were conducted in Wuhan, China. The 3M-CNN model had three main features: (1) multimodal data-based multistream CNNs, i.e., using ZY-3 imagery-derived true color, false color, and digital elevation model data to form three CNNs; (2) multisize neighbors, i.e., using different neighbors of optical and topographic data as inputs; and (3) multiscale convolution flows revised from an inception module for optical and topographic data. Results showed that the proposed 3M-CNN model achieved excellent overall accuracies on two different images, and outperformed other comparative models. In particular, the 3M-CNN model yielded obvious better visual performances. In general, the proposed process was beneficial for the FLCC of complex landscape areas. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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20 pages, 24441 KiB  
Article
Disparity Estimation of High-Resolution Remote Sensing Images with Dual-Scale Matching Network
by Sheng He, Ruqin Zhou, Shenhong Li, San Jiang and Wanshou Jiang
Remote Sens. 2021, 13(24), 5050; https://doi.org/10.3390/rs13245050 - 13 Dec 2021
Cited by 16 | Viewed by 6623
Abstract
As an essential task in remote sensing, disparity estimation of high-resolution stereo images is still confronted with intractable problems due to extremely complex scenes and dynamically changing disparities. Especially in areas containing texture-less regions, repetitive patterns, disparity discontinuities, and occlusions, stereo matching is [...] Read more.
As an essential task in remote sensing, disparity estimation of high-resolution stereo images is still confronted with intractable problems due to extremely complex scenes and dynamically changing disparities. Especially in areas containing texture-less regions, repetitive patterns, disparity discontinuities, and occlusions, stereo matching is difficult. Recently, convolutional neural networks have provided a new paradigm for disparity estimation, but it is difficult for current models to consider both accuracy and speed. This paper proposes a novel end-to-end network to overcome the aforementioned obstacles. The proposed network learns stereo matching at dual scales, in which the low one captures coarse-grained information while the high one captures fine-grained information, helpful for matching structures of different scales. Moreiver, we construct cost volumes from negative to positive values to make the network work well for both negative and nonnegative disparities since the disparity varies dramatically in remote sensing stereo images. A 3D encoder-decoder module formed by factorized 3D convolutions is introduced to adaptively learn cost aggregation, which is of high efficiency and able to alleviate the edge-fattening issue at disparity discontinuities and approximate the matching of occlusions. Besides, we use a refinement module that brings in shallow features as guidance to attain high-quality full-resolution disparity maps. The proposed network is compared with several typical models. Experimental results on a challenging dataset demonstrate that our network shows powerful learning and generalization abilities. It achieves convincing performance on both accuracy and efficiency, and improvements of stereo matching in these challenging areas are noteworthy. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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21 pages, 4347 KiB  
Article
At-Sensor Radiometric Correction of a Multispectral Camera (RedEdge) for sUAS Vegetation Mapping
by Cuizhen Wang
Sensors 2021, 21(24), 8224; https://doi.org/10.3390/s21248224 - 9 Dec 2021
Cited by 17 | Viewed by 3438
Abstract
Rapid advancement of drone technology enables small unmanned aircraft systems (sUAS) for quantitative applications in public and private sectors. The drone-mounted 5-band MicaSense RedEdge cameras, for example, have been popularly adopted in the agroindustry for assessment of crop healthiness. The camera extracts surface [...] Read more.
Rapid advancement of drone technology enables small unmanned aircraft systems (sUAS) for quantitative applications in public and private sectors. The drone-mounted 5-band MicaSense RedEdge cameras, for example, have been popularly adopted in the agroindustry for assessment of crop healthiness. The camera extracts surface reflectance by referring to a pre-calibrated reflectance panel (CRP). This study tests the performance of a Matrace100/RedEdge-M camera in extracting surface reflectance orthoimages. Exploring multiple flights and field experiments, an at-sensor radiometric correction model was developed that integrated the default CRP and a Downwelling Light Sensor (DLS). Results at three vegetated sites reveal that the current CRP-only RedEdge-M correction procedure works fine except the NIR band, and the performance is less stable on cloudy days affected by sun diurnal, weather, and ground variations. The proposed radiometric correction model effectively reduces these local impacts to the extracted surface reflectance. Results also reveal that the Normalized Difference Vegetation Index (NDVI) from the RedEdge orthoimage is prone to overestimation and saturation in vegetated fields. Taking advantage of the camera’s red edge band centered at 717 nm, this study proposes a red edge NDVI (ReNDVI). The non-vegetation can be easily excluded with ReNDVI < 0.1. For vegetation, the ReNDVI provides reasonable values in a wider histogram than NDVI. It could be better applied to assess vegetation healthiness across the site. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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26 pages, 4897 KiB  
Article
Joint Use of in-Scene Background Radiance Estimation and Optimal Estimation Methods for Quantifying Methane Emissions Using PRISMA Hyperspectral Satellite Data: Application to the Korpezhe Industrial Site
by Nicolas Nesme, Rodolphe Marion, Olivier Lezeaux, Stéphanie Doz, Claude Camy-Peyret and Pierre-Yves Foucher
Remote Sens. 2021, 13(24), 4992; https://doi.org/10.3390/rs13244992 - 8 Dec 2021
Cited by 7 | Viewed by 4313
Abstract
Methane (CH4) is one of the most contributing anthropogenic greenhouse gases (GHGs) in terms of global warming. Industry is one of the largest anthropogenic sources of methane, which are currently only roughly estimated. New satellite hyperspectral imagers, such as PRISMA, open [...] Read more.
Methane (CH4) is one of the most contributing anthropogenic greenhouse gases (GHGs) in terms of global warming. Industry is one of the largest anthropogenic sources of methane, which are currently only roughly estimated. New satellite hyperspectral imagers, such as PRISMA, open up daily temporal monitoring of industrial methane sources at a spatial resolution of 30 m. Here, we developed the Characterization of Effluents Leakages in Industrial Environment (CELINE) code to inverse images of the Korpezhe industrial site. In this code, the in-Scene Background Radiance (ISBR) method was combined with a standard Optimal Estimation (OE) approach. The ISBR-OE method avoids the use of a complete and time-consuming radiative transfer model. The ISBR-OEM developed here overcomes the underestimation issues of the linear method (LM) used in the literature for high concentration plumes and controls a posteriori uncertainty. For the Korpezhe site, using the ISBR-OEM instead of the LM -retrieved CH4 concentration map led to a bias correction on CH4 mass from 4 to 16% depending on the source strength. The most important CH4 source has an estimated flow rate ranging from 0.36 ± 0.3 kg·s−1 to 4 ± 1.76 kg·s−1 on nine dates. These local and variable sources contribute to the CH4 budget and can better constrain climate change models. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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25 pages, 6166 KiB  
Article
Adaptive Feature Weighted Fusion Nested U-Net with Discrete Wavelet Transform for Change Detection of High-Resolution Remote Sensing Images
by Congcong Wang, Wenbin Sun, Deqin Fan, Xiaoding Liu and Zhi Zhang
Remote Sens. 2021, 13(24), 4971; https://doi.org/10.3390/rs13244971 - 7 Dec 2021
Cited by 9 | Viewed by 3874
Abstract
The characteristics of a wide variety of scales about objects and complex texture features of high-resolution remote sensing images make deep learning-based change detection methods the mainstream method. However, existing deep learning methods have problems with spatial information loss and insufficient feature representation, [...] Read more.
The characteristics of a wide variety of scales about objects and complex texture features of high-resolution remote sensing images make deep learning-based change detection methods the mainstream method. However, existing deep learning methods have problems with spatial information loss and insufficient feature representation, resulting in unsatisfactory effects of small objects detection and boundary positioning in high-resolution remote sensing images change detection. To address the problems, a network architecture based on 2-dimensional discrete wavelet transform and adaptive feature weighted fusion is proposed. The proposed network takes Siamese network and Nested U-Net as the backbone; 2-dimensional discrete wavelet transform is used to replace the pooling layer; and the inverse transform is used to replace the upsampling to realize image reconstruction, reduce the loss of spatial information, and fully retain the original image information. In this way, the proposed network can accurately detect changed objects of different scales and reconstruct change maps with clear boundaries. Furthermore, different feature fusion methods of different stages are proposed to fully integrate multi-scale and multi-level features and improve the comprehensive representation ability of features, so as to achieve a more refined change detection effect while reducing pseudo-changes. To verify the effectiveness and advancement of the proposed method, it is compared with seven state-of-the-art methods on two datasets of Lebedev and SenseTime from the three aspects of quantitative analysis, qualitative analysis, and efficiency analysis, and the effectiveness of proposed modules is validated by an ablation study. The results of quantitative analysis and efficiency analysis show that, under the premise of taking into account the operation efficiency, our method can improve the recall while ensuring the detection precision, and realize the improvement of the overall detection performance. Specifically, it shows an average improvement of 37.9% and 12.35% on recall, and 34.76% and 11.88% on F1 with the Lebedev and SenseTime datasets, respectively, compared to other methods. The qualitative analysis shows that our method has better performance on small objects detection and boundary positioning than other methods, and a more refined change map can be obtained. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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16 pages, 4020 KiB  
Article
Swin-HSTPS: Research on Target Detection Algorithms for Multi-Source High-Resolution Remote Sensing Images
by Kun Fang, Jianquan Ouyang and Buwei Hu
Sensors 2021, 21(23), 8113; https://doi.org/10.3390/s21238113 - 4 Dec 2021
Cited by 6 | Viewed by 2647
Abstract
Traffic port stations are composed of buildings, infrastructure, and transportation vehicles. The target detection of traffic port stations in high-resolution remote sensing images needs to collect feature information of nearby small targets, comprehensively analyze and classify, and finally complete the traffic port station [...] Read more.
Traffic port stations are composed of buildings, infrastructure, and transportation vehicles. The target detection of traffic port stations in high-resolution remote sensing images needs to collect feature information of nearby small targets, comprehensively analyze and classify, and finally complete the traffic port station positioning. At present, deep learning methods based on convolutional neural networks have made great progress in single-target detection of high-resolution remote sensing images. How to show good adaptability to the recognition of multi-target complexes of high-resolution remote sensing images is a difficult point in the current remote sensing field. This paper constructs a novel high-resolution remote sensing image traffic port station detection model (Swin-HSTPS) to achieve high-resolution remote sensing image traffic port station detection (such as airports, ports) and improve the multi-target complex in high-resolution remote sensing images The recognition accuracy of high-resolution remote sensing images solves the problem of high-precision positioning by comprehensive analysis of the feature combination information of multiple small targets in high-resolution remote sensing images. The model combines the characteristics of the MixUp hybrid enhancement algorithm, and enhances the image feature information in the preprocessing stage. The PReLU activation function is added to the forward network of the Swin Transformer model network to construct a ResNet-like residual network and perform convolutional feature maps. Non-linear transformation strengthens the information interaction of each pixel block. This experiment evaluates the superiority of the model training by comparing the two indicators of average precision and average recall in the training phase. At the same time, in the prediction stage, the accuracy of the prediction target is measured by confidence. Experimental results show that the optimal average precision of the Swin-HSTPS reaches 85.3%, which is about 8% higher than the average precision of the Swin Transformer detection model. At the same time, the target prediction accuracy is also higher than the Swin Transformer detection model, which can accurately locate traffic port stations such as airports and ports in high-resolution remote sensing images. This model inherits the advantages of the Swin Transformer detection model, and is superior to mainstream models such as R-CNN and YOLOv5 in terms of the target prediction ability of high-resolution remote sensing image traffic port stations. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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18 pages, 5817 KiB  
Article
De-Noising of Magnetotelluric Signals by Discrete Wavelet Transform and SVD Decomposition
by Rui Zhou, Jiangtao Han, Zhenyu Guo and Tonglin Li
Remote Sens. 2021, 13(23), 4932; https://doi.org/10.3390/rs13234932 - 4 Dec 2021
Cited by 15 | Viewed by 2575
Abstract
Magnetotelluric (MT) sounding data can easily be damaged by various types of noise, especially in industrial areas, where the quality of measured data is poor. Most traditional de-noising methods are ineffective to the low signal-to-noise ratio of data. To solve the above problem, [...] Read more.
Magnetotelluric (MT) sounding data can easily be damaged by various types of noise, especially in industrial areas, where the quality of measured data is poor. Most traditional de-noising methods are ineffective to the low signal-to-noise ratio of data. To solve the above problem, we propose the use of a de-noising method for the detection of noise in MT data based on discrete wavelet transform and singular value decomposition (SVD), with multiscale dispersion entropy and phase space reconstruction carried out for pretreatment. No “over processing” takes place in the proposed method. Compared with wavelet transform and SVD decomposition in synthetic tests, the proposed method removes the profile of noise more completely, including large-scale noise and impulse noise. For high levels or low levels of noise, the proposed method can increase the signal-to-noise ratio of data more obviously. Moreover, application to the field MT data can prove the performance of the proposed method. The proposed method is a feasible method for the elimination of various noise types and can improve MT data with high noise levels, obtaining a recovery in the response. It can improve abrupt points and distortion in MT response curves more effectively than the robust method can. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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30 pages, 23106 KiB  
Article
Automatic Extraction of Indoor Structural Information from Point Clouds
by Dongyang Cheng, Junchao Zhang, Dangjun Zhao, Jianlai Chen and Di Tian
Remote Sens. 2021, 13(23), 4930; https://doi.org/10.3390/rs13234930 - 4 Dec 2021
Cited by 9 | Viewed by 2990
Abstract
We propose an innovative method with which to extract building interior structure information automatically, including ceiling, floor, and wall. Our approach outperforms previous methods in the following respects. First, we propose an approach based on principal component analysis (PCA) to find the ground [...] Read more.
We propose an innovative method with which to extract building interior structure information automatically, including ceiling, floor, and wall. Our approach outperforms previous methods in the following respects. First, we propose an approach based on principal component analysis (PCA) to find the ground plane, which is regarded as the new Cartesian plane. Second, to reduce the complexity of data processing, the data are projected into two dimensions and transformed into a binary image via the operation of an improved radius outlier removal (ROR) filter. Third, a traditional thinning algorithm is adopted to extract the image skeleton. Then, we propose a method for calculating slope through the nearest neighbor point. Moreover, the line is represented with the slopes to obtain information pertaining to the interior planes. Finally, the outline of the line is restored to a three-dimensional structure. The proposed method is evaluated in multiple scenarios, and the results show that the method is accurate (the maximum error of 0.03 m was in three scenarios) in indoor environments. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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20 pages, 6405 KiB  
Article
Sentinel-2 Time Series Analysis for Identification of Underutilized Land in Europe
by Carina Sobe, Manuela Hirschmugl and Andreas Wimmer
Remote Sens. 2021, 13(23), 4920; https://doi.org/10.3390/rs13234920 - 3 Dec 2021
Cited by 1 | Viewed by 2385
Abstract
Biomass and bioenergy play a central role in Europe’s Green Transition. Currently, biomass is representing half of the renewable energy sources used. While the role of renewables in the energy mix is undisputed, there have been many controversial discussions on the use of [...] Read more.
Biomass and bioenergy play a central role in Europe’s Green Transition. Currently, biomass is representing half of the renewable energy sources used. While the role of renewables in the energy mix is undisputed, there have been many controversial discussions on the use of biomass for energy due to the “food versus fuel” debate. Using previously underutilized lands for bioenergy is one possibility to prevent this discussion. This study supports the attempts to increase biomass for bioenergy through the provision of improved methods to identify underutilized lands in Europe. We employ advanced analysis methods based on time series modelling using Sentinel-2 (S2) data from 2017 to 2019 in order to distinguish utilized from underutilized land in twelve study areas in different bio-geographical regions (BGR) across Europe. The calculated parameters of the computed model function combined with temporal statistics were used to train a random forest classifier (RF). The achieved overall accuracies (OA) per study area vary between 80.25 and 96.76%, with confidence intervals (CI) ranging between 1.77% and 6.28% at a 95% confidence level. All in all, nearly 500,000 ha of underutilized land potentially available for agricultural bioenergy production were identified in this study, with the greatest amount mapped in Eastern Europe. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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28 pages, 1981 KiB  
Article
Measurements and Modeling of Optical-Equivalent Snow Grain Sizes under Arctic Low-Sun Conditions
by Evelyn Jäkel, Tim Carlsen, André Ehrlich, Manfred Wendisch, Michael Schäfer, Sophie Rosenburg, Konstantina Nakoudi, Marco Zanatta, Gerit Birnbaum, Veit Helm, Andreas Herber, Larysa Istomina, Linlu Mei and Anika Rohde
Remote Sens. 2021, 13(23), 4904; https://doi.org/10.3390/rs13234904 - 3 Dec 2021
Cited by 9 | Viewed by 3100
Abstract
The size and shape of snow grains directly impacts the reflection by a snowpack. In this article, different approaches to retrieve the optical-equivalent snow grain size (ropt) or, alternatively, the specific surface area (SSA) using satellite, airborne, and ground-based observations [...] Read more.
The size and shape of snow grains directly impacts the reflection by a snowpack. In this article, different approaches to retrieve the optical-equivalent snow grain size (ropt) or, alternatively, the specific surface area (SSA) using satellite, airborne, and ground-based observations are compared and used to evaluate ICON-ART (ICOsahedral Nonhydrostatic—Aerosols and Reactive Trace gases) simulations. The retrieval methods are based on optical measurements and rely on the ropt-dependent absorption of solar radiation in snow. The measurement data were taken during a three-week campaign that was conducted in the North of Greenland in March/April 2018, such that the retrieval methods and radiation measurements are affected by enhanced uncertainties under these low-Sun conditions. An adjusted airborne retrieval method is applied which uses the albedo at 1700 nm wavelength and combines an atmospheric and snow radiative transfer model to account for the direct-to-global fraction of the solar radiation incident on the snow. From this approach, we achieved a significantly improved uncertainty (<25%) and a reduced effect of atmospheric masking compared to the previous method. Ground-based in situ measurements indicated an increase of ropt of 15 µm within a five-day period after a snowfall event which is small compared to previous observations under similar temperature regimes. ICON-ART captured the observed change of ropt during snowfall events, but systematically overestimated the subsequent snow grain growth by about 100%. Adjusting the growth rate factor to 0.012 µm2 s−1 minimized the difference between model and observations. Satellite-based and airborne retrieval methods showed higher ropt over sea ice (<300 µm) than over land surfaces (<100 µm) which was reduced by data filtering of surface roughness features. Moderate-Resolution Imaging Spectroradiometer (MODIS) retrievals revealed a large spread within a series of subsequent individual overpasses, indicating their limitations in observing the snow grain size evolution in early spring conditions with low Sun. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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14 pages, 9160 KiB  
Article
Sea Surface Temperature Analysis for Fengyun-3C Data Using Oriented Elliptic Correlation Scales
by Zhihong Liao, Bin Xu, Junxia Gu and Chunxiang Shi
Sensors 2021, 21(23), 8067; https://doi.org/10.3390/s21238067 - 2 Dec 2021
Cited by 1 | Viewed by 1929
Abstract
Sea surface temperature (SST) is critical for global climate change analysis and research. In this study, we used visible and infrared scanning radiometer (VIRR) sea surface temperature (SST) data from the Fengyun-3C (FY-3C) satellite for SST analysis, and applied the Kalman filtering methods [...] Read more.
Sea surface temperature (SST) is critical for global climate change analysis and research. In this study, we used visible and infrared scanning radiometer (VIRR) sea surface temperature (SST) data from the Fengyun-3C (FY-3C) satellite for SST analysis, and applied the Kalman filtering methods with oriented elliptic correlation scales to construct SST fields. Firstly, the model for the oriented elliptic correlation scale was established for SST analysis. Secondly, observation errors from each type of SST data source were estimated using the optimal matched datasets, and background field errors were calculated using the model of oriented elliptic correlation scale. Finally, the blended SST analysis product was obtained using the Kalman filtering method, then the SST fields using the optimum interpolation (OI) method were chosen for comparison to validate results. The quality analysis for 2016 revealed that the Kalman analysis with a root-mean-square error (RMSE) of 0.3243 °C had better performance than did the OI analysis with a RMSE of 0.3911 °C, which was closer to the OISST product RMSE of 0.2897 °C. The results demonstrated that the Kalman filtering method with dynamic observation error and background error estimation was significantly superior to the OI method in SST analysis for FY-3C SST data. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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14 pages, 4556 KiB  
Article
Continuous Change Mapping to Understand Wetland Quantity and Quality Evolution and Driving Forces: A Case Study in the Liao River Estuary from 1986 to 2018
by Jianwei Peng, Shuguang Liu, Weizhi Lu, Maochou Liu, Shuailong Feng and Pifu Cong
Remote Sens. 2021, 13(23), 4900; https://doi.org/10.3390/rs13234900 - 2 Dec 2021
Cited by 31 | Viewed by 4344
Abstract
Coastal wetland ecosystems, one of the most important ecosystems in the world, play an important role in regulating climate, sequestering blue carbon, and maintaining sustainable development of coastal zones. Wetland landscapes are notoriously difficult to map with satellite data, particularly in highly complex, [...] Read more.
Coastal wetland ecosystems, one of the most important ecosystems in the world, play an important role in regulating climate, sequestering blue carbon, and maintaining sustainable development of coastal zones. Wetland landscapes are notoriously difficult to map with satellite data, particularly in highly complex, dynamic coastal regions. The Liao River Estuary (LRE) wetland in Liaoning Province, China, has attracted major attention due to its status as Asia’s largest coastal wetland, with extensive Phragmites australis (reeds), Suaeda heteroptera (seepweed, red beach), and other natural resources that have been continuously encroached upon by anthropogenic land-use activities. Using the Continuous Change Detection and Classification (CCDC) algorithm and all available Landsat images, we mapped the spatial–temporal changes of LRE coastal wetlands (e.g., seepweed, reed, tidal flats, and shallow marine water) annually from 1986 to 2018 and analyzed the changes and driving forces. Results showed that the total area of coastal wetlands in the LRE shrank by 14.8% during the study period. The tidal flats were the most seriously affected type, with 45.7% of its total area lost. One of the main characteristics of wetland change was the concurrent disappearance and emergence of wetlands in different parts of the LRE, creating drastically different mixtures of wetland quality (e.g., wetland age composition) in addition to area change. The reduction and replacement/translocation of coastal wetlands were mainly caused by human activities related to urbanization, tourism, land reclamation, and expansion of aquaculture ponds. Our efforts in mapping annual changes of wetlands provide direct, specific, and spatially explicit information on rates, patterns, and causes of coastal wetland change, both in coverage and quality, so as to contribute to the effective plans and policies for coastal management, preservation, and restoration of coastal ecosystem services. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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19 pages, 3014 KiB  
Article
A Digital-Simulation Model for a Full-Polarized Microwave Radiometer System and Its Calibration
by Jia Ding, Zhenzhan Wang, Yongqiang Duan, Xiaolin Tong and Hao Lu
Remote Sens. 2021, 13(23), 4888; https://doi.org/10.3390/rs13234888 - 1 Dec 2021
Cited by 2 | Viewed by 2518
Abstract
A digital-correlation full-polarized microwave radiometer is an important passive remote sensor, as it can obtain the amplitude and phase information of an electromagnetic wave at the same time. It is widely used in the measurement of sea surface wind speed and direction. Its [...] Read more.
A digital-correlation full-polarized microwave radiometer is an important passive remote sensor, as it can obtain the amplitude and phase information of an electromagnetic wave at the same time. It is widely used in the measurement of sea surface wind speed and direction. Its configuration is complicated, so the error analysis of the instrument is often difficult. This paper presents a full-polarized radiometer system model that can be used to analyze various errors, which include input signal models and a full-polarized radiometer (receiver) model. The input signal models are generated by WGN (white Gaussian noise), and the full-polarized radiometer model consists of an RF front-end model and digital back-end model. The calibration matrix is obtained by solving the overdetermined equations, and the output voltage is converted into Stokes brightness temperature through the calibration matrix. Then, we use the four Stokes parameters to analyze the sensitivity, linearity, and calibration residuals, from which the simulation model is validated. Finally, two examples of error analysis, including gain imbalance and quantization error, are given through a simulation model. In general, the simulation model proposed in this paper has good accuracy and can play an important role in the error analysis and pre-development of the fully polarized radiometer. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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21 pages, 10375 KiB  
Article
An Improved Equivalent Squint Range Model and Imaging Approach for Sliding Spotlight SAR Based on Highly Elliptical Orbit
by Xinchang Hu, Pengbo Wang, Hongcheng Zeng and Yanan Guo
Remote Sens. 2021, 13(23), 4883; https://doi.org/10.3390/rs13234883 - 1 Dec 2021
Cited by 5 | Viewed by 3118
Abstract
As an emerging orbital system with flexibility and brand application prospects, the highly elliptical orbit synthetic aperture radar (HEO SAR) can achieve both a low orbit detailed survey and continuous earth surface observation in high orbit, which could be applied to marine reconnaissance [...] Read more.
As an emerging orbital system with flexibility and brand application prospects, the highly elliptical orbit synthetic aperture radar (HEO SAR) can achieve both a low orbit detailed survey and continuous earth surface observation in high orbit, which could be applied to marine reconnaissance and surveillance. However, due to its large eccentricity, two challenges have been faced in the signal processing of HEO SAR at present. The first challenge is that the traditional equivalent squint range model (ESRM) fails to accurately describe the entire range for the whole orbit period including the perigee, the apogee, and the squint subduction section. The second one is to exploit an efficient HEO SAR imaging algorithm in the squinted case which solves the problem that traditional imaging algorithm fails to achieve the focused imaging processing of HEO SAR during the entire orbit period. In this paper, a novel imaging algorithm for HEO SAR is presented. Firstly, the signal model based on the geometric configuration of the large elliptical orbit is established and the Doppler parameter characteristics of SAR are analyzed. Secondly, due to the particularity of Doppler parameters variation in the whole period of HEO, the equivalent velocity and equivalent squint angle used in MESRM can no longer be applied, a refined fourth-order equivalent squint range model(R4-ESRM) that is suitable for HEO SAR is developed by introducing fourth-order Doppler parameter into Modified ESRM (MESRM), which accurately reconstructs the range history of HEO SAR. Finally, a novel imaging algorithm combining azimuth resampling and time-frequency domain hybrid correlation based on R4-ESRM is derived. Simulation is performed to demonstrate the feasibility and validity of the presented algorithm and range model, showing that it achieves the precise phase compensation and well focusing. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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15 pages, 4764 KiB  
Article
Design and Optimization for Mounting Primary Mirror with Reduced Sensitivity to Temperature Change in an Aerial Optoelectronic Sensor
by Meijun Zhang, Qipeng Lu, Haonan Tian, Dejiang Wang, Cheng Chen and Xin Wang
Sensors 2021, 21(23), 7993; https://doi.org/10.3390/s21237993 - 30 Nov 2021
Cited by 11 | Viewed by 3560
Abstract
In order to improve the image quality of the aerial optoelectronic sensor over a wide range of temperature changes, high thermal adaptability of the primary mirror as the critical components is considered. Integrated optomechanical analysis and optimization for mounting primary mirrors are carried [...] Read more.
In order to improve the image quality of the aerial optoelectronic sensor over a wide range of temperature changes, high thermal adaptability of the primary mirror as the critical components is considered. Integrated optomechanical analysis and optimization for mounting primary mirrors are carried out. The mirror surface shape error caused by uniform temperature decrease was treated as the objective function, and the fundamental frequency of the mirror assembly and the surface shape error caused by gravity parallel or vertical to the optical axis are taken as the constraints. A detailed size optimization is conducted to optimize its dimension parameters. Sensitivities of the optical system performance with respect to the size parameters are further evaluated. The configuration of the primary mirror and the flexure are obtained. The simulated optimization results show that the size parameters differently affect the optical performance and which factors are the key. The mirror surface shape error under 30 °C uniform temperature decrease effectively decreased from 26.5 nm to 11.6 nm, despite the weight of the primary mirror assembly increases by 0.3 kg. Compared to the initial design, the value of the system’s modulation transfer function (0° field angle) is improved from 0.15 to 0.21. Namely, the optical performance of the camera under thermal load has been enhanced and thermal adaptability of the primary mirror has been obviously reinforced after optimization. Based on the optimized results, a prototype of the primary mirror assembly is manufactured and assembled. A ground thermal test was conducted to verify difference in imaging quality at room and low temperature, respectively. The image quality of the camera meets the requirements of the index despite degrading. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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20 pages, 14700 KiB  
Article
ECAP-YOLO: Efficient Channel Attention Pyramid YOLO for Small Object Detection in Aerial Image
by Munhyeong Kim, Jongmin Jeong and Sungho Kim
Remote Sens. 2021, 13(23), 4851; https://doi.org/10.3390/rs13234851 - 29 Nov 2021
Cited by 103 | Viewed by 8074
Abstract
Detection of small targets in aerial images is still a difficult problem due to the low resolution and background-like targets. With the recent development of object detection technology, efficient and high-performance detector techniques have been developed. Among them, the YOLO series is a [...] Read more.
Detection of small targets in aerial images is still a difficult problem due to the low resolution and background-like targets. With the recent development of object detection technology, efficient and high-performance detector techniques have been developed. Among them, the YOLO series is a representative method of object detection that is light and has good performance. In this paper, we propose a method to improve the performance of small target detection in aerial images by modifying YOLOv5. The backbone is was modified by applying the first efficient channel attention module, and the channel attention pyramid method was proposed. We propose an efficient channel attention pyramid YOLO (ECAP-YOLO). Second, in order to optimize the detection of small objects, we eliminated the module for detecting large objects and added a detect layer to find smaller objects, reducing the computing power used for detecting small targets and improving the detection rate. Finally, we use transposed convolution instead of upsampling. Comparing the method proposed in this paper to the original YOLOv5, the performance improvement for the mAP was 6.9% when using the VEDAI dataset, 5.4% when detecting small cars in the xView dataset, 2.7% when detecting small vehicle and small ship classes from the DOTA dataset, and approximately 2.4% when finding small cars in the Arirang dataset. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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24 pages, 8762 KiB  
Article
Desertification Extraction Based on a Microwave Backscattering Contribution Decomposition Model at the Dry Bottom of the Aral Sea
by Yubin Song, Hongwei Zheng, Xi Chen, Anming Bao, Jiaqiang Lei, Wenqiang Xu, Geping Luo and Qing Guan
Remote Sens. 2021, 13(23), 4850; https://doi.org/10.3390/rs13234850 - 29 Nov 2021
Cited by 4 | Viewed by 2288
Abstract
The fine particles produced during the desertification process provide a rich material source for sand and dust activities. Accurately locating the desertified areas is a prerequisite for human intervention in sand and dust activities. In arid and semi-arid regions, due to very sparse [...] Read more.
The fine particles produced during the desertification process provide a rich material source for sand and dust activities. Accurately locating the desertified areas is a prerequisite for human intervention in sand and dust activities. In arid and semi-arid regions, due to very sparse vegetation coverage, the microwave surface scattering model is very suitable for describing the variation of topsoil property during the process of desertification. However, the microwave backscattering coefficient (MBC) trend of the soil during the desertification process is still unclear now. Moreover, the MBC of a resolution unit usually involves the contribution of soil and vegetation. These problems seriously limit the application of microwave remote sensing technology in desertification identification. In this paper, we studied the soil MBC change trend during the desertification process and proposed a microwave backscattering contribution decomposition (MBCD) model to estimate the soil MBC of a resolution unit. Furthermore, a simple microwave backscattering threshold (SMSBT) model was established to describe the severity of desertification. The MBCD and SMSBT models were verified qualitatively through landscape photos of sampling points from a field survey in November 2018. The results showed that the MBC would gradually decline with the deepening degree of desertification. The MBCD model and the corresponding least squares method can be used to estimate the soil MBC accurately, and the SMSBT model can accurately distinguish different degrees of desertification. The results of desertification classification showed that more than 68% of the dry bottom of the Aral Sea is suffering from different degrees of desertification. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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16 pages, 841 KiB  
Technical Note
Ship Detection via Dilated Rate Search and Attention-Guided Feature Representation
by Jianming Hu, Xiyang Zhi, Tianjun Shi, Lijian Yu and Wei Zhang
Remote Sens. 2021, 13(23), 4840; https://doi.org/10.3390/rs13234840 - 29 Nov 2021
Cited by 3 | Viewed by 1910
Abstract
Due to the complexity of scene interference and the variability of ship scale and position, automatic ship detection in remote sensing images makes for challenging research. The existing deep networks rarely design receptive fields that fit the target scale based on training data. [...] Read more.
Due to the complexity of scene interference and the variability of ship scale and position, automatic ship detection in remote sensing images makes for challenging research. The existing deep networks rarely design receptive fields that fit the target scale based on training data. Moreover, most of them ignore the effective retention of position information in the feature extraction process, which reduces the contribution of features to subsequent classification. To overcome these limitations, we propose a novel ship detection framework combining the dilated rate selection and attention-guided feature representation strategies, which can efficiently detect ships of different scales under the interference of complex environments such as clouds, sea clutter and mist. Specifically, we present a dilated convolution parameter search strategy to adaptively select the dilated rate for the multi-branch extraction architecture, adaptively obtaining context information of different receptive fields without sacrificing the image resolution. Moreover, to enhance the spatial position information of the feature maps, we calculate the correlation of spatial points from the vertical and horizontal directions and embed it into the channel compression coding process, thus generating the multi-dimensional feature descriptors which are sensitive to direction and position characteristics of ships. Experimental results on the Airbus dataset demonstrate that the proposed method achieves state-of-the-art performance compared with other detection models. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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24 pages, 12355 KiB  
Article
Accuracy Comparison and Assessment of DSM Derived from GFDM Satellite and GF-7 Satellite Imagery
by Xiaoyong Zhu, Xinming Tang, Guo Zhang, Bin Liu and Wenmin Hu
Remote Sens. 2021, 13(23), 4791; https://doi.org/10.3390/rs13234791 - 26 Nov 2021
Cited by 18 | Viewed by 2980
Abstract
Digital Surface Model (DSM) derived from high resolution satellite imagery is important for various applications. GFDM is China’s first civil optical remote sensing satellite with multiple agile imaging modes and sub-meter resolution. Its panchromatic resolution is 0.5 m and 1.68 m for multi-spectral [...] Read more.
Digital Surface Model (DSM) derived from high resolution satellite imagery is important for various applications. GFDM is China’s first civil optical remote sensing satellite with multiple agile imaging modes and sub-meter resolution. Its panchromatic resolution is 0.5 m and 1.68 m for multi-spectral images. Compared with the onboard stereo viewing instruments (0.8 m for forward image, 0.65 m for back image, and 2.6 m for back multi-spectrum images) of GF-7, a mapping satellite of China in the same period, their accuracy is very similar. However, the accuracy of GFDM DSM has not yet been verified or fully characterized, and the detailed difference between the two has not yet been assessed either. This paper evaluates the DSM accuracy generated by GFDM and GF-7 satellite imagery using high-precision reference DSM and the observations of Ground Control Points (GCPs) as the reference data. A method to evaluate the DSM accuracy based on regional DSM errors and GCPs errors is proposed. Through the analysis of DSM subtraction, profile lines, strips detection and residuals coupling differences, the differences of DSM overall accuracy, vertical accuracy, horizontal accuracy and the strips errors between GFDM DSM and GF-7 DSM are evaluated. The results show that the overall accuracy of both is close while the vertical accuracy is slightly different. When regional DSM is used as the benchmark, the GFDM DSM has a slight advantage in elevation accuracy, but there are some regular fluctuation strips with small amplitude. When GCPs are used as the reference, the elevation Root Mean Square Error (RMSE) of GFDM DSM is about 0.94 m, and that of GF-7 is 0.67 m. GF-7 DSM is more accurate, but both of the errors are within 1 m. The DSM image residuals of the GF-7 are within 0.5 pixel, while the residuals of GFDM are relatively large, reaching 0.8 pixel. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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27 pages, 23056 KiB  
Article
Reconstruction of All-Weather Daytime and Nighttime MODIS Aqua-Terra Land Surface Temperature Products Using an XGBoost Approach
by Weiwei Tan, Chunzhu Wei, Yang Lu and Desheng Xue
Remote Sens. 2021, 13(22), 4723; https://doi.org/10.3390/rs13224723 - 22 Nov 2021
Cited by 35 | Viewed by 3835
Abstract
Generating spatiotemporally continuous land surface temperature (LST) data is in great demand for hydrology, meteorology, ecology, environmental studies, etc. However, the thermal infrared (TIR)-based LST measurements are prone to cloud contamination with missing pixels. To repair the missing pixels, a new XGBoost-based linking [...] Read more.
Generating spatiotemporally continuous land surface temperature (LST) data is in great demand for hydrology, meteorology, ecology, environmental studies, etc. However, the thermal infrared (TIR)-based LST measurements are prone to cloud contamination with missing pixels. To repair the missing pixels, a new XGBoost-based linking approach for reconstructing daytime and nighttime Moderate Resolution Imaging Spectroradiometer (MODIS) LST measurements was introduced. The instantaneous solar radiation and two soil-related predictors from China Data Assimilation System (CLDAS) 0.0625°/1-h data were selected as the linking variables to depict the relationship with instantaneous MODIS LST data. Other land surface properties, including two vegetation indices, the water index, the surface albedo, and topographic parameters, were also used as the predictor variables. The XGBoost method was used to fit an LST linking model by the training datasets from clear-sky pixels and was then applied to the MODIS Aqua-Terra LSTs during summer time (June to August) in 2017 and 2018 across China. The recovered LST data was further rectified with the Savitzky–Golay (SG) filtering method. The results showed the distribution of the reconstructed LSTs present a reasonable pattern for different land-cover types and topography. The evaluation results using in situ longwave radiation measurements showed the RMSE varies from 3.91 K to 5.53 K for the cloud-free pixels and from 4.42 K to 4.97 K for the cloud-covered pixels. In addition, the reconstructed LST products correlated well with CLDAS LST data with similar LST spatial patterns. The variable importance analysis revealed that the two soil-related predictors and the elevation variable are key parameters due to their great contribution to the XGBoost model performance. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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25 pages, 7521 KiB  
Article
Seamless Mosaicking of UAV-Based Push-Broom Hyperspectral Images for Environment Monitoring
by Lina Yi, Jing M. Chen, Guifeng Zhang, Xiao Xu, Xing Ming and Wenji Guo
Remote Sens. 2021, 13(22), 4720; https://doi.org/10.3390/rs13224720 - 22 Nov 2021
Cited by 22 | Viewed by 3464
Abstract
This paper proposes a systematic image mosaicking methodology to produce hyperspectral image for environment monitoring using an emerging UAV-based push-broom hyperspectral imager. The suitability of alternative methods in each step is assessed by experiments of an urban scape, a river course and a [...] Read more.
This paper proposes a systematic image mosaicking methodology to produce hyperspectral image for environment monitoring using an emerging UAV-based push-broom hyperspectral imager. The suitability of alternative methods in each step is assessed by experiments of an urban scape, a river course and a forest study area. First, the hyperspectral image strips were acquired by sequentially stitching the UAV images acquired by push-broom scanning along each flight line. Next, direct geo-referencing was applied to each image strip to get initial geo-rectified result. Then, with ground control points, the curved surface spline function was used to transform the initial geo-rectified image strips to improve their geometrical accuracy. To further remove the displacement between pairs of image strips, an improved phase correlation (IPC) and a SIFT and RANSAC-based method (SR) were used in image registration. Finally, the weighted average and the best stitching image fusion method were used to remove the spectral differences between image strips and get the seamless mosaic. Experiment results showed that as the GCPs‘ number increases, the mosaicked image‘s geometrical accuracy increases. In image registration, there exists obvious edge information that can be accurately extracted from the urban scape and river course area; comparative results can be achieved by the IPC method with less time cost. However, for the ground objects with complex texture like forest, the edges extracted from the image is prone to be inaccurate and result in the failure of the IPC method, and only the SR method can get a good result. In image fusion, the best stitching fusion method can get seamless results for all three study areas. Whereas, the weighted average fusion method was only useful in eliminating the stitching line for the river course and forest areas but failed for the urban scape area due to the spectral heterogeneity of different ground objects. For different environment monitoring applications, the proposed methodology provides a practical solution to seamlessly mosaic UAV-based push-broom hyperspectral images with high geometrical accuracy and spectral fidelity. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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15 pages, 5936 KiB  
Article
Retreating Shorelines as an Emerging Threat to Adélie Penguins on Inexpressible Island
by Xintong Chen, Jiquan Chen, Xiao Cheng, Lizhong Zhu, Bing Li and Xianglan Li
Remote Sens. 2021, 13(22), 4718; https://doi.org/10.3390/rs13224718 - 22 Nov 2021
Cited by 3 | Viewed by 2671
Abstract
Long-term observation of penguin abundance and distribution may warn of changes in the Antarctic marine ecosystem and provide support for penguin conservation. We conducted an unmanned aerial vehicle (UAV) survey of the Adélie penguin (Pygoscelis adeliae) colony on Inexpressible Island and [...] Read more.
Long-term observation of penguin abundance and distribution may warn of changes in the Antarctic marine ecosystem and provide support for penguin conservation. We conducted an unmanned aerial vehicle (UAV) survey of the Adélie penguin (Pygoscelis adeliae) colony on Inexpressible Island and obtained aerial images with a resolution of 0.07 m in 2018. We estimated penguin abundance and identified the spatial extent of the penguin colony. A total of 24,497 breeding pairs were found on Inexpressible Island within a colony area of 57,507 m2. Based on historical images, the colony area expanded by 30,613 m2 and abundance increased by 4063 pairs between 1983 and 2012. Between 2012 and 2018 penguin abundance further increased by 3314 pairs, although the colony area decreased by 1903 m2. In general, Adélie penguins bred on Inexpressible Island at an elevation <20 m, and >55% of penguins had territories within 150 m of the shoreline. This suggests that penguins prefer to breed in areas with a low elevation and close to the shoreline. We observed a retreat of the shoreline on Inexpressible Island between 1983 and 2018, especially along the northern coast, which may have played a key role in the expansion of the penguin colony on the northern coast. In sum, it appears that retreating shorelines reshaped penguin distribution on the island and may be an emerging risk factor for penguins. These results highlight the importance of remote sensing techniques for monitoring changes in the Antarctic marine ecosystem and providing reliable data for Antarctic penguin conservation. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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15 pages, 8113 KiB  
Technical Note
Raytracing Simulated GPS Radio Wave Propagation Paths Experiencing Large Disturbances When Going through the Top of the Sub-Cloud Layer
by Shengpeng Yang, Xiaolei Zou and Richard Anthes
Remote Sens. 2021, 13(22), 4693; https://doi.org/10.3390/rs13224693 - 20 Nov 2021
Cited by 3 | Viewed by 3337
Abstract
Global positioning satellite system (GPS) radio waves that reach the tropical lower troposphere are strongly affected by small-scale water vapor fluctuations. We examine along-the-ray simulations of the impact parameter at every ray integration step using the high-resolution European Centre for Medium-Range Weather Forecasts [...] Read more.
Global positioning satellite system (GPS) radio waves that reach the tropical lower troposphere are strongly affected by small-scale water vapor fluctuations. We examine along-the-ray simulations of the impact parameter at every ray integration step using the high-resolution European Centre for Medium-Range Weather Forecasts ERA5 reanalysis as the input model states. We find that disturbances to the impact parameter arise when ray paths go through the top of the sub-cloud layer, where there is a pronounced reduction with increasing height in the humidity, and wet refractivity has a strong local vertical gradient, creating multipath. Additionally, the horizontal gradients of refractivity cause the impact parameter to vary along the ray. The disturbances to the impact parameter are confined to an area about 250 km horizontally and 4 km vertically from the perigee point. Beyond 250 km from the perigee, the impact parameter remains constant. The vertical gradient of refractivity is largest at the top of the sub-cloud layer, usually between 1.5 and 3.0 km, and becomes negligibly small above 4 km. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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25 pages, 9733 KiB  
Article
Regional Crustal Vertical Deformation Driven by Terrestrial Water Load Depending on CORS Network and Environmental Loading Data: A Case Study of Southeast Zhejiang
by Wanqiu Li, Jie Dong, Wei Wang, Hanjiang Wen, Huanling Liu, Qiuying Guo, Guobiao Yao and Chuanyin Zhang
Sensors 2021, 21(22), 7699; https://doi.org/10.3390/s21227699 - 19 Nov 2021
Cited by 5 | Viewed by 2414
Abstract
Monitoring regional terrestrial water load deformation is of great significance to the dynamic maintenance and hydrodynamic study of the regional benchmark framework. In view of the lack of a spatial interpolation method based on the GNSS (Global Navigation Satellite System) elevation time series [...] Read more.
Monitoring regional terrestrial water load deformation is of great significance to the dynamic maintenance and hydrodynamic study of the regional benchmark framework. In view of the lack of a spatial interpolation method based on the GNSS (Global Navigation Satellite System) elevation time series for obtaining terrestrial water load deformation information, this paper proposes to employ a CORS (Continuously Operating Reference Stations) network combined with environmental loading data, such as ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric data, the GLDAS (Global Land Data Assimilation System) hydrological model, and MSLA (Mean Sea Level Anomaly) data. Based on the load deformation theory and spherical harmonic analysis method, we took 38 CORS stations in southeast Zhejiang province as an example and comprehensively determined the vertical deformation of the crust as caused by regional terrestrial water load changes from January 2015 to December 2017, and then compared these data with the GRACE (Gravity Recovery and Climate Experiment) satellite. The results show that the vertical deformation value of the terrestrial water load in southeast Zhejiang, as monitored by the CORS network, can reach a centimeter, and the amplitude changes from ?1.8 cm to 2.4 cm. The seasonal change is obvious, and the spatial distribution takes a ladder form from inland to coastal regions. The surface vertical deformation caused by groundwater load changes in the east–west–south–north–central sub-regions show obvious fluctuations from 2015 to 2017, and the trends of the five sub-regions are consistent. The amplitude of surface vertical deformation caused by groundwater load change in the west is higher than that in the east. We tested the use of GRACE for the verification of CORS network monitoring results and found a relatively consistent temporal distribution between both data sets after phase delay correction on GRACE, except for in three months—November in 2015, and January and February in 2016. The results show that the comprehensive solution based on the CORS network can effectively improve the monitoring of crustal vertical deformation during regional terrestrial water load change. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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14 pages, 5442 KiB  
Review
A Review on the Development of Earthquake Warning System Using Low-Cost Sensors in Taiwan
by Yih-Min Wu and Himanshu Mittal
Sensors 2021, 21(22), 7649; https://doi.org/10.3390/s21227649 - 18 Nov 2021
Cited by 20 | Viewed by 10574
Abstract
Seismic instrumentation for earthquake early warnings (EEWs) has improved significantly in the last few years, considering the station coverage, data quality, and the related applications. The official EEW system in Taiwan is operated by the Central Weather Bureau (CWB) and is responsible for [...] Read more.
Seismic instrumentation for earthquake early warnings (EEWs) has improved significantly in the last few years, considering the station coverage, data quality, and the related applications. The official EEW system in Taiwan is operated by the Central Weather Bureau (CWB) and is responsible for issuing the regional warning for moderate-to-large earthquakes occurring in and around Taiwan. The low-cost micro-electro-mechanical system (MEMS)-based P-Alert EEW system is operational in Taiwan for on-site warnings and for producing shakemaps. Since 2010, this P-Alert system, installed by the National Taiwan University (NTU), has shown its importance during various earthquakes that caused damage in Taiwan. Although the system is capable of acting as a regional as well as an on-site warning system, it is particularly useful for on-site warning. Using real-time seismic signals, each P-Alert system can provide a 2–8 s-long warning time for the locations situated in the blind zone of the CWB regional warning system. The shakemaps plotted using this instrumentation help to assess the damage pattern and rupture directivity, a key feature in the risk mitigation process. These shakemaps are delivered to the intended users, including the disaster mitigation authorities, for possible relief purposes. Earlier, the network provided only peak ground acceleration (PGA) shakemaps, but has now been updated to include peak ground velocity (PGV), spectral acceleration (Sa) at different periods, and CWB intensity maps. The PGA and PGV shakemaps plotted using this network have proven helpful in establishing the fact that PGV is a better indicator of damage detection than PGA. This instrumentation is also useful in structural health-monitoring and estimating co-seismic deformations. Encouraged by the performance of the P-Alert network, more instruments are installed in Asia-Pacific countries. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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21 pages, 4560 KiB  
Article
Spatiotemporal Trends and Variations of the Rainfall Amount, Intensity, and Frequency in TRMM Multi-satellite Precipitation Analysis (TMPA) Data
by Qian Liu, Long S. Chiu, Xianjun Hao and Chaowei Yang
Remote Sens. 2021, 13(22), 4629; https://doi.org/10.3390/rs13224629 - 17 Nov 2021
Cited by 6 | Viewed by 2745
Abstract
The spatiotemporal mean rain rate (MR) can be characterized by the rain frequency (RF) and the conditional rain rate (CR). We computed these parameters for each season using the TMPA 3-hourly, 0.25° gridded data for the 1998–2017 period at a quasi-global scale, 50°N~50°S. [...] Read more.
The spatiotemporal mean rain rate (MR) can be characterized by the rain frequency (RF) and the conditional rain rate (CR). We computed these parameters for each season using the TMPA 3-hourly, 0.25° gridded data for the 1998–2017 period at a quasi-global scale, 50°N~50°S. For the global long-term average, MR, RF, and CR are 2.83 mm/d, 10.55%, and 25.05 mm/d, respectively. The seasonal time series of global mean RF and CR show significant decreasing and increasing trends, respectively, while MR depicts only a small but significant trend. The seasonal anomaly of RF decreased by 5.29% and CR increased 13.07 mm/d over the study period, while MR only slightly decreased by ?0.029 mm/day. The spatiotemporal patterns in MR, RF, and CR suggest that although there is no prominent trend in the total precipitation amount, the frequency of rainfall events becomes smaller and the average intensity of a single event becomes stronger. Based on the co-variability of RF and CR, the paper optimally classifies the precipitation over land and ocean into four categories using K-means clustering. The terrestrial clusters are consistent with the dry and wet climatology, while categories over the ocean indicate high RF and medium CR in the Inter Tropical Convergence Zone (ITCZ) region; low RF with low CR in oceanic dry zones; and low RF and high CR in storm track areas. Empirical Orthogonal Function (EOF) analysis was then performed, and these results indicated that the major pattern of MR is characterized by an El Niño-Southern Oscillation (ENSO) signal while RF and CR variations are dominated by their trends. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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11 pages, 9637 KiB  
Technical Note
Mitigation of Mutual Antenna Coupling Effects for Active Radar Targets in L-Band
by Anna Maria Büchner, Klaus Weidenhaupt, Bernd Gabler, Markus Limbach and Marco Schwerdt
Remote Sens. 2021, 13(22), 4614; https://doi.org/10.3390/rs13224614 - 16 Nov 2021
Cited by 1 | Viewed by 2320
Abstract
In order to realize a compact L-band transponder design for the calibration of spaceborne synthetic aperture radar (SAR) systems, a novel antenna was developed by DLR. As with previous designs for different frequency bands, the future transponder is based on a two-antenna concept. [...] Read more.
In order to realize a compact L-band transponder design for the calibration of spaceborne synthetic aperture radar (SAR) systems, a novel antenna was developed by DLR. As with previous designs for different frequency bands, the future transponder is based on a two-antenna concept. This paper addresses the issue of antenna coupling between corrugated L-band horn antennas, which are operated in close proximity. The antenna coupling is analyzed via simulations and measurements by utilizing specifically defined coupling parameters. Additionally, improvements to further lower the mutual antenna coupling have been designed, tested, and are described in this paper. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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20 pages, 8161 KiB  
Article
A Moving Ship Detection and Tracking Method Based on Optical Remote Sensing Images from the Geostationary Satellite
by Wei Yu, Hongjian You, Peng Lv, Yuxin Hu and Bing Han
Sensors 2021, 21(22), 7547; https://doi.org/10.3390/s21227547 - 13 Nov 2021
Cited by 13 | Viewed by 3398
Abstract
Geostationary optical remote sensing satellites, such as the GF-4, have a high temporal resolution and wide coverage, which enables the continuous tracking and observation of ship targets over a large range. However, the ship targets in the images are usually small and dim [...] Read more.
Geostationary optical remote sensing satellites, such as the GF-4, have a high temporal resolution and wide coverage, which enables the continuous tracking and observation of ship targets over a large range. However, the ship targets in the images are usually small and dim and the images are easily affected by clouds, islands and other factors, which make it difficult to detect the ship targets. This paper proposes a new method for detecting ships moving on the sea surface using GF-4 satellite images. First, the adaptive nonlinear gray stretch (ANGS) method was used to enhance the image and highlight small and dim ship targets. Second, a multi-scale dual-neighbor difference contrast measure (MDDCM) method was designed to enable detection of the position of the candidate ship target. The shape characteristics of each candidate area were analyzed to remove false ship targets. Finally, the joint probability data association (JPDA) method was used for multi-frame data association and tracking. Our results suggest that the proposed method can effectively detect and track moving ship targets in GF-4 satellite optical remote sensing images, with better detection performance than other classical methods. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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22 pages, 8456 KiB  
Article
Improved Oriented Object Detection in Remote Sensing Images Based on a Three-Point Regression Method
by Falin Wu, Jiaqi He, Guopeng Zhou, Haolun Li, Yushuang Liu and Xiaohong Sui
Remote Sens. 2021, 13(22), 4517; https://doi.org/10.3390/rs13224517 - 10 Nov 2021
Cited by 4 | Viewed by 2797
Abstract
Object detection in remote sensing images plays an important role in both military and civilian remote sensing applications. Objects in remote sensing images are different from those in natural images. They have the characteristics of scale diversity, arbitrary directivity, and dense arrangement, which [...] Read more.
Object detection in remote sensing images plays an important role in both military and civilian remote sensing applications. Objects in remote sensing images are different from those in natural images. They have the characteristics of scale diversity, arbitrary directivity, and dense arrangement, which causes difficulties in object detection. For objects with a large aspect ratio and that are oblique and densely arranged, using an oriented bounding box can help to avoid deleting some correct detection bounding boxes by mistake. The classic rotational region convolutional neural network (R2CNN) has advantages for text detection. However, R2CNN has poor performance in the detection of slender objects with arbitrary directivity in remote sensing images, and its fault tolerance rate is low. In order to solve this problem, this paper proposes an improved R2CNN based on a double detection head structure and a three-point regression method, namely, TPR-R2CNN. The proposed network modifies the original R2CNN network structure by applying a double fully connected (2-fc) detection head and classification fusion. One detection head is for classification and horizontal bounding box regression, the other is for classification and oriented bounding box regression. The three-point regression method (TPR) is proposed for oriented bounding box regression, which determines the positions of the oriented bounding box by regressing the coordinates of the center point and the first two vertices. The proposed network was validated on the DOTA-v1.5 and HRSC2016 datasets, and it achieved a mean average precision (mAP) of 3.90% and 15.27%, respectively, from feature pyramid network (FPN) baselines with a ResNet-50 backbone. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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23 pages, 152294 KiB  
Article
Monitoring and Quantitative Human Risk Assessment of Municipal Solid Waste Landfill Using Integrated Satellite–UAV–Ground Survey Approach
by Shuai Zhang, Yunhong Lv, Haiben Yang, Yingyue Han, Jingyu Peng, Jiwu Lan, Liangtong Zhan, Yunmin Chen and Bate Bate
Remote Sens. 2021, 13(22), 4496; https://doi.org/10.3390/rs13224496 - 9 Nov 2021
Cited by 11 | Viewed by 4117
Abstract
Landfills are the dominant method of municipal solid waste (MSW) disposal in many developing countries, which are extremely susceptible to failure under circumstances of high pore water pressure and insufficient compaction. Catastrophic landfill failures have occurred worldwide, causing large numbers of fatalities. Tianziling [...] Read more.
Landfills are the dominant method of municipal solid waste (MSW) disposal in many developing countries, which are extremely susceptible to failure under circumstances of high pore water pressure and insufficient compaction. Catastrophic landfill failures have occurred worldwide, causing large numbers of fatalities. Tianziling landfill, one of the largest engineered sanitary landfills in China, has experienced massive deformation since January 2020, making early identification and monitoring of great significance for the purpose of risk management. The human risk posed by potential landfill failures also needs to be quantitatively evaluated. The interferometric synthetic aperture radar (InSAR) technique, unmanned aerial vehicle (UAV) photogrammetry, and ground measurements were combined to obtain landfill deformation data in this study. The integrated satellite–UAV–ground survey (ISUGS) approach ensures a comprehensive understanding of landfill deformation and evolution. The deformation characteristics obtained using the InSAR technique and UAV photogrammetry were analyzed and compared. A close relationship between the most severe mobility events, precipitation episodes, and was observed. Based on early hazard identification using ISUGS, a quantitative risk assessment (QRA) method and F-N curves were proposed, which can be applied to landfills. The comparison showed that ISUGS allowed a better understanding of the spatial and temporal evolution of the landfill and more accurate QRA results, which could be as references for local governments to take effective precautions. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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17 pages, 7568 KiB  
Article
Mapping Aquifer Storage Properties Using S-Wave Velocity and InSAR-Derived Surface Displacement in the Kumamoto Area, Southwest Japan
by Mohamed Mourad, Takeshi Tsuji, Tatsunori Ikeda, Kazuya Ishitsuka, Shigeki Senna and Kiyoshi Ide
Remote Sens. 2021, 13(21), 4391; https://doi.org/10.3390/rs13214391 - 31 Oct 2021
Cited by 5 | Viewed by 2875
Abstract
We present a novel approach to mapping the storage coefficient (Sk) from InSAR-derived surface deformation and S-wave velocity (Vs). We first constructed a 3D Vs model in the Kumamoto area, southwest Japan, by applying 3D empirical Bayesian kriging to [...] Read more.
We present a novel approach to mapping the storage coefficient (Sk) from InSAR-derived surface deformation and S-wave velocity (Vs). We first constructed a 3D Vs model in the Kumamoto area, southwest Japan, by applying 3D empirical Bayesian kriging to the 1D Vs profiles estimated by the surface-wave analysis at 676 measured points. We also used the time series of InSAR deformation and groundwater-level data at 13 well sites covering April 2016 and December 2018 and estimated the Sk of the confined aquifer. The Sk estimated from InSAR, and well data ranged from ~0.03 to 2 × 10?3, with an average of 7.23 × 10?3, values typical for semi-confined and confined conditions. We found a clear relationship between the Sk and Vs at well locations, indicating that the compressibility of an aquifer is related to the stiffness or Vs. By applying the relationship to the 3D Vs model, we succeeded in mapping the Sk in an extensive area. Furthermore, the estimated Sk distribution correlates well with the hydrogeological setting: semi-confined conditions are predicted in the Kumamoto alluvial plain with a high Sk. Our approach is thus effective for estimating aquifer storage properties from Vs, even where limited groundwater-level data are available. Furthermore, we can estimate groundwater-level variation from the geodetic data. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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25 pages, 9228 KiB  
Article
Parallel Ensemble Deep Learning for Real-Time Remote Sensing Video Multi-Target Detection
by Long Sun, Jie Chen, Dazheng Feng and Mengdao Xing
Remote Sens. 2021, 13(21), 4377; https://doi.org/10.3390/rs13214377 - 30 Oct 2021
Cited by 4 | Viewed by 4110
Abstract
Unmanned aerial vehicle (UAV) is one of the main means of information warfare, such as in battlefield cruises, reconnaissance, and military strikes. Rapid detection and accurate recognition of key targets in UAV images are the basis of subsequent military tasks. The UAV image [...] Read more.
Unmanned aerial vehicle (UAV) is one of the main means of information warfare, such as in battlefield cruises, reconnaissance, and military strikes. Rapid detection and accurate recognition of key targets in UAV images are the basis of subsequent military tasks. The UAV image has characteristics of high resolution and small target size, and in practical application, the detection speed is often required to be fast. Existing algorithms are not able to achieve an effective trade-off between detection accuracy and speed. Therefore, this paper proposes a parallel ensemble deep learning framework for unmanned aerial vehicle video multi-target detection, which is a global and local joint detection strategy. It combines a deep learning target detection algorithm with template matching to make full use of image information. It also integrates multi-process and multi-threading mechanisms to speed up processing. Experiments show that the system has high detection accuracy for targets with focal lengths varying from one to ten times. At the same time, the real-time and stable display of detection results is realized by aiming at the moving UAV video image. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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19 pages, 3092 KiB  
Article
Ground Moving Target Imaging for Highly Squint SAR by Modified Minimum Entropy Algorithm and Spectrum Rotation
by Shichao Xiong, Jiacheng Ni, Qun Zhang, Ying Luo and Longqiang Yu
Remote Sens. 2021, 13(21), 4373; https://doi.org/10.3390/rs13214373 - 30 Oct 2021
Cited by 3 | Viewed by 2145
Abstract
Ground moving target (GMT) is displaced and defocused in conventional synthetic aperture radar (SAR) image due to the residual phase error of non-cooperative GMT motion. In this study, a GMT imaging (GMTIm) method is proposed for highly squint SAR. As the squint angle [...] Read more.
Ground moving target (GMT) is displaced and defocused in conventional synthetic aperture radar (SAR) image due to the residual phase error of non-cooperative GMT motion. In this study, a GMT imaging (GMTIm) method is proposed for highly squint SAR. As the squint angle become large, the displace and defocus effect of the GMT image become severe and the geometry distortion of the GMT image cannot be ignored. The proposed method first deduced the two-dimensional (2-D) frequency domain signal of the GMT and the bulk compression function of the Range Migration Algorithm (RMA) in highly squint SAR. Then GMT ROI data are extracted and a modified minimum entropy algorithm (MMEA) is proposed to refocus the GMT image. MMEA introduces the idea of bisection into the iteration process to converge more efficiently than the previous minimum entropy method. To overcome the geometry distortion of the GMT image, an equivalent squint angle spectrum rotation method is proposed. Finally, to suppress the GMT image sidelobe, the sparse characteristic of GMT is considered and a sparse enhancement method is adopted. The proposed method can realize GMTIm in highly squint SAR where the squint angle reaches to 75 degrees. The PSNR and ISLR of point target in highly squint SAR is close to that in side-looking SAR. The simulated point target data and ship data are used to validate the effectiveness of the proposed method. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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22 pages, 4171 KiB  
Article
Target Detection in High-Resolution SAR Image via Iterating Outliers and Recursing Saliency Depth
by Zongyong Cui, Yi Qin, Yating Zhong, Zongjie Cao and Haiyi Yang
Remote Sens. 2021, 13(21), 4315; https://doi.org/10.3390/rs13214315 - 27 Oct 2021
Cited by 6 | Viewed by 2643
Abstract
In dealing with the problem of target detection in high-resolution Synthetic Aperture Radar (SAR) images, segmenting before detecting is the most commonly used approach. After the image is segmented by the superpixel method, the segmented area is usually a mixture of target and [...] Read more.
In dealing with the problem of target detection in high-resolution Synthetic Aperture Radar (SAR) images, segmenting before detecting is the most commonly used approach. After the image is segmented by the superpixel method, the segmented area is usually a mixture of target and background, but the existing regional feature model does not take this into account, and cannot accurately reflect the features of the SAR image. Therefore, we propose a target detection method based on iterative outliers and recursive saliency depth. At first, we use the conditional entropy to model the features of the superpixel region, which is more in line with the actual SAR image features. Then, through iterative anomaly detection, we achieve effective background selection and detection threshold design. After that, recursing saliency depth is used to enhance the effective outliers and suppress the background false alarm to realize the correction of superpixel saliency value. Finally, the local graph model is used to optimize the detection results. Compared with Constant False Alarm Rate (CFAR) and Weighted Information Entropy (WIE) methods, the results show that our method has better performance and is more in line with the actual situation. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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19 pages, 21533 KiB  
Article
Constraint Loss for Rotated Object Detection in Remote Sensing Images
by Luyang Zhang, Haitao Wang, Lingfeng Wang, Chunhong Pan, Qiang Liu and Xinyao Wang
Remote Sens. 2021, 13(21), 4291; https://doi.org/10.3390/rs13214291 - 25 Oct 2021
Cited by 4 | Viewed by 5420
Abstract
Rotated object detection is an extension of object detection that uses an oriented bounding box instead of a general horizontal bounding box to define the object position. It is widely used in remote sensing images, scene text, and license plate recognition. The existing [...] Read more.
Rotated object detection is an extension of object detection that uses an oriented bounding box instead of a general horizontal bounding box to define the object position. It is widely used in remote sensing images, scene text, and license plate recognition. The existing rotated object detection methods usually add an angle prediction channel in the bounding box prediction branch, and smooth L1 loss is used as the regression loss function. However, we argue that smooth L1 loss causes a sudden change in loss and slow convergence due to the angle solving mechanism of open CV (the angle between the horizontal line and the first side of the bounding box in the counter-clockwise direction is defined as the rotation angle), and this problem exists in most existing regression loss functions. To solve the above problems, we propose a decoupling modulation mechanism to overcome the problem of sudden changes in loss. On this basis, we also proposed a constraint mechanism, the purpose of which is to accelerate the convergence of the network and ensure optimization toward the ideal direction. In addition, the proposed decoupling modulation mechanism and constraint mechanism can be integrated into the popular regression loss function individually or together, which further improves the performance of the model and makes the model converge faster. The experimental results show that our method achieves 75.2% performance on the aerial image dataset DOTA (OBB task), and saves more than 30% of computing resources. The method also achieves a state-of-the-art performance in HRSC2016, and saved more than 40% of computing resources, which confirms the applicability of the approach. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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21 pages, 9406 KiB  
Article
A Method of Infrared Small Target Detection in Strong Wind Wave Backlight Conditions
by Dongdong Ma, Lili Dong and Wenhai Xu
Remote Sens. 2021, 13(20), 4189; https://doi.org/10.3390/rs13204189 - 19 Oct 2021
Cited by 2 | Viewed by 2488
Abstract
How to accurately detect small targets from the complex maritime environment has been a bottleneck problem. The strong wind-wave backlight conditions (SWWBC) is the most common situation in the process of distress target detection. In order to solve this problem, the main contribution [...] Read more.
How to accurately detect small targets from the complex maritime environment has been a bottleneck problem. The strong wind-wave backlight conditions (SWWBC) is the most common situation in the process of distress target detection. In order to solve this problem, the main contribution of this paper is to propose a small target detection method suitable for SWWBC. First of all, for the purpose of suppressing the gray value of the background, it is analyzed that some minimum points with the lowest gray value tend to gather in the interior of the small target. As the distance from the extreme point increases, the gray value of the pixel in all directions also increases by the same extent. Therefore, an inverse Gaussian difference (IGD) preprocessing method similar to the distribution of the target pixel value is proposed to suppress the uniform sea wave and intensity of the sky background. So as to achieve the purpose of background suppression. Secondly, according to the feature that the small target tends to “ellipse shape” in both horizontal and vertical directions, a multi-scale and multi-directional Gabor filter is applied to filter out interference without “ellipse shape”. Combined with the inter-scale difference (IsD) operation and iterative normalization operator to process the results of the same direction under different scales, it can further suppress the noise interference, highlight the significance of the target, and fuse the processing results to enrich the target information. Then, according to different texture feature distributions of the target and noise in the multi-scale feature fusion results, a cross-correlation (CC) algorithm is proposed to eliminate noise. Finally, according to the dispersion of the number of extreme points and the significance of the intensity of the small target compared with the sea wave and sky noise, a new peak significance remeasurement method is proposed to highlight the intensity of the target and combined with a binary method to achieve accurate target segmentation. In order to better evaluate the performance index of the proposed method, compared with current state-of-art maritime target detection technologies. The experimental results of multiple image sequence sets confirm that the proposed method has higher accuracy, lower false alarm rate, lower complexity, and higher stability. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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19 pages, 2881 KiB  
Article
Multi-Scale Feature Mapping Network for Hyperspectral Image Super-Resolution
by Jing Zhang, Minhao Shao, Zekang Wan and Yunsong Li
Remote Sens. 2021, 13(20), 4180; https://doi.org/10.3390/rs13204180 - 19 Oct 2021
Cited by 12 | Viewed by 2633
Abstract
Hyperspectral Image (HSI) can continuously cover tens or even hundreds of spectral segments for each spatial pixel. Limited by the cost and commercialization requirements of remote sensing satellites, HSIs often lose a lot of information due to insufficient image spatial resolution. For the [...] Read more.
Hyperspectral Image (HSI) can continuously cover tens or even hundreds of spectral segments for each spatial pixel. Limited by the cost and commercialization requirements of remote sensing satellites, HSIs often lose a lot of information due to insufficient image spatial resolution. For the high-dimensional nature of HSIs and the correlation between the spectra, the existing Super-Resolution (SR) methods for HSIs have the problems of excessive parameter amount and insufficient information complementarity between the spectra. This paper proposes a Multi-Scale Feature Mapping Network (MSFMNet) based on the cascaded residual learning to adaptively learn the prior information of HSIs. MSFMNet simplifies each part of the network into a few simple yet effective network modules. To learn the spatial-spectral characteristics among different spectral segments, a multi-scale feature generation and fusion Multi-Scale Feature Mapping Block (MSFMB) based on wavelet transform and spatial attention mechanism is designed in MSFMNet to learn the spectral features between different spectral segments. To effectively improve the multiplexing rate of multi-level spectral features, a Multi-Level Feature Fusion Block (MLFFB) is designed to fuse the multi-level spectral features. In the image reconstruction stage, an optimized sub-pixel convolution module is used for the up-sampling of different spectral segments. Through a large number of verifications on the three general hyperspectral datasets, the superiority of this method compared with the existing hyperspectral SR methods is proved. In subjective and objective experiments, its experimental performance is better than its competitors. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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23 pages, 1473 KiB  
Article
A Robust Adaptive Unscented Kalman Filter for Floating Doppler Wind-LiDAR Motion Correction
by Andreu Salcedo-Bosch, Francesc Rocadenbosch and Joaquim Sospedra
Remote Sens. 2021, 13(20), 4167; https://doi.org/10.3390/rs13204167 - 18 Oct 2021
Cited by 13 | Viewed by 3011
Abstract
This study presents a new method for correcting the six degrees of freedom motion-induced error in ZephIR 300 floating Doppler Wind-LiDAR-derived data, based on a Robust Adaptive Unscented Kalman Filter. The filter takes advantage of the known floating Doppler Wind-LiDAR (FDWL) dynamics, a [...] Read more.
This study presents a new method for correcting the six degrees of freedom motion-induced error in ZephIR 300 floating Doppler Wind-LiDAR-derived data, based on a Robust Adaptive Unscented Kalman Filter. The filter takes advantage of the known floating Doppler Wind-LiDAR (FDWL) dynamics, a velocity–azimuth display algorithm, and a wind model describing the LiDAR-retrieved wind vector without motion influence. The filter estimates the corrected wind vector by adapting itself to different atmospheric and motion scenarios, and by estimating the covariance matrices of related noise processes. The measured turbulence intensity by the FDWL (with and without correction) was compared against a reference fixed LiDAR over a 25-day period at “El Pont del Petroli”, Barcelona. After correction, the apparent motion-induced turbulence was greatly reduced, and the statistical indicators showed overall improvement. Thus, the Mean Difference improved from ?1.70% (uncorrected) to 0.36% (corrected), the Root Mean Square Error (RMSE) improved from 2.01% to 0.86%, and coefficient of determination improved from 0.85 to 0.93. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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18 pages, 5449 KiB  
Article
EFM-Net: Feature Extraction and Filtration with Mask Improvement Network for Object Detection in Remote Sensing Images
by Yu Wang, Yannan Jia and Lize Gu
Remote Sens. 2021, 13(20), 4151; https://doi.org/10.3390/rs13204151 - 16 Oct 2021
Cited by 5 | Viewed by 2934
Abstract
Object detection is an essential task in computer vision. Many methods have made significant progress in ordinary object detection. Due to the particularity of remote sensing images, the detection target is tiny, the background is messy, dense, and has mutual occlusion, which makes [...] Read more.
Object detection is an essential task in computer vision. Many methods have made significant progress in ordinary object detection. Due to the particularity of remote sensing images, the detection target is tiny, the background is messy, dense, and has mutual occlusion, which makes the general detection method challenging to apply to remote sensing images. For these problems, we propose a new detection framework feature extraction and filtration method with a mask improvement network (EFM-Net) to enhance object detection ability. In EFM-Net, we designed a multi-branched feature extraction (MBFE) module to better capture the information in the feature graph. In order to suppress the background interference, we designed a background filtering module based on attention mechanisms to enhance the attention of objects. Finally, we proposed a mask generate the boundary improvement method to make the network more robust to occlusion detection. We tested the DOTA v1.0, NWPU VHR-10, and UCAS-AOD datasets, and the experimental results show that our method has excellent effects. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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27 pages, 29400 KiB  
Article
Hyperspectral Super-Resolution Via Joint Regularization of Low-Rank Tensor Decomposition
by Meng Cao, Wenxing Bao and Kewen Qu
Remote Sens. 2021, 13(20), 4116; https://doi.org/10.3390/rs13204116 - 14 Oct 2021
Cited by 5 | Viewed by 2527
Abstract
The hyperspectral image super-resolution (HSI-SR) problem aims at reconstructing the high resolution spatial–spectral information of the scene by fusing low-resolution hyperspectral images (LR-HSI) and the corresponding high-resolution multispectral image (HR-MSI). In order to effectively preserve the spatial and spectral structure of hyperspectral images, [...] Read more.
The hyperspectral image super-resolution (HSI-SR) problem aims at reconstructing the high resolution spatial–spectral information of the scene by fusing low-resolution hyperspectral images (LR-HSI) and the corresponding high-resolution multispectral image (HR-MSI). In order to effectively preserve the spatial and spectral structure of hyperspectral images, a new joint regularized low-rank tensor decomposition method (JRLTD) is proposed for HSI-SR. This model alleviates the problem that the traditional HSI-SR method, based on tensor decomposition, fails to adequately take into account the manifold structure of high-dimensional HR-HSI and is sensitive to outliers and noise. The model first operates on the hyperspectral data using the classical Tucker decomposition to transform the hyperspectral data into the form of a three-mode dictionary multiplied by the core tensor, after which the graph regularization and unidirectional total variational (TV) regularization are introduced to constrain the three-mode dictionary. In addition, we impose the l1-norm on core tensor to characterize the sparsity. While effectively preserving the spatial and spectral structures in the fused hyperspectral images, the presence of anomalous noise values in the images is reduced. In this paper, the hyperspectral image super-resolution problem is transformed into a joint regularization optimization problem based on tensor decomposition and solved by a hybrid framework between the alternating direction multiplier method (ADMM) and the proximal alternate optimization (PAO) algorithm. Experimental results conducted on two benchmark datasets and one real dataset show that JRLTD shows superior performance over state-of-the-art hyperspectral super-resolution algorithms. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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14 pages, 9076 KiB  
Article
Visual Digital Forest Model Based on a Remote Sensing Data and Forest Inventory Data
by Marsel Vagizov R., Eugenie Istomin P., Valerie Miheev L., Artem Potapov P. and Natalya Yagotinceva V.
Remote Sens. 2021, 13(20), 4092; https://doi.org/10.3390/rs13204092 - 13 Oct 2021
Cited by 9 | Viewed by 4161
Abstract
This article discusses the process of creating a digital forest model based on remote sensing data, three-dimensional modeling, and forest inventory data. Remote sensing data of the Earth provide a fundamental tool for integrating subsequent objects into a digital forest model, enabling the [...] Read more.
This article discusses the process of creating a digital forest model based on remote sensing data, three-dimensional modeling, and forest inventory data. Remote sensing data of the Earth provide a fundamental tool for integrating subsequent objects into a digital forest model, enabling the creation of an accurate digital model of a selected forest quarter by using forest inventory data in educational and experimental forestry, and providing a valuable and extensive database of forest characteristics. The formalization and compilation of technologies for connecting forest inventory databases and remote sensing data with the construction of three-dimensional tree models for a dynamic display of changes in forests provide an additional source of data for obtaining new knowledge. The quality of forest resource management can be improved by obtaining the most accurate details of the current state of forests. Using machine learning and regression analysis methods as part of a digital model, it is possible to visually assess the course of planting growth, changes in species composition, and other morphological characteristics of forests. The goal of digital, interactive forest modeling is to create virtual simulations of the future status of forests using a combination of predictive forest inventory models and machine learning technology. The research findings provide a basic idea and technique for developing local digital forest models based on remote sensing and data integration technologies. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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14 pages, 6547 KiB  
Technical Note
Spatio-Temporal Variations of Precipitable Water Vapor and Horizontal Tropospheric Gradients from GPS during Typhoon Lekima
by Manhong Tu, Weixing Zhang, Jingna Bai, Di Wu, Hong Liang and Yidong Lou
Remote Sens. 2021, 13(20), 4082; https://doi.org/10.3390/rs13204082 - 13 Oct 2021
Cited by 7 | Viewed by 2335
Abstract
GPS data during Typhoon Lekima at 700 stations in China were processed by the Precise Point Positioning (PPP) method. A refined regional Tm model was used to derive the precipitable water vapor (PWV) at these GPS stations. Spatio-temporal variations of PWV with [...] Read more.
GPS data during Typhoon Lekima at 700 stations in China were processed by the Precise Point Positioning (PPP) method. A refined regional Tm model was used to derive the precipitable water vapor (PWV) at these GPS stations. Spatio-temporal variations of PWV with the typhoon process were analyzed. As the typhoon approached, PWV at stations near the typhoon center increased sharply from about 50 mm to nearly 80 mm and then dropped back to about 40–50 mm as the typhoon left. Comparisons of GPS, radiosonde, the Global Data Assimilation System (GDAS) Global Forecast System (GFS) analysis products and ERA5 reanalysis products at four matched GPS-RS stations show overall overestimations of PWV from radiosonde, GFS and ERA5 compared with GPS in a statistical perspective. An empirical orthogonal functions (EOF) analysis of the PWV during the typhoon event revealed some different patterns of variability, with both the first EOF (~36.1% of variance) and second EOF (~30.3% of variance) showing distinctively large anomalies over the typhoon landing locations. The typhoon caused a large horizontal tropospheric gradient (HTG) with the magnitude reaching 5 mm and the direction pointing to the typhoon center when it made a landfall on mainland China. The magnitude and the consistency of the HTG direction decreased overall as the typhoon weakened. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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14 pages, 3704 KiB  
Article
A Fast Storage Method for Drone-Borne Passive Microwave Radiation Measurement
by Xiangkun Wan, Xiaofeng Li, Tao Jiang, Xingming Zheng, Xiaojie Li and Lei Li
Sensors 2021, 21(20), 6767; https://doi.org/10.3390/s21206767 - 12 Oct 2021
Cited by 2 | Viewed by 2039
Abstract
A drone-borne microwave radiometer requires a high sampling frequency and a continuous acquisition capability to detect and mitigate radio frequency interference (RFI), but existing methods cannot store such a large amount of data. In this paper, the dual polling write method (DPSM) for [...] Read more.
A drone-borne microwave radiometer requires a high sampling frequency and a continuous acquisition capability to detect and mitigate radio frequency interference (RFI), but existing methods cannot store such a large amount of data. In this paper, the dual polling write method (DPSM) for secure digital cards triggered by a timer under a multitask framework based on STM32 MCU is proposed to meet the requirements of continuous data storage. The card programming step was changed from a query waiting structure to a polling query flag bit structure, and time-sharing processing and parallel processing were used to simulate multithreading. The experimental results were as follows: (1) the time consumption of the whole storage procedure was reduced from 4000 microseconds to 200–400 microseconds; (2) the time consumption of the card programming step was reduced from 3000 microseconds in the first block and 1000 microseconds in the second and subsequent blocks to 17–174 microseconds and 18–71 microseconds, respectively, compared with the existing method; (3) the delay in the whole sampling cycle was reduced from 3942 microseconds to 0 microseconds. The results of this paper can meet the data storage requirements of a drone-borne microwave radiometer and be applied to the high-speed storage of other devices. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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13 pages, 5378 KiB  
Article
Research on the Principle and Cooperative Processing Method of MRS Multisystem Joint Detection
by Cong Li, Zhaofa Zeng, Zhuo Wang and Xiaofeng Yi
Sensors 2021, 21(20), 6725; https://doi.org/10.3390/s21206725 - 10 Oct 2021
Viewed by 2151
Abstract
Magnetic resonance sounding (MRS) technology is the only geophysical means to directly and quantitatively detect groundwater and has achieved good results in hydrogeological prospecting applications. In recent years, researchers have conducted considerable research on the efficiency of a single instrument, yielding certain results. [...] Read more.
Magnetic resonance sounding (MRS) technology is the only geophysical means to directly and quantitatively detect groundwater and has achieved good results in hydrogeological prospecting applications. In recent years, researchers have conducted considerable research on the efficiency of a single instrument, yielding certain results. However, the overall work efficiency of this method has not been effectively determined in its application to a large-scale survey. Hence, we propose both a joint detection method for MRS that determines the minimum working distance when multiple systems operate simultaneously and a collaborative measurement method of dual systems operating simultaneously in a fixed range of work areas. The cooperative working mode of the instruments is tested in the detection area, and the working mode proposed in this paper is shown to effectively avoid measurement interference between systems. Compared with the working mode of a single set of instruments, the measurement efficiency is more than doubled. Through this research, the feasibility of multiple MRS instruments working together in the same work area is verified, which provides effective technical support for the rapid and high-efficiency utilization of MRS over a wide range of measurement areas. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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22 pages, 8917 KiB  
Article
Gap-Filling of NDVI Satellite Data Using Tucker Decomposition: Exploiting Spatio-Temporal Patterns
by Andri Freyr Þórðarson, Andreas Baum, Mónica García, Sergio M. Vicente-Serrano and Anders Stockmarr
Remote Sens. 2021, 13(19), 4007; https://doi.org/10.3390/rs13194007 - 6 Oct 2021
Cited by 8 | Viewed by 3621
Abstract
Remote sensing satellite images in the optical domain often contain missing or misleading data due to overcast conditions or sensor malfunctioning, concealing potentially important information. In this paper, we apply expectation maximization (EM) Tucker to NDVI satellite data from the Iberian Peninsula in [...] Read more.
Remote sensing satellite images in the optical domain often contain missing or misleading data due to overcast conditions or sensor malfunctioning, concealing potentially important information. In this paper, we apply expectation maximization (EM) Tucker to NDVI satellite data from the Iberian Peninsula in order to gap-fill missing information. EM Tucker belongs to a family of tensor decomposition methods that are known to offer a number of interesting properties, including the ability to directly analyze data stored in multidimensional arrays and to explicitly exploit their multiway structure, which is lost when traditional spatial-, temporal- and spectral-based methods are used. In order to evaluate the gap-filling accuracy of EM Tucker for NDVI images, we used three data sets based on advanced very-high resolution radiometer (AVHRR) imagery over the Iberian Peninsula with artificially added missing data as well as a data set originating from the Iberian Peninsula with natural missing data. The performance of EM Tucker was compared to a simple mean imputation, a spatio-temporal hybrid method, and an iterative method based on principal component analysis (PCA). In comparison, imputation of the missing data using EM Tucker consistently yielded the most accurate results across the three simulated data sets, with levels of missing data ranging from 10 to 90%. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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19 pages, 8416 KiB  
Article
A Fast Hyperspectral Anomaly Detection Algorithm Based on Greedy Bilateral Smoothing and Extended Multi-Attribute Profile
by Senhao Liu, Lifu Zhang, Yi Cen, Likun Chen and Yibo Wang
Remote Sens. 2021, 13(19), 3954; https://doi.org/10.3390/rs13193954 - 2 Oct 2021
Cited by 2 | Viewed by 2505
Abstract
To address the difficulty of separating background materials from similar materials associated with the use of “single-spectral information” for hyperspectral anomaly detection, a fast hyperspectral anomaly detection algorithm based on what we term the “greedy bilateral smoothing and extended multi-attribute profile” (GBSAED) method [...] Read more.
To address the difficulty of separating background materials from similar materials associated with the use of “single-spectral information” for hyperspectral anomaly detection, a fast hyperspectral anomaly detection algorithm based on what we term the “greedy bilateral smoothing and extended multi-attribute profile” (GBSAED) method is proposed to improve detection precision and operation efficiency. This method utilizes “greedy bilateral smoothing” to decompose the low-rank part of a hyperspectral image (HSI) dataset and calculate spectral anomalies. This process improves the operational efficiency. Then, the extended multi-attribute profile is used to extract spatial anomalies and restrict the shape of anomalies. Finally, the two components are combined to limit false alarms and obtain appropriate detection results. This new method considers both spectral and spatial information with an improved structure that ensures operational efficiency. Using five real HSI datasets, this study demonstrates that the GBSAED method is more robust than eight representative algorithms under diverse application scenarios and greatly improves detection precision and operational efficiency. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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23 pages, 86149 KiB  
Article
Feasibility of the Spatiotemporal Fusion Model in Monitoring Ebinur Lake’s Suspended Particulate Matter under the Missing-Data Scenario
by Changjiang Liu, Pan Duan, Fei Zhang, Chi-Yung Jim, Mou Leong Tan and Ngai Weng Chan
Remote Sens. 2021, 13(19), 3952; https://doi.org/10.3390/rs13193952 - 2 Oct 2021
Cited by 17 | Viewed by 3120
Abstract
High-frequency monitoring of suspended particulate matter (SPM) concentration can improve water resource management. Missing high-resolution satellite images could hamper remote-sensing SPM monitoring. This study resolved the problem by applying spatiotemporal fusion technology to obtain high spatial resolution and dense time-series data to fill [...] Read more.
High-frequency monitoring of suspended particulate matter (SPM) concentration can improve water resource management. Missing high-resolution satellite images could hamper remote-sensing SPM monitoring. This study resolved the problem by applying spatiotemporal fusion technology to obtain high spatial resolution and dense time-series data to fill image-data gaps. Three data sources (MODIS, Landsat 8, and Sentinel 2) and two spatiotemporal fusion methods (the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data fusion (FSDAF)) were used to reconstruct missing satellite images. We compared their fusion accuracy and verified the consistency of fusion images between data sources. For the fusion images, we used random forest (RF) and XGBoost as inversion methods and set “fusion first” and “inversion first” strategies to test the method’s feasibility in Ebinur Lake, Xinjiang, arid northwestern China. Our results showed that (1) the blue, green, red, and NIR bands of ESTARFM fusion image were better than FSDAF, with a good consistency (R2 ? 0.54) between the fused Landsat 8, Sentinel 2 images, and their original images; (2) the original image and fusion image offered RF inversion effect better than XGBoost. The inversion accuracy based on Landsat 8 and Sentinel 2 were R2 0.67 and 0.73, respectively. The correlation of SPM distribution maps of the two data sources attained a good consistency of R2 0.51; (3) in retrieving SPM from fused images, the “fusion first” strategy had better accuracy. The optimal combination was ESTARFM (Landsat 8)_RF and ESTARFM (Sentinel 2)_RF, consistent with original SPM maps (R2 = 0.38, 0.41, respectively). Overall, the spatiotemporal fusion model provided effective SPM monitoring under the image-absence scenario, with good consistency in the inversion of SPM. The findings provided the research basis for long-term and high-frequency remote-sensing SPM monitoring and high-precision smart water resource management. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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21 pages, 2255 KiB  
Article
Bi-Spectral Infrared Algorithm for Cloud Coverage over Oceans by the JEM-EUSO Mission Program
by David Santalices, Susana Briz, Antonio J. de Castro and Fernando López
Sensors 2021, 21(19), 6506; https://doi.org/10.3390/s21196506 - 29 Sep 2021
Cited by 1 | Viewed by 2069
Abstract
The need to monitor specific areas for different applications requires high spatial and temporal resolution. This need has led to the proliferation of ad hoc systems on board nanosatellites, drones, etc. These systems require low cost, low power consumption, and low weight. The [...] Read more.
The need to monitor specific areas for different applications requires high spatial and temporal resolution. This need has led to the proliferation of ad hoc systems on board nanosatellites, drones, etc. These systems require low cost, low power consumption, and low weight. The work we present follows this trend. Specifically, this article evaluates a method to determine the cloud map from the images provided by a simple bi-spectral infrared camera within the framework of JEM-EUSO (The Joint Experiment Missions-Extrem Universe Space Observatory). This program involves different experiments whose aim is determining properties of Ultra-High Energy Cosmic Ray (UHECR) via the detection of atmospheric fluorescence light. Since some of those projects use UV instruments on board space platforms, they require knowledge of the cloudiness state in the FoV of the instrument. For that reason, some systems will include an infrared (IR) camera. This study presents a test to generate a binary cloudiness mask (CM) over the ocean, employing bi-spectral IR data. The database is created from Moderate-Resolution Imaging Spectroradiometer (MODIS) data (bands 31 and 32). The CM is based on a split-window algorithm. It uses an estimation of the brightness temperature calculated from a statistical study of an IR images database along with an ancillary sea surface temperature. This statistical procedure to obtain the estimate of the brightness temperature is one of the novel contributions of this work. The difference between the measured and estimation of the brightness temperature determines whether a pixel is cover or clear. That classification requires defining several thresholds which depend on the scenarios. The procedure for determining those thresholds is also novel. Then, the results of the algorithm are compared with the MODIS CM. The agreement is above 90%. The performance of the proposed CM is similar to that of other studies. The validation also shows that cloud edges concentrate the vast majority of discrepancies with the MODIS CM. The relatively high accuracy of the algorithm is a relevant result for the JEM-EUSO program. Further work will combine the proposed algorithm with complementary studies in the framework of JEM-EUSO to reinforce the CM above the cloud edges. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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12 pages, 2920 KiB  
Communication
Airborne SAR Autofocus Based on Blurry Imagery Classification
by Jianlai Chen, Hanwen Yu, Gang Xu, Junchao Zhang, Buge Liang and Degui Yang
Remote Sens. 2021, 13(19), 3872; https://doi.org/10.3390/rs13193872 - 27 Sep 2021
Cited by 10 | Viewed by 2724
Abstract
Existing airborne SAR autofocus methods can be classified as parametric and non-parametric. Generally, non-parametric methods, such as the widely used phase gradient autofocus (PGA) algorithm, are only suitable for scenes with many dominant point targets, while the parametric ones are suitable for all [...] Read more.
Existing airborne SAR autofocus methods can be classified as parametric and non-parametric. Generally, non-parametric methods, such as the widely used phase gradient autofocus (PGA) algorithm, are only suitable for scenes with many dominant point targets, while the parametric ones are suitable for all types of scenes, in theory, but their efficiency is generally low. In practice, whether many dominant point targets are present in the scene is usually unknown, so determining what kind of algorithm should be selected is not straightforward. To solve this issue, this article proposes an airborne SAR autofocus approach combined with blurry imagery classification to improve the autofocus efficiency for ensuring autofocus precision. In this approach, we embed the blurry imagery classification based on a typical VGGNet in a deep learning community into the traditional autofocus framework as a preprocessing step before autofocus processing to analyze whether dominant point targets are present in the scene. If many dominant point targets are present in the scene, the non-parametric method is used for autofocus processing. Otherwise, the parametric one is adopted. Therefore, the advantage of the proposed approach is the automatic batch processing of all kinds of airborne measured data. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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21 pages, 31158 KiB  
Article
Solar Contamination on HIRAS Cold Calibration View and the Corrected Radiance Assessment
by Lu Lee, Chunqiang Wu, Chengli Qi, Xiuqing Hu, Mingge Yuan, Mingjian Gu, Chunyuan Shao and Peng Zhang
Remote Sens. 2021, 13(19), 3869; https://doi.org/10.3390/rs13193869 - 27 Sep 2021
Cited by 2 | Viewed by 1896
Abstract
The deep-space (DS) view spectra are used as a cold reference to calibrate the Hyperspectral Infrared Atmospheric Sounder (HIRAS) Earth scene (ES) observations. The DS spectra stability in the moving average window is crucial to the calibration accuracy of ES radiances. While in [...] Read more.
The deep-space (DS) view spectra are used as a cold reference to calibrate the Hyperspectral Infrared Atmospheric Sounder (HIRAS) Earth scene (ES) observations. The DS spectra stability in the moving average window is crucial to the calibration accuracy of ES radiances. While in the winter and spring seasons, the HIRAS detector-3 DS view is susceptible to solar stray light intrusion when the satellite flies towards the tail of every descending orbit, and as a result, the measured DS spectra are contaminated by the stray light pseudo spectra, especially in the short-wave infrared (SWIR) band. The solar light intrusion issue was addressed on 13 December 2019 when the DS view angle of the scene selection mirror (SSM) was adjusted from ?77.4° to ?87°. As for the historic contaminated data, a correction method is applied to detect the anomalous data by checking the continuity of the DS spectra and then replace them with the proximate normal ones. The historic ES observations are recalibrated after the contaminated DS spectra correction. The effect of the correction is assessed by comparing the recalibrated HIRAS radiances with those measured by the Cross-track Infrared Sounder onboard the Suomi National Polar-orbiting Partnership Satellite (SNPP/CrIS) via the extended simultaneous nadir overpasses (SNOx) technique and by checking the consistency among the radiance data from different HIRAS detectors. The results show that the large biases of the radiance brightness temperature (BT) caused by the contamination are ameliorated greatly to the levels observed in the normal conditions. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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25 pages, 9340 KiB  
Article
Change Detection Using a Texture Feature Space Outlier Index from Mono-Temporal Remote Sensing Images and Vector Data
by Dongsheng Wei, Dongyang Hou, Xiaoguang Zhou and Jun Chen
Remote Sens. 2021, 13(19), 3857; https://doi.org/10.3390/rs13193857 - 26 Sep 2021
Cited by 10 | Viewed by 4930
Abstract
Multi-temporal remote sensing images are the primary sources for change detection. However, it is difficult to obtain comparable multi-temporal images at the same season and time of day with the same sensor. Considering texture homogeneity among objects belonging to the same category, this [...] Read more.
Multi-temporal remote sensing images are the primary sources for change detection. However, it is difficult to obtain comparable multi-temporal images at the same season and time of day with the same sensor. Considering texture homogeneity among objects belonging to the same category, this paper presents a new change detection approach using a texture feature space outlier index from mono-temporal remote sensing images and vector data. In the proposed approach, a texture feature contribution index (TFCI) is defined based on information gain to select the optimal texture features, and a feature space outlier index (FSOI) based on local reachability density is presented to automatically identify outlier samples and changed objects. Our approach includes three steps: (1) the sampling method is designed considering spatial distribution and topographic properties of image objects extracted by segmenting the recent image with existing vector map. (2) Samples with changed categories are refined by an iteration procedure of texture feature selection and outlier sample elimination; and (3) the changed image objects are identified and classified using the refined samples to calculate the FSOI values of the image objects. Three experiments in the two study areas were conducted to validate its performance. Overall accuracies of 95.94%, 96.36%, and 96.28% were achieved, respectively, while the omission and commission errors for every category were all very low. Four widely used methods with two-temporal images were selected for comparison, and the accuracy of the proposed method is higher than theirs. This indicates that our approach is effective and feasible. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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11 pages, 42611 KiB  
Communication
Early-Season Mapping of Winter Crops Using Sentinel-2 Optical Imagery
by Haifeng Tian, Yongjiu Wang, Ting Chen, Lijun Zhang and Yaochen Qin
Remote Sens. 2021, 13(19), 3822; https://doi.org/10.3390/rs13193822 - 24 Sep 2021
Cited by 75 | Viewed by 4459
Abstract
Sentinel-2 imagery is an unprecedented data source with high spatial, spectral and temporal resolution in addition to free access. The objective of this paper was to evaluate the potential of using Sentinel-2 data to map winter crops in the early growth stage. Analysis [...] Read more.
Sentinel-2 imagery is an unprecedented data source with high spatial, spectral and temporal resolution in addition to free access. The objective of this paper was to evaluate the potential of using Sentinel-2 data to map winter crops in the early growth stage. Analysis of three winter crop types—winter garlic, winter canola and winter wheat—was carried out in two agricultural regions of China. We analysed the spectral characteristics and vegetation index profiles of these crops in the early growth stage and other land cover types based on Sentinel-2 images. A decision tree classification model was built to distinguish the crops based on these data. The results demonstrate that winter garlic and winter wheat can be distinguished four months before harvest, while winter canola can be distinguished two months before harvest. The overall classification accuracy was 96.62% with a kappa coefficient of 0.95. Therefore, Sentinel-2 images can be used to accurately identify these winter crops in the early growth stage, making them an important data source in the field of agricultural remote sensing. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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18 pages, 13944 KiB  
Article
A Coarse-to-Fine Contour Optimization Network for Extracting Building Instances from High-Resolution Remote Sensing Imagery
by Fang Fang, Kaishun Wu, Yuanyuan Liu, Shengwen Li, Bo Wan, Yanling Chen and Daoyuan Zheng
Remote Sens. 2021, 13(19), 3814; https://doi.org/10.3390/rs13193814 - 23 Sep 2021
Cited by 13 | Viewed by 3619
Abstract
Building instances extraction is an essential task for surveying and mapping. Challenges still exist in extracting building instances from high-resolution remote sensing imagery mainly because of complex structures, variety of scales, and interconnected buildings. This study proposes a coarse-to-fine contour optimization network to [...] Read more.
Building instances extraction is an essential task for surveying and mapping. Challenges still exist in extracting building instances from high-resolution remote sensing imagery mainly because of complex structures, variety of scales, and interconnected buildings. This study proposes a coarse-to-fine contour optimization network to improve the performance of building instance extraction. Specifically, the network contains two special sub-networks: attention-based feature pyramid sub-network (AFPN) and coarse-to-fine contour sub-network. The former sub-network introduces channel attention into each layer of the original feature pyramid network (FPN) to improve the identification of small buildings, and the latter is designed to accurately extract building contours via two cascaded contour optimization learning. Furthermore, the whole network is jointly optimized by multiple losses, that is, a contour loss, a classification loss, a box regression loss and a general mask loss. Experimental results on three challenging building extraction datasets demonstrated that the proposed method outperformed the state-of-the-art methods’ accuracy and quality of building contours. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
(This article belongs to the Section AI Remote Sensing)
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18 pages, 1774 KiB  
Article
Dual Attention Feature Fusion and Adaptive Context for Accurate Segmentation of Very High-Resolution Remote Sensing Images
by Hao Shi, Jiahe Fan, Yupei Wang and Liang Chen
Remote Sens. 2021, 13(18), 3715; https://doi.org/10.3390/rs13183715 - 17 Sep 2021
Cited by 12 | Viewed by 2805
Abstract
Land cover classification of high-resolution remote sensing images aims to obtain pixel-level land cover understanding, which is often modeled as semantic segmentation of remote sensing images. In recent years, convolutional network (CNN)-based land cover classification methods have achieved great advancement. However, previous methods [...] Read more.
Land cover classification of high-resolution remote sensing images aims to obtain pixel-level land cover understanding, which is often modeled as semantic segmentation of remote sensing images. In recent years, convolutional network (CNN)-based land cover classification methods have achieved great advancement. However, previous methods fail to generate fine segmentation results, especially for the object boundary pixels. In order to obtain boundary-preserving predictions, we first propose to incorporate spatially adapting contextual cues. In this way, objects with similar appearance can be effectively distinguished with the extracted global contextual cues, which are very helpful to identify pixels near object boundaries. On this basis, low-level spatial details and high-level semantic cues are effectively fused with the help of our proposed dual attention mechanism. Concretely, when fusing multi-level features, we utilize the dual attention feature fusion module based on both spatial and channel attention mechanisms to relieve the influence of the large gap, and further improve the segmentation accuracy of pixels near object boundaries. Extensive experiments were carried out on the ISPRS 2D Semantic Labeling Vaihingen data and GaoFen-2 data to demonstrate the effectiveness of our proposed method. Our method achieves better performance compared with other state-of-the-art methods. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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20 pages, 10334 KiB  
Article
New Channel Errors Estimation Method for Multichannel SAR Based on Virtual Calibration Source
by Zhen Liang, Xikai Fu and Xiaolei Lv
Remote Sens. 2021, 13(18), 3625; https://doi.org/10.3390/rs13183625 - 11 Sep 2021
Cited by 3 | Viewed by 2316
Abstract
The multichannel synthetic aperture radar (SAR) system can effectively overcome the fundamental limitation between high-resolution and wide-swath. However, the unavoidable channel errors will result in a mismatch of the reconstruction filter and false targets in pairs. To address this issue, a novel channel [...] Read more.
The multichannel synthetic aperture radar (SAR) system can effectively overcome the fundamental limitation between high-resolution and wide-swath. However, the unavoidable channel errors will result in a mismatch of the reconstruction filter and false targets in pairs. To address this issue, a novel channel errors calibration method is proposed based on the idea of minimizing the mean square error (MMSE) between the signal subspace and the space spanned by the practical steering vectors. The practical steering matrix of each Doppler bin can be constructed according to the Doppler spectrum. Compared with the time-domain correlation method, the proposed method no longer depends on the accuracy of the Doppler centroid estimation. Besides, compared with the orthogonal subspace method, the proposed method has the advantage of robustness under the condition of large samples by using the diagonal loading technique. To evaluate the performance, the results of simulation data and the real data acquired by the GF-3 dual-channel SAR system demonstrate that the proposed method has higher accuracy and more robustness than the conventional methods, especially in the case of low SNRs and high non-uniformity. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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15 pages, 953 KiB  
Technical Note
Micro-Motion Parameter Extraction for Ballistic Missile with Wideband Radar Using Improved Ensemble EMD Method
by Nannan Zhu, Jun Hu, Shiyou Xu, Wenzhen Wu, Yunfan Zhang and Zengping Chen
Remote Sens. 2021, 13(17), 3545; https://doi.org/10.3390/rs13173545 - 6 Sep 2021
Cited by 16 | Viewed by 3130
Abstract
Micro-motion parameters extraction is crucial in recognizing ballistic missiles with a wideband radar. It is known that the phase-derived range (PDR) method can provide a sub-wavelength level accuracy. However, it is sensitive and unstable when the signal-to-noise ratio (SNR) is low. In this [...] Read more.
Micro-motion parameters extraction is crucial in recognizing ballistic missiles with a wideband radar. It is known that the phase-derived range (PDR) method can provide a sub-wavelength level accuracy. However, it is sensitive and unstable when the signal-to-noise ratio (SNR) is low. In this paper, an improved PDR method is proposed to reduce the impacts of low SNRs. First, the high range resolution profile (HRRP) is divided into a series of segments so that each segment contains a single scattering point. Then, the peak values of each segment are viewed as non-stationary signals, which are further decomposed into a series of intrinsic mode functions (IMFs) with different energy, using the ensemble empirical mode decomposition with the complementary adaptive noise (EEMDCAN) method. In the EEMDCAN decomposition, positive and negative adaptive noise pairs are added to each IMF layer to effectively eliminate the mode-mixing phenomenon that exists in the original empirical mode decomposition (EMD) method. An energy threshold is designed to select proper IMFs to reconstruct the envelop for high estimation accuracy and low noise effects. Finally, the least-square algorithm is used to do the ambiguous phases unwrapping to obtain the micro-curve, which can be further used to estimate the micro-motion parameters of the warhead. Simulation results show that the proposed method performs well with SNR at ?5 dB with an accuracy level of sub-wavelength. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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22 pages, 1483 KiB  
Article
A Spatial Variant Motion Compensation Algorithm for High-Monofrequency Motion Error in Mini-UAV-Based BiSAR Systems
by Zhanze Wang, Feifeng Liu, Simin He and Zhixiang Xu
Remote Sens. 2021, 13(17), 3544; https://doi.org/10.3390/rs13173544 - 6 Sep 2021
Cited by 3 | Viewed by 2389
Abstract
High-frequency motion errors can drastically decrease the image quality in mini-unmanned-aerial-vehicle (UAV)-based bistatic synthetic aperture radar (BiSAR), where the spatial variance is much more complex than that in monoSAR. High-monofrequency motion error is a special BiSAR case in which the different motion errors [...] Read more.
High-frequency motion errors can drastically decrease the image quality in mini-unmanned-aerial-vehicle (UAV)-based bistatic synthetic aperture radar (BiSAR), where the spatial variance is much more complex than that in monoSAR. High-monofrequency motion error is a special BiSAR case in which the different motion errors from transmitters and receivers lead to the formation of monofrequency motion error. Furthermore, neither of the classic processors, BiSAR and monoSAR, can compensate for the coupled high-monofrequency motion errors. In this paper, a spatial variant motion compensation algorithm for high-monofrequency motion errors is proposed. First, the bistatic rotation error model that causes high-monofrequency motion error is re-established to account for the bistatic spatial variance of image formation. Second, the corresponding parameters of error model nonlinear gradient are obtained by the joint estimation of subimages. Third, the bistatic spatial variance can be adaptively compensated for based on the error of the nonlinear gradient through contour projection. It is suggested based on the simulation and experimental results that the proposed algorithm can effectively compensate for high-monofrequency motion error in mini-UAV-based BiSAR system conditions. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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27 pages, 8624 KiB  
Article
An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques
by Thomas P. F. Dowling, Peilin Song, Mark C. De Jong, Lutz Merbold, Martin J. Wooster, Jingfeng Huang and Yongqiang Zhang
Remote Sens. 2021, 13(17), 3522; https://doi.org/10.3390/rs13173522 - 5 Sep 2021
Cited by 9 | Viewed by 4031
Abstract
Satellite-derived land surface temperature (LST) data are most commonly observed in the longwave infrared (LWIR) spectral region. However, such data suffer frequent gaps in coverage caused by cloud cover. Filling these ‘cloud gaps’ usually relies on statistical re-constructions using proximal clear sky LST [...] Read more.
Satellite-derived land surface temperature (LST) data are most commonly observed in the longwave infrared (LWIR) spectral region. However, such data suffer frequent gaps in coverage caused by cloud cover. Filling these ‘cloud gaps’ usually relies on statistical re-constructions using proximal clear sky LST pixels, whilst this is often a poor surrogate for shadowed LSTs insulated under cloud. Another solution is to rely on passive microwave (PM) LST data that are largely unimpeded by cloud cover impacts, the quality of which, however, is limited by the very coarse spatial resolution typical of PM signals. Here, we combine aspects of these two approaches to fill cloud gaps in the LWIR-derived LST record, using Kenya (East Africa) as our study area. The proposed “cloud gap-filling” approach increases the coverage of daily Aqua MODIS LST data over Kenya from <50% to >90%. Evaluations were made against the in situ and SEVIRI-derived LST data respectively, revealing root mean square errors (RMSEs) of 2.6 K and 3.6 K for the proposed method by mid-day, compared with RMSEs of 4.3 K and 6.7 K for the conventional proximal-pixel-based statistical re-construction method. We also find that such accuracy improvements become increasingly apparent when the total cloud cover residence time increases in the morning-to-noon time frame. At mid-night, cloud gap-filling performance is also better for the proposed method, though the RMSE improvement is far smaller (<0.3 K) than in the mid-day period. The results indicate that our proposed two-step cloud gap-filling method can improve upon performances achieved by conventional methods for cloud gap-filling and has the potential to be scaled up to provide data at continental or global scales as it does not rely on locality-specific knowledge or datasets. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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29 pages, 37593 KiB  
Article
Observation of Surface Displacement Associated with Rapid Urbanization and Land Creation in Lanzhou, Loess Plateau of China with Sentinel-1 SAR Imagery
by Yuming Wei, Xiaojie Liu, Chaoying Zhao, Roberto Tomás and Zhuo Jiang
Remote Sens. 2021, 13(17), 3472; https://doi.org/10.3390/rs13173472 - 1 Sep 2021
Cited by 8 | Viewed by 3128
Abstract
Lanzhou is one of the cities with the higher number of civil engineering projects for mountain excavation and city construction (MECC) on the China’s Loess Plateau. As a result, the city is suffering from severe surface displacement, which is posing an increasing threat [...] Read more.
Lanzhou is one of the cities with the higher number of civil engineering projects for mountain excavation and city construction (MECC) on the China’s Loess Plateau. As a result, the city is suffering from severe surface displacement, which is posing an increasing threat to the safety of the buildings. However, up to date, there is no comprehensive and high-precision displacement map to characterize the spatiotemporal surface displacement patterns in the city of Lanzhou. In this study, satellite-based observations, including optical remote sensing and synthetic aperture radar (SAR) sensing, were jointly used to characterize the landscape and topography changes in Lanzhou between 1997 and 2020 and investigate the spatiotemporal patterns of the surface displacement associated with the large-scale MECC projects from 2015 December to March 2021. First, we retrieved the landscape changes in Lanzhou during the last 23 years using multi-temporal optical remote sensing images. Results illustrate that the landscape in local areas of Lanzhou has been dramatically changed as a result of the large-scale MECC projects and rapid urbanization. Then, we optimized the ordinary time series InSAR processing procedure by a “dynamic estimation of digital elevation model (DEM) errors” step added before displacement inversion to avoid the false displacement signals caused by DEM errors. The DEM errors and the high-precision surface displacement maps between December 2015 and March 2021 were calculated with 124 ascending and 122 descending Sentinel-1 SAR images. By combining estimated DEM errors and optical images, we detected and mapped historical MECC areas in the study area since 2000, retrieved the excavated and filling areas of the MECC projects, and evaluated their areas and volumes as well as the thickness of the filling loess. Results demonstrated that the area and volume of the excavated regions were basically equal to that of the filling regions, and the maximum thickness of the filling loess was greater than 90 m. Significant non-uniform surface displacements were observed in the filling regions of the MECC projects, with the maximum cumulative displacement lower than ?40 cm. 2D displacement results revealed that surface displacement associated with the MECC project was dominated by settlements. From the correlation analysis between the displacement and the filling thickness, we found that the displacement magnitude was positively correlated with the thickness of the filling loess. This finding indicated that the compaction and consolidation process of the filling loess largely dominated the surface displacement. Our findings are of paramount importance for the urban planning and construction on the Loess Plateau region in which large-scale MECC projects are being developed. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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22 pages, 7582 KiB  
Article
Cross-Domain Scene Classification Based on a Spatial Generalized Neural Architecture Search for High Spatial Resolution Remote Sensing Images
by Yuling Chen, Wentao Teng, Zhen Li, Qiqi Zhu and Qingfeng Guan
Remote Sens. 2021, 13(17), 3460; https://doi.org/10.3390/rs13173460 - 1 Sep 2021
Cited by 5 | Viewed by 3164
Abstract
By labelling high spatial resolution (HSR) images with specific semantic classes according to geographical properties, scene classification has been proven to be an effective method for HSR remote sensing image semantic interpretation. Deep learning is widely applied in HSR remote sensing scene classification. [...] Read more.
By labelling high spatial resolution (HSR) images with specific semantic classes according to geographical properties, scene classification has been proven to be an effective method for HSR remote sensing image semantic interpretation. Deep learning is widely applied in HSR remote sensing scene classification. Most of the scene classification methods based on deep learning assume that the training datasets and the test datasets come from the same datasets or obey similar feature distributions. However, in practical application scenarios, it is difficult to guarantee this assumption. For new datasets, it is time-consuming and labor-intensive to repeat data annotation and network design. The neural architecture search (NAS) can automate the process of redesigning the baseline network. However, traditional NAS lacks the generalization ability to different settings and tasks. In this paper, a novel neural network search architecture framework—the spatial generalization neural architecture search (SGNAS) framework—is proposed. This model applies the NAS of spatial generalization to cross-domain scene classification of HSR images to bridge the domain gap. The proposed SGNAS can automatically search the architecture suitable for HSR image scene classification and possesses network design principles similar to the manually designed networks. To obtain a simple and low-dimensional search space, the traditional NAS search space was optimized and the human-the-loop method was used. To extend the optimized search space to different tasks, the search space was generalized. The experimental results demonstrate that the network searched by the SGNAS framework with good generalization ability displays its effectiveness for cross-domain scene classification of HSR images, both in accuracy and time efficiency. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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20 pages, 5302 KiB  
Article
High Speed Maneuvering Platform Squint TOPS SAR Imaging Based on Local Polar Coordinate and Angular Division
by Bowen Bie, Yinghui Quan, Kaijie Xu, Guangcai Sun and Mengdao Xing
Remote Sens. 2021, 13(16), 3329; https://doi.org/10.3390/rs13163329 - 23 Aug 2021
Cited by 1 | Viewed by 2096
Abstract
This paper proposes an imaging algorithm for synthetic aperture radar (SAR) mounted on a high-speed maneuvering platform with squint terrain observation by progressive scan mode. To overcome the mismatch between range model and the signal after range walk correction, the range history is [...] Read more.
This paper proposes an imaging algorithm for synthetic aperture radar (SAR) mounted on a high-speed maneuvering platform with squint terrain observation by progressive scan mode. To overcome the mismatch between range model and the signal after range walk correction, the range history is calculated in local polar format. The Doppler ambiguity is resolved by nonlinear derotation and zero-padding. The recovered signal is divided into several blocks in Doppler according to the angular division. Keystone transform is used to remove the space-variant range cell migration (RCM) components. Thus, the residual RCM terms can be compensated by a unified phase function. Frequency domain perturbation terms are introduced to correct the space-variant Doppler chirp rate term. The focusing parameters are calculated according to the scene center of each angular block and the signal of each block can be processed in parallel. The image of each block is focused in range-Doppler domain. After the geometric correction, the final focused image can be obtained by directly combined the images of all angular blocks. Simulated SAR data has verified the effectiveness of the proposed algorithm. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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20 pages, 3433 KiB  
Article
Investigation of Thundercloud Features in Different Regions
by Andrei Sin’kevich, Bruce Boe, Sunil Pawar, Jing Yang, Ali Abshaev, Yulia Dovgaluk, Julduz Gekkieva, Venkatachalam Gopalakrishnan, Alexander Kurov, Yurii Mikhailovskii, Marina Toropova and Nikolai Veremei
Remote Sens. 2021, 13(16), 3216; https://doi.org/10.3390/rs13163216 - 13 Aug 2021
Cited by 8 | Viewed by 2393
Abstract
A comparison of thundercloud characteristics in different regions of the world was conducted. The clouds studied developed in India, China and in two regions of Russia. Several field projects were discussed. Cloud characteristics were measured by weather radars, the SEVERI instrument installed on [...] Read more.
A comparison of thundercloud characteristics in different regions of the world was conducted. The clouds studied developed in India, China and in two regions of Russia. Several field projects were discussed. Cloud characteristics were measured by weather radars, the SEVERI instrument installed on board of the Meteosat satellite, and lightning detection systems. The statistical characteristics of the clouds were tabulated from radar scans and correlated with lightning observations. Thunderclouds in India differ significantly from those observed in other regions. The relationships among lightning strike frequency, supercooled cloud volume, and precipitation intensity were analyzed. In most cases, high correlation was observed between lightning strike frequency and supercooled volume. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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14 pages, 1715 KiB  
Article
Unsupervised Reconstruction of Sea Surface Currents from AIS Maritime Traffic Data Using Trainable Variational Models
by Simon Benaïchouche, Clément Legoff, Yann Guichoux, François Rousseau and Ronan Fablet
Remote Sens. 2021, 13(16), 3162; https://doi.org/10.3390/rs13163162 - 10 Aug 2021
Cited by 5 | Viewed by 3460
Abstract
The estimation of ocean dynamics is a key challenge for applications ranging from climate modeling to ship routing. State-of-the-art methods relying on satellite-derived altimetry data can hardly resolve spatial scales below ?100 km. In this work we investigate the relevance of AIS data [...] Read more.
The estimation of ocean dynamics is a key challenge for applications ranging from climate modeling to ship routing. State-of-the-art methods relying on satellite-derived altimetry data can hardly resolve spatial scales below ?100 km. In this work we investigate the relevance of AIS data streams as a new mean for the estimation of the surface current velocities. Using a physics-informed observation model, we propose to solve the associated the ill-posed inverse problem using a trainable variational formulation. The latter exploits variational auto-encoders coupled with neural ODE to represent sea surface dynamics. We report numerical experiments on a real AIS dataset off South Africa in a highly dynamical ocean region. They support the relevance of the proposed learning-based AIS-driven approach to significantly improve the reconstruction of sea surface currents compared with state-of-the-art methods, including altimetry-based ones. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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24 pages, 9501 KiB  
Article
Estimation of Evapotranspiration and Its Components across China Based on a Modified Priestley–Taylor Algorithm Using Monthly Multi-Layer Soil Moisture Data
by Wanqiu Xing, Weiguang Wang, Quanxi Shao, Linye Song and Mingzhu Cao
Remote Sens. 2021, 13(16), 3118; https://doi.org/10.3390/rs13163118 - 6 Aug 2021
Cited by 8 | Viewed by 3235
Abstract
Although soil moisture (SM) is an important constraint factor of evapotranspiration (ET), the majority of the satellite-driven ET models do not include SM observations, especially the SM at different depths, since its spatial and temporal distribution is difficult to [...] Read more.
Although soil moisture (SM) is an important constraint factor of evapotranspiration (ET), the majority of the satellite-driven ET models do not include SM observations, especially the SM at different depths, since its spatial and temporal distribution is difficult to obtain. Based on monthly three-layer SM data at a 0.25° spatial resolution determined from multi-sources, we updated the original Priestley Taylor–Jet Propulsion Laboratory (PT-JPL) algorithm to the Priestley Taylor–Soil Moisture Evapotranspiration (PT-SM ET) algorithm by incorporating SM control into soil evaporation (Es) and canopy transpiration (T). Both algorithms were evaluated using 17 eddy covariance towers across different biomes of China. The PT-SM ET model shows increased R2, NSE and reduced RMSE, Bias, with more improvements occurring in water-limited regions. SM incorporation into T enhanced ET estimates by increasing R2 and NSE by 4% and 18%, respectively, and RMSE and Bias were respectively reduced by 34% and 7 mm. Moreover, we applied the two ET algorithms to the whole of China and found larger increases in T and Es in the central, northeastern, and southern regions of China when using the PT-SM algorithm compared with the original algorithm. Additionally, the estimated mean annual ET increased from the northwest to the southeast. The SM constraint resulted in higher transpiration estimate and lower evaporation estimate. Es was greatest in the northwest arid region, interception was a large fraction in some rainforests, and T was dominant in most other regions. Further improvements in the estimation of ET components at high spatial and temporal resolution are likely to lead to a better understanding of the water movement through the soil–plant–atmosphere continuum. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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11 pages, 3898 KiB  
Technical Note
Reason Analysis of the Jiwenco Glacial Lake Outburst Flood (GLOF) and Potential Hazard on the Qinghai-Tibetan Plateau
by Shijin Wang, Yuande Yang, Wenyu Gong, Yanjun Che, Xinggang Ma and Jia Xie
Remote Sens. 2021, 13(16), 3114; https://doi.org/10.3390/rs13163114 - 6 Aug 2021
Cited by 17 | Viewed by 3210
Abstract
Glacial lake outburst flood (GLOF) is one of the major natural disasters in the Qinghai-Tibetan Plateau (QTP). On 25 June 2020, the outburst of the Jiwenco Glacial Lake (JGL) in the upper reaches of Nidu river in Jiari County of the QTP reached [...] Read more.
Glacial lake outburst flood (GLOF) is one of the major natural disasters in the Qinghai-Tibetan Plateau (QTP). On 25 June 2020, the outburst of the Jiwenco Glacial Lake (JGL) in the upper reaches of Nidu river in Jiari County of the QTP reached the downstream Niwu Township on 26 June, causing damage to many bridges, roads, houses, and other infrastructure, and disrupting telecommunications for several days. Based on radar and optical image data, the evolution of the JGL before and after the outburst was analyzed. The results showed that the area and storage capacity of the JGL were 0.58 square kilometers and 0.071 cubic kilometers, respectively, before the outburst (29 May), and only 0.26 square kilometers and 0.017 cubic kilometers remained after the outburst (27 July). The outburst reservoir capacity was as high as 5.4 million cubic meters. The main cause of the JGL outburst was the heavy precipitation process before outburst and the ice/snow/landslides entering the lake was the direct inducement. The outburst flood/debris flow disaster also led to many sections of the river and buildings in Niwu Township at high risk. Therefore, it is urgent to pay more attention to glacial lake outburst floods and other low-probability disasters, and early real-time engineering measures should be taken to minimize their potential impacts. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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33 pages, 13651 KiB  
Article
A New 32-Day Average-Difference Method for Calculating Inter-Sensor Calibration Radiometric Biases between SNPP and NOAA-20 Instruments within ICVS Framework
by Banghua Yan, Mitch Goldberg, Xin Jin, Ding Liang, Jingfeng Huang, Warren Porter, Ninghai Sun, Lihang Zhou, Chunhui Pan, Flavio Iturbide-Sanchez, Quanhua Liu and Kun Zhang
Remote Sens. 2021, 13(16), 3079; https://doi.org/10.3390/rs13163079 - 5 Aug 2021
Cited by 4 | Viewed by 2626
Abstract
Two existing double-difference (DD) methods, using either a 3rdSensor or Radiative Transfer Modeling (RTM) as a transfer, are applicable primarily for limited regions and channels, and, thus critical in capturing inter-sensor calibration radiometric bias features. A supplementary method is also desirable [...] Read more.
Two existing double-difference (DD) methods, using either a 3rdSensor or Radiative Transfer Modeling (RTM) as a transfer, are applicable primarily for limited regions and channels, and, thus critical in capturing inter-sensor calibration radiometric bias features. A supplementary method is also desirable for estimating inter-sensor calibration biases at the window and lower sounding channels where the DD methods have non-negligible errors. In this study, using the Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar Satellite System (JPSS)-1 (alias NOAA-20) as an example, we present a new inter-sensor bias statistical method by calculating 32-day averaged differences (32D-AD) of radiometric measurements between the same instrument onboard two satellites. In the new method, a quality control (QC) scheme using one-sigma (for radiance difference), or two-sigma (for radiance) thresholds are established to remove outliers that are significantly affected by diurnal biases within the 32-day temporal coverage. The performance of the method is assessed by applying it to estimate inter-sensor calibration radiometric biases for four instruments onboard SNPP and NOAA-20, i.e., Advanced Technology Microwave Sounder (ATMS), Cross-track Infrared Sounder (CrIS), Nadir Profiler (NP) within the Ozone Mapping and Profiler Suite (OMPS), and Visible Infrared Imaging Radiometer Suite (VIIRS). Our analyses indicate that the globally-averaged inter-sensor differences using the 32D-AD method agree with those using the existing DD methods for available channels, with margins partially due to remaining diurnal errors. In addition, the new method shows its capability in assessing zonal mean features of inter-sensor calibration biases at upper sounding channels. It also detects the solar intrusion anomaly occurring on NOAA-20 OMPS NP at wavelengths below 300 nm over the Northern Hemisphere. Currently, the new method is being operationally adopted to monitor the long-term trends of (globally-averaged) inter-sensor calibration radiometric biases at all channels for the above sensors in the Integrated Calibration/Validation System (ICVS). It is valuable in demonstrating the quality consistencies of the SDR data at the four instruments between SNPP and NOAA-20 in long-term statistics. The methodology is also applicable for other POES cross-sensor calibration bias assessments with minor changes. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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18 pages, 1814 KiB  
Article
Interferometric Phase Error Analysis and Compensation in GNSS-InSAR: A Case Study of Structural Monitoring
by Zhanze Wang, Feifeng Liu, Tao Zeng and Chenghao Wang
Remote Sens. 2021, 13(15), 3041; https://doi.org/10.3390/rs13153041 - 3 Aug 2021
Cited by 20 | Viewed by 3274
Abstract
Global navigation satellite system (GNSS)-based synthetic aperture radar interferometry (InSAR) employs GNSS satellites as transmitters of opportunity and a fixed receiver with two channels, i.e., direct wave and echo, on the ground. The repeat-pass concept is adopted in GNSS-based InSAR to retrieve the [...] Read more.
Global navigation satellite system (GNSS)-based synthetic aperture radar interferometry (InSAR) employs GNSS satellites as transmitters of opportunity and a fixed receiver with two channels, i.e., direct wave and echo, on the ground. The repeat-pass concept is adopted in GNSS-based InSAR to retrieve the deformation of the target area, and it has inherited advantages from the GNSS system, such as a short repeat-pass period and multi-angle retrieval. However, several interferometric phase errors, such as inter-channel and atmospheric errors, are introduced into GNSS-based InSAR, which seriously decreases the accuracy of the retrieved deformation. In this paper, a deformation retrieval algorithm is presented to assess the compensation of the interferometric phase errors in GNSS-based InSAR. Firstly, the topological phase error was eliminated based on accurate digital elevation model (DEM) information from a light detection and ranging (lidar) system. Secondly, the inter-channel phase error was compensated, using direct wave in the echo channel, i.e., a back lobe signal. Finally, by modeling the atmospheric phase, the residual atmospheric phase error was compensated for. This is the first realization of the deformation detection of urban scenes using a GNSS-based system, and the results suggest the effectiveness of the phase error compensation algorithm. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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18 pages, 5211 KiB  
Article
A Second-Order Time-Difference Position Constrained Reduced-Dynamic Technique for the Precise Orbit Determination of LEOs Using GPS
by Hui Wei, Jiancheng Li, Xinyu Xu, Shoujian Zhang and Kaifa Kuang
Remote Sens. 2021, 13(15), 3033; https://doi.org/10.3390/rs13153033 - 2 Aug 2021
Cited by 1 | Viewed by 2381
Abstract
In this paper, we propose a new reduced-dynamic (RD) method by introducing the second-order time-difference position (STP) as additional pseudo-observations (named the RD_STP method) for the precise orbit determination (POD) of low Earth orbiters (LEOs) from GPS observations. Theoretical and numerical analyses show [...] Read more.
In this paper, we propose a new reduced-dynamic (RD) method by introducing the second-order time-difference position (STP) as additional pseudo-observations (named the RD_STP method) for the precise orbit determination (POD) of low Earth orbiters (LEOs) from GPS observations. Theoretical and numerical analyses show that the accuracies of integrating the STPs of LEOs at 30 s intervals are better than 0.01 m when the forces (<10?5 ms?2) acting on the LEOs are ignored. Therefore, only using the Earth’s gravity model is good enough for the proposed RD_STP method. All unmodeled dynamic models (e.g., luni-solar gravitation, tide forces) are treated as the error sources of the STP pseudo-observation. In addition, there are no pseudo-stochastic orbit parameters to be estimated in the RD_STP method. Finally, we use the RD_STP method to process 15 days of GPS data from the GOCE mission. The results show that the accuracy of the RD_STP solution is more accurate and smoother than the kinematic solution in nearly polar and equatorial regions, and consistent with the RD solution. The 3D RMS of the differences between the RD_STP and RD solutions is 1.93 cm for 1 s sampling. This indicates that the proposed method has a performance comparable to the RD method, and could be an alternative for the POD of LEOs. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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27 pages, 12834 KiB  
Article
Moving Target Shadow Analysis and Detection for ViSAR Imagery
by Zhihua He, Xing Chen, Tianzhu Yi, Feng He, Zhen Dong and Yue Zhang
Remote Sens. 2021, 13(15), 3012; https://doi.org/10.3390/rs13153012 - 31 Jul 2021
Cited by 9 | Viewed by 3344
Abstract
The video synthetic aperture radar (ViSAR) is a new application in radar techniques. ViSAR provides high- or moderate-resolution SAR images with a faster frame rate, which permits the detection of the dynamic changes in the interested area. A moving target with moderate velocity [...] Read more.
The video synthetic aperture radar (ViSAR) is a new application in radar techniques. ViSAR provides high- or moderate-resolution SAR images with a faster frame rate, which permits the detection of the dynamic changes in the interested area. A moving target with moderate velocity can be detected by shadow detection in ViSAR. This paper analyses the frame rate and the shadow feature, discusses the velocity limitation of ViSAR moving target shadow detection and quantitatively gives the expression of velocity limitation. Furthermore, a fast factorized back projection (FFBP) based SAR video formation method and a shadow-based ground moving target detection method are proposed to generate SAR videos and detect the moving target shadow. The experimental results with simulated data prove the validity and feasibility of the proposed quantitative analysis and the proposed methods. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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23 pages, 8381 KiB  
Article
Three-Dimensional Interferometric ISAR Imaging Algorithm Based on Cross Coherence Processing
by Qian Lv and Shaozhe Zhang
Sensors 2021, 21(15), 5073; https://doi.org/10.3390/s21155073 - 27 Jul 2021
Cited by 3 | Viewed by 2487
Abstract
Interferometric inverse synthetic aperture radar (InISAR) has received significant attention in three-dimensional (3D) imaging due to its applications in target classification and recognition. The traditional two-dimensional (2D) ISAR image can be interpreted as a filtered projection of a 3D target’s reflectivity function onto [...] Read more.
Interferometric inverse synthetic aperture radar (InISAR) has received significant attention in three-dimensional (3D) imaging due to its applications in target classification and recognition. The traditional two-dimensional (2D) ISAR image can be interpreted as a filtered projection of a 3D target’s reflectivity function onto an image plane. Such a plane usually depends on unknown radar-target geometry and dynamics, which results in difficulty interpreting an ISAR image. Using the L-shape InISAR imaging system, this paper proposes a novel 3D target reconstruction algorithm based on Dechirp processing and 2D interferometric ISAR imaging, which can jointly estimate the effective rotation vector and the height of scattering center. In order to consider only the areas of the target with meaningful interferometric phase and mitigate the effects of noise and sidelobes, a special cross-channel coherence-based detector (C3D) is introduced. Compared to the multichannel CLEAN technique, advantages of the C3D include the following: (1) the computational cost is lower without complex iteration and (2) the proposed method, which can avoid propagating errors, is more suitable for a target with multi-scattering points. Moreover, misregistration and its influence on target reconstruction are quantitatively discussed. Theoretical analysis and numerical simulations confirm the suitability of the algorithm for 3D imaging of multi-scattering point targets with high efficiency and demonstrate the reliability and effectiveness of the proposed method in the presence of noise. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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17 pages, 10673 KiB  
Article
Cloud Cover throughout All the Paddy Rice Fields in Guangdong, China: Impacts on Sentinel 2 MSI and Landsat 8 OLI Optical Observations
by Rui Jiang, Arturo Sanchez-Azofeifa, Kati Laakso, Yan Xu, Zhiyan Zhou, Xiwen Luo, Junhao Huang, Xin Chen and Yu Zang
Remote Sens. 2021, 13(15), 2961; https://doi.org/10.3390/rs13152961 - 27 Jul 2021
Cited by 17 | Viewed by 3030
Abstract
Cloud cover hinders the effective use of vegetation indices from optical satellite-acquired imagery in cloudy agricultural production areas, such as Guangdong, a subtropical province in southern China which supports two-season rice production. The number of cloud-free observations for the earth-orbiting optical satellite sensors [...] Read more.
Cloud cover hinders the effective use of vegetation indices from optical satellite-acquired imagery in cloudy agricultural production areas, such as Guangdong, a subtropical province in southern China which supports two-season rice production. The number of cloud-free observations for the earth-orbiting optical satellite sensors must be determined to verify how much their observations are affected by clouds. This study determines the quantified wide-ranging impact of clouds on optical satellite observations by mapping the annual total observations (ATOs), annual cloud-free observations (ACFOs), monthly cloud-free observations (MCFOs) maps, and acquisition probability (AP) of ACFOs for the Sentinel 2 (2017–2019) and Landsat 8 (2014–2019) for all the paddy rice fields in Guangdong province (APRFG), China. The ATOs of Landsat 8 showed relatively stable observations compared to the Sentinel 2, and the per-field ACFOs of Sentinel 2 and Landsat 8 were unevenly distributed. The MCFOs varied on a monthly basis, but in general, the MCFOs were greater between August and December than between January and July. Additionally, the AP of usable ACFOs with 52.1% (Landsat 8) and 47.7% (Sentinel 2) indicated that these two satellite sensors provided markedly restricted observation capability for rice in the study area. Our findings are particularly important and useful in the tropics and subtropics, and the analysis has described cloud cover frequency and pervasiveness throughout different portions of the rice growing season, providing insight into how rice monitoring activities by using Sentinel 2 and Landsat 8 imagery in Guangdong would be impacted by cloud cover. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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16 pages, 5805 KiB  
Article
A Fast Aircraft Detection Method for SAR Images Based on Efficient Bidirectional Path Aggregated Attention Network
by Ru Luo, Lifu Chen, Jin Xing, Zhihui Yuan, Siyu Tan, Xingmin Cai and Jielan Wang
Remote Sens. 2021, 13(15), 2940; https://doi.org/10.3390/rs13152940 - 27 Jul 2021
Cited by 29 | Viewed by 3744
Abstract
In aircraft detection from synthetic aperture radar (SAR) images, there are several major challenges: the shattered features of the aircraft, the size heterogeneity and the interference of a complex background. To address these problems, an Efficient Bidirectional Path Aggregation Attention Network (EBPA2N) is [...] Read more.
In aircraft detection from synthetic aperture radar (SAR) images, there are several major challenges: the shattered features of the aircraft, the size heterogeneity and the interference of a complex background. To address these problems, an Efficient Bidirectional Path Aggregation Attention Network (EBPA2N) is proposed. In EBPA2N, YOLOv5s is used as the base network and then the Involution Enhanced Path Aggregation (IEPA) module and Effective Residual Shuffle Attention (ERSA) module are proposed and systematically integrated to improve the detection accuracy of the aircraft. The IEPA module aims to effectively extract advanced semantic and spatial information to better capture multi-scale scattering features of aircraft. Then, the lightweight ERSA module further enhances the extracted features to overcome the interference of complex background and speckle noise, so as to reduce false alarms. To verify the effectiveness of the proposed network, Gaofen-3 airports SAR data with 1 m resolution are utilized in the experiment. The detection rate and false alarm rate of our EBPA2N algorithm are 93.05% and 4.49%, respectively, which is superior to the latest networks of EfficientDet-D0 and YOLOv5s, and it also has an advantage of detection speed. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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20 pages, 5018 KiB  
Article
Hyperspectral and Multispectral Image Fusion Using Coupled Non-Negative Tucker Tensor Decomposition
by Marzieh Zare, Mohammad Sadegh Helfroush, Kamran Kazemi and Paul Scheunders
Remote Sens. 2021, 13(15), 2930; https://doi.org/10.3390/rs13152930 - 26 Jul 2021
Cited by 22 | Viewed by 3426
Abstract
Fusing a low spatial resolution hyperspectral image (HSI) with a high spatial resolution multispectral image (MSI), aiming to produce a super-resolution hyperspectral image, has recently attracted increasing research interest. In this paper, a novel approach based on coupled non-negative tensor decomposition is proposed. [...] Read more.
Fusing a low spatial resolution hyperspectral image (HSI) with a high spatial resolution multispectral image (MSI), aiming to produce a super-resolution hyperspectral image, has recently attracted increasing research interest. In this paper, a novel approach based on coupled non-negative tensor decomposition is proposed. The proposed method performs a tucker tensor factorization of a low resolution hyperspectral image and a high resolution multispectral image under the constraint of non-negative tensor decomposition (NTD). The conventional matrix factorization methods essentially lose spatio-spectral structure information when stacking the 3D data structure of a hyperspectral image into a matrix form. Moreover, the spectral, spatial, or their joint structural features have to be imposed from the outside as a constraint to well pose the matrix factorization problem. The proposed method has the advantage of preserving the spatio-spectral structure of hyperspectral images. In this paper, the NTD is directly imposed on the coupled tensors of the HSI and MSI. Hence, the intrinsic spatio-spectral structure of the HSI is represented without loss, and spatial and spectral information can be interdependently exploited. Furthermore, multilinear interactions of different modes of the HSIs can be exactly modeled with the core tensor of the Tucker tensor decomposition. The proposed method is straightforward and easy to implement. Unlike other state-of-the-art approaches, the complexity of the proposed approach is linear with the size of the HSI cube. Experiments on two well-known datasets give promising results when compared with some recent methods from the literature. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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15 pages, 3479 KiB  
Article
A Building Roof Identification CNN Based on Interior-Edge-Adjacency Features Using Hyperspectral Imagery
by Chengming Ye, Hongfu Li, Chunming Li, Xin Liu, Yao Li, Jonathan Li, Wesley Nunes Gonçalves and José Marcato Junior
Remote Sens. 2021, 13(15), 2927; https://doi.org/10.3390/rs13152927 - 26 Jul 2021
Cited by 3 | Viewed by 2976
Abstract
Hyperspectral remote sensing can obtain both spatial and spectral information of ground objects. It is an important prerequisite for a hyperspectral remote sensing application to make good use of spectral and image features. Therefore, we improved the Convolutional Neural Network (CNN) model by [...] Read more.
Hyperspectral remote sensing can obtain both spatial and spectral information of ground objects. It is an important prerequisite for a hyperspectral remote sensing application to make good use of spectral and image features. Therefore, we improved the Convolutional Neural Network (CNN) model by extracting interior-edge-adjacency features of building roof and proposed a new CNN model with a flexible structure: Building Roof Identification CNN (BRI-CNN). Our experimental results demonstrated that the BRI-CNN can not only extract interior-edge-adjacency features of building roof, but also change the weight of these different features during the training process, according to selected samples. Our approach was tested using the Indian Pines (IP) data set and our comparative study indicates that the BRI-CNN model achieves at least 0.2% higher overall accuracy than that of the capsule network model, and more than 2% than that of CNN models. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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16 pages, 4970 KiB  
Article
True-Color Reconstruction Based on Hyperspectral LiDAR Echo Energy
by Tengfeng Wang, Xiaoxia Wan, Bowen Chen and Shuo Shi
Remote Sens. 2021, 13(15), 2854; https://doi.org/10.3390/rs13152854 - 21 Jul 2021
Cited by 1 | Viewed by 2505
Abstract
With the development of remote sensing technology, the simultaneous acquisition of 3D point cloud and color information has become the constant goal for scientific research and commercial applications in this field. However, since radar echo data in practice refer to the value of [...] Read more.
With the development of remote sensing technology, the simultaneous acquisition of 3D point cloud and color information has become the constant goal for scientific research and commercial applications in this field. However, since radar echo data in practice refer to the value of the spectral channel and its corresponding energy, it is still impossible to obtain accurate tristimulus values of the point through color integral calculation after traditional normalization and multispectral correction. Furthermore, the reflectance of the target, the laser transmission power and other factors lead to the problems of no echo energy or weak echo energy in some bands of the visible spectrum, which further leads to large chromatic difference compared to the color calculated from the spectral reflectance of standard color card. In response to these problems, the hyperbolic tangent spectrum correction model with parameters is proposed for the spectrum correction of the acquired hyperspectral LiDAR in the 470–700 nm band. In addition, the improved gradient boosting decision tree sequence prediction algorithm is proposed for the reconstruction of missing spectrum in the 400–470 nm band where the echo energy is weak and missing. Experimental results show that there is relatively small chromatic difference between the obtained spectral information after correction and reconstruction and the spectrum of standard color card, achieving the purpose of true color reconstruction. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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20 pages, 8973 KiB  
Article
Evaluation of Eight Global Precipitation Datasets in Hydrological Modeling
by Yiheng Xiang, Jie Chen, Lu Li, Tao Peng and Zhiyuan Yin
Remote Sens. 2021, 13(14), 2831; https://doi.org/10.3390/rs13142831 - 19 Jul 2021
Cited by 32 | Viewed by 4361
Abstract
The number of global precipitation datasets (PPs) is on the rise and they are commonly used for hydrological applications. A comprehensive evaluation on their performance in hydrological modeling is required to improve their performance. This study comprehensively evaluates the performance of eight widely [...] Read more.
The number of global precipitation datasets (PPs) is on the rise and they are commonly used for hydrological applications. A comprehensive evaluation on their performance in hydrological modeling is required to improve their performance. This study comprehensively evaluates the performance of eight widely used PPs in hydrological modeling by comparing with gauge-observed precipitation for a large number of catchments. These PPs include the Global Precipitation Climatology Centre (GPCC), Climate Hazards Group Infrared Precipitation with Station dataset (CHIRPS) V2.0, Climate Prediction Center Morphing Gauge Blended dataset (CMORPH BLD), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN CDR), Tropical Rainfall Measuring Mission multi-satellite Precipitation Analysis 3B42RT (TMPA 3B42RT), Multi-Source Weighted-Ensemble Precipitation (MSWEP V2.0), European Center for Medium-range Weather Forecast Reanalysis 5 (ERA5) and WATCH Forcing Data methodology applied to ERA-Interim Data (WFDEI). Specifically, the evaluation is conducted over 1382 catchments in China, Europe and North America for the 1998-2015 period at a daily temporal scale. The reliabilities of PPs in hydrological modeling are evaluated with a calibrated hydrological model using rain gauge observations. The effectiveness of PPs-specific calibration and bias correction in hydrological modeling performances are also investigated for all PPs. The results show that: (1) compared with the rain gauge observations, GPCC provides the best performance overall, followed by MSWEP V2.0; (2) among the eight PPs, the ones incorporating daily gauge data (MSWEP V2.0 and CMORPH BLD) provide superior hydrological performance, followed by those incorporating 5-day (CHIRPS V2.0) and monthly (TMPA 3B42RT, WFDEI, and PERSIANN CDR) gauge data. MSWEP V2.0 and CMORPH BLD perform better than GPCC, underscoring the effectiveness of merging multiple satellite and reanalysis datasets; (3) regionally, all PPs exhibit better performances in temperate regions than in arid or topographically complex mountainous regions; and (4) PPs-specific calibration and bias correction both can improve the streamflow simulations for all eight PPs in terms of the Nash and Sutcliffe efficiency and the absolute bias. This study provides insights on the reliabilities of PPs in hydrological modeling and the approaches to improve their performance, which is expected to provide a reference for the applications of global precipitation datasets. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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13 pages, 5411 KiB  
Article
Retrieving Sun-Induced Chlorophyll Fluorescence from Hyperspectral Data with TanSat Satellite
by Shilei Li, Maofang Gao and Zhao-Liang Li
Sensors 2021, 21(14), 4886; https://doi.org/10.3390/s21144886 - 18 Jul 2021
Cited by 6 | Viewed by 2925
Abstract
A series of algorithms for satellite retrievals of sun-induced chlorophyll fluorescence (SIF) have been developed and applied to different sensors. However, research on SIF retrieval using hyperspectral data is performed in narrow spectral windows, assuming that SIF remains constant. In this paper, based [...] Read more.
A series of algorithms for satellite retrievals of sun-induced chlorophyll fluorescence (SIF) have been developed and applied to different sensors. However, research on SIF retrieval using hyperspectral data is performed in narrow spectral windows, assuming that SIF remains constant. In this paper, based on the singular vector decomposition (SVD) technique, we present an approach for retrieving SIF, which can be applied to remotely sensed data with ultra-high spectral resolution and in a broad spectral window without assuming that the SIF remains constant. The idea is to combine the first singular vector, the pivotal information of the non-fluorescence spectrum, with the low-frequency contribution of the atmosphere, plus a linear combination of the remaining singular vectors to express the non-fluorescence spectrum. Subject to instrument settings, the retrieval was performed within a spectral window of approximately 7 nm that contained only Fraunhofer lines. In our retrieval, hyperspectral data of the O2-A band from the first Chinese carbon dioxide observation satellite (TanSat) was used. The Bayesian Information Criterion (BIC) was introduced to self-adaptively determine the number of free parameters and reduce retrieval noise. SIF retrievals were compared with TanSat SIF and OCO-2 SIF. The results showed good consistency and rationality. A sensitivity analysis was also conducted to verify the performance of this approach. To summarize, the approach would provide more possibilities for retrieving SIF from hyperspectral data. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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24 pages, 5419 KiB  
Article
Evaluation of Precise Microwave Ranging Technology for Low Earth Orbit Formation Missions with Beidou Time-Synchronize Receiver
by Xiaoliang Wang, Shufan Wu, Deren Gong, Qiang Shen, Dengfeng Wang and Christopher Damaren
Sensors 2021, 21(14), 4883; https://doi.org/10.3390/s21144883 - 17 Jul 2021
Cited by 4 | Viewed by 2700
Abstract
In this study, submillimeter level accuracy K-band microwave ranging (MWR) equipment is demonstrated, aiming to verify the detection of the Earth’s gravity field (EGF) and digital elevation models (DEM), through spacecraft formation flying (SFF) in low Earth orbit (LEO). In particular, this paper [...] Read more.
In this study, submillimeter level accuracy K-band microwave ranging (MWR) equipment is demonstrated, aiming to verify the detection of the Earth’s gravity field (EGF) and digital elevation models (DEM), through spacecraft formation flying (SFF) in low Earth orbit (LEO). In particular, this paper introduces in detail an integrated BeiDou III B1C/B2a dual frequency receiver we designed and developed, including signal processing scheme, gain allocation, and frequency planning. The receiver matched the 0.1 ns precise synchronize time-frequency benchmark for the MWR system, verified by a static and dynamic test, compared with a time interval counter synchronization solution. Moreover, MWR equipment ranging accuracy is explored in-depth by using different ranging techniques. The test results show that MWR achieved 40 ?m and 1.6 ?m/s accuracy for ranging and range rate during tests, using synchronous dual one-way ranging (DOWR) microwave phase accumulation frame, and 6 ?m/s range rate accuracy obtained through a one-way ranging experiment. The ranging error sources of the whole MWR system in-orbit are analyzed, while the relative orbit dynamic models, for formation scenes, and adaptive Kalman filter algorithms, for SFF relative navigation designs, are introduced. The performance of SFF relative navigation using MWR are tested in a hardware in loop (HIL) simulation system within a high precision six degree of freedom (6-DOF) moving platform. The final estimation error from adaptive relative navigation system using MWR are about 0.42 mm (range/RMS) and 0.87 ?m/s (range rate/RMS), which demonstrated the promising accuracy for future applications of EGF and DEM formation missions in space. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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24 pages, 5110 KiB  
Article
Refocusing of Moving Ships in Squint SAR Images Based on Spectrum Orthogonalization
by Xuyao Tong, Min Bao, Guangcai Sun, Liang Han, Yu Zhang and Mengdao Xing
Remote Sens. 2021, 13(14), 2807; https://doi.org/10.3390/rs13142807 - 17 Jul 2021
Cited by 9 | Viewed by 2692
Abstract
Moving ship refocusing is challenging because the target motion parameters are unknown. Moreover, moving ships in squint synthetic aperture radar (SAR) images obtained by the back-projection (BP) algorithm usually suffer from geometric deformation and spectrum winding. Therefore, a spectrum-orthogonalization algorithm that refocuses moving [...] Read more.
Moving ship refocusing is challenging because the target motion parameters are unknown. Moreover, moving ships in squint synthetic aperture radar (SAR) images obtained by the back-projection (BP) algorithm usually suffer from geometric deformation and spectrum winding. Therefore, a spectrum-orthogonalization algorithm that refocuses moving ships in squint SAR images is presented. First, “squint minimization” is introduced to correct the spectrum by two spectrum compression functions: one to align the spectrum centers and another to translate the inclined spectrum into orthogonalized form. Then, the precise analytic function of the two-dimensional (2D) wavenumber spectrum is derived to obtain the phase error. Finally, motion compensation is performed in the two-dimensional wavenumber domain after the motion parameter is estimated by maximizing the image sharpness. This method has low computational complexity because it lacks interpolation and can be implemented by the inverse fast Fourier translation (IFFT) and fast Fourier translation (FFT). Processing results of simulation experiments and the GaoFen-3 squint SAR data validate the effectiveness of this method. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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24 pages, 6171 KiB  
Article
CPISNet: Delving into Consistent Proposals of Instance Segmentation Network for High-Resolution Aerial Images
by Xiangfeng Zeng, Shunjun Wei, Jinshan Wei, Zichen Zhou, Jun Shi, Xiaoling Zhang and Fan Fan
Remote Sens. 2021, 13(14), 2788; https://doi.org/10.3390/rs13142788 - 15 Jul 2021
Cited by 16 | Viewed by 3122
Abstract
Instance segmentation of high-resolution aerial images is challenging when compared to object detection and semantic segmentation in remote sensing applications. It adopts boundary-aware mask predictions, instead of traditional bounding boxes, to locate the objects-of-interest in pixel-wise. Meanwhile, instance segmentation can distinguish the densely [...] Read more.
Instance segmentation of high-resolution aerial images is challenging when compared to object detection and semantic segmentation in remote sensing applications. It adopts boundary-aware mask predictions, instead of traditional bounding boxes, to locate the objects-of-interest in pixel-wise. Meanwhile, instance segmentation can distinguish the densely distributed objects within a certain category by a different color, which is unavailable in semantic segmentation. Despite the distinct advantages, there are rare methods which are dedicated to the high-quality instance segmentation for high-resolution aerial images. In this paper, a novel instance segmentation method, termed consistent proposals of instance segmentation network (CPISNet), for high-resolution aerial images is proposed. Following top-down instance segmentation formula, it adopts the adaptive feature extraction network (AFEN) to extract the multi-level bottom-up augmented feature maps in design space level. Then, elaborated RoI extractor (ERoIE) is designed to extract the mask RoIs via the refined bounding boxes from proposal consistent cascaded (PCC) architecture and multi-level features from AFEN. Finally, the convolution block with shortcut connection is responsible for generating the binary mask for instance segmentation. Experimental conclusions can be drawn on the iSAID and NWPU VHR-10 instance segmentation dataset: (1) Each individual module in CPISNet acts on the whole instance segmentation utility; (2) CPISNet* exceeds vanilla Mask R-CNN 3.4%/3.8% AP on iSAID validation/test set and 9.2% AP on NWPU VHR-10 instance segmentation dataset; (3) The aliasing masks, missing segmentations, false alarms, and poorly segmented masks can be avoided to some extent for CPISNet; (4) CPISNet receives high precision of instance segmentation for aerial images and interprets the objects with fitting boundary. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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17 pages, 14420 KiB  
Technical Note
A Sparse Denoising-Based Super-Resolution Method for Scanning Radar Imaging
by Qiping Zhang, Yin Zhang, Yongchao Zhang, Yulin Huang and Jianyu Yang
Remote Sens. 2021, 13(14), 2768; https://doi.org/10.3390/rs13142768 - 14 Jul 2021
Cited by 9 | Viewed by 2615
Abstract
Scanning radar enables wide-range imaging through antenna scanning and is widely used for radar warning. The Rayleigh criterion indicates that narrow beams of radar are required to improve the azimuth resolution. However, a narrower beam means a larger antenna aperture. In practical applications, [...] Read more.
Scanning radar enables wide-range imaging through antenna scanning and is widely used for radar warning. The Rayleigh criterion indicates that narrow beams of radar are required to improve the azimuth resolution. However, a narrower beam means a larger antenna aperture. In practical applications, due to platform limitations, the antenna aperture is limited, resulting in a low azimuth resolution. The conventional sparse super-resolution method (SSM) has been proposed for improving the azimuth resolution of scanning radar imaging and achieving superior performance. This method uses the L1 norm to represent the sparse prior of the target and solves the L1 regularization problem to achieve super-resolution imaging under the regularization framework. The resolution of strong-point targets is improved efficiently. However, for some targets with typical shapes, the strong sparsity of the L1 norm treats them as strong-point targets, resulting in the loss of shape characteristics. Thus, we can only see the strong points in its processing results. However, in some applications that need to identify targets in detail, SSM can lead to false judgments. In this paper, a sparse denoising-based super-resolution method (SDBSM) is proposed to compensate for the deficiency of traditional SSM. The proposed SDBSM uses a sparse minimization scheme for denoising, which helps to reduce the influence of noise. Then, the super-resolution imaging is achieved by alternating iterative denoising and deconvolution. As the proposed SDBSM uses the L1 norm for denoising rather than deconvolution, the strong sparsity constraint of the L1 norm is reduced. Therefore, it can effectively preserve the shape of the target while improving the azimuth resolution. The performance of the proposed SDBSM was demonstrated via simulation and real data processing results. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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10 pages, 4230 KiB  
Technical Note
Inversion of Geothermal Heat Flux under the Ice Sheet of Princess Elizabeth Land, East Antarctica
by Lin Li, Xueyuan Tang, Jingxue Guo, Xiangbin Cui, Enzhao Xiao, Khalid Latif, Bo Sun, Qiao Zhang and Xiaosong Shi
Remote Sens. 2021, 13(14), 2760; https://doi.org/10.3390/rs13142760 - 14 Jul 2021
Cited by 8 | Viewed by 4742
Abstract
Antarctic geothermal heat flux is a basic input variable for ice sheet dynamics simulation. It greatly affects the temperature and mechanical properties at the bottom of the ice sheet, influencing sliding, melting, and internal deformation. Due to the fact that the Antarctica is [...] Read more.
Antarctic geothermal heat flux is a basic input variable for ice sheet dynamics simulation. It greatly affects the temperature and mechanical properties at the bottom of the ice sheet, influencing sliding, melting, and internal deformation. Due to the fact that the Antarctica is covered by a thick ice sheet, direct measurements of heat flux are very limited. This study was carried out to estimate the regional heat flux in the Antarctic continent through geophysical inversion. Princess Elizabeth Land, East Antarctica is one of the areas in which we have a weak understanding of geothermal heat flux. Through the latest airborne geomagnetic data, we inverted the Curie depth, obtaining the heat flux of bedrock based on the one-dimensional steady-state heat conduction equation. The results indicated that the Curie depth of the Princess Elizabeth Land is shallower than previously estimated, and the heat flux is consequently higher. Thus, the contribution of subglacial heat flux to the melting at the bottom of the ice sheet is likely greater than previously expected in this region. It further provides research clues for the formation of the developed subglacial water system in Princess Elizabeth Land. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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18 pages, 2052 KiB  
Article
A Novel Method for Refocusing Moving Ships in SAR Images via ISAR Technique
by Xinlin Jia, Hongjun Song and Wenjing He
Remote Sens. 2021, 13(14), 2738; https://doi.org/10.3390/rs13142738 - 12 Jul 2021
Cited by 11 | Viewed by 3180
Abstract
As an active microwave remote sensing device, synthetic aperture radar (SAR) has been widely used in the field of marine surveillance. However, moving ships appear defocused in SAR images, which seriously affects the classification and identification of ships. Considering the three-dimensional (3-D) rotational [...] Read more.
As an active microwave remote sensing device, synthetic aperture radar (SAR) has been widely used in the field of marine surveillance. However, moving ships appear defocused in SAR images, which seriously affects the classification and identification of ships. Considering the three-dimensional (3-D) rotational motions (roll, pitch, and yaw) of the navigating ship, a novel method for refocusing moving ships in SAR images based on inverse synthetic aperture radar (ISAR) technique is proposed. First, a rectangular window is used to extract the defocused ship subimage. Next, the subimage is transformed into the ISAR equivalent echo domain, and the range migration and phase error caused by the identical movement of all ship scatterers are compensated. Then, the optimal imaging time can be selected by the maximum image contrast search method. Finally, the iterative adaptive approach (IAA) is used to obtain the image with high resolution. This method has satisfactory imaging performance in both azimuth resolution and image focus, and the amount of calculation is small due to the processing of subimages. Simulated data and Gaofen-3 real SAR data are used to verify the effectiveness of the proposed method. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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22 pages, 4465 KiB  
Article
A Multi-Task Network with Distance–Mask–Boundary Consistency Constraints for Building Extraction from Aerial Images
by Furong Shi and Tong Zhang
Remote Sens. 2021, 13(14), 2656; https://doi.org/10.3390/rs13142656 - 6 Jul 2021
Cited by 11 | Viewed by 3476
Abstract
Deep-learning technologies, especially convolutional neural networks (CNNs), have achieved great success in building extraction from areal images. However, shape details are often lost during the down-sampling process, which results in discontinuous segmentation or inaccurate segmentation boundary. In order to compensate for the loss [...] Read more.
Deep-learning technologies, especially convolutional neural networks (CNNs), have achieved great success in building extraction from areal images. However, shape details are often lost during the down-sampling process, which results in discontinuous segmentation or inaccurate segmentation boundary. In order to compensate for the loss of shape information, two shape-related auxiliary tasks (i.e., boundary prediction and distance estimation) were jointly learned with building segmentation task in our proposed network. Meanwhile, two consistency constraint losses were designed based on the multi-task network to exploit the duality between the mask prediction and two shape-related information predictions. Specifically, an atrous spatial pyramid pooling (ASPP) module was appended to the top of the encoder of a U-shaped network to obtain multi-scale features. Based on the multi-scale features, one regression loss and two classification losses were used for predicting the distance-transform map, segmentation, and boundary. Two inter-task consistency-loss functions were constructed to ensure the consistency between distance maps and masks, and the consistency between masks and boundary maps. Experimental results on three public aerial image data sets showed that our method achieved superior performance over the recent state-of-the-art models. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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21 pages, 11604 KiB  
Article
A Convolutional Neural Network Based on Grouping Structure for Scene Classification
by Xuan Wu, Zhijie Zhang, Wanchang Zhang, Yaning Yi, Chuanrong Zhang and Qiang Xu
Remote Sens. 2021, 13(13), 2457; https://doi.org/10.3390/rs13132457 - 23 Jun 2021
Cited by 19 | Viewed by 2930
Abstract
Convolutional neural network (CNN) is capable of automatically extracting image features and has been widely used in remote sensing image classifications. Feature extraction is an important and difficult problem in current research. In this paper, data augmentation for avoiding over fitting was attempted [...] Read more.
Convolutional neural network (CNN) is capable of automatically extracting image features and has been widely used in remote sensing image classifications. Feature extraction is an important and difficult problem in current research. In this paper, data augmentation for avoiding over fitting was attempted to enrich features of samples to improve the performance of a newly proposed convolutional neural network with UC-Merced and RSI-CB datasets for remotely sensed scene classifications. A multiple grouped convolutional neural network (MGCNN) for self-learning that is capable of promoting the efficiency of CNN was proposed, and the method of grouping multiple convolutional layers capable of being applied elsewhere as a plug-in model was developed. Meanwhile, a hyper-parameter C in MGCNN is introduced to probe into the influence of different grouping strategies for feature extraction. Experiments on the two selected datasets, the RSI-CB dataset and UC-Merced dataset, were carried out to verify the effectiveness of this newly proposed convolutional neural network, the accuracy obtained by MGCNN was 2% higher than the ResNet-50. An algorithm of attention mechanism was thus adopted and incorporated into grouping processes and a multiple grouped attention convolutional neural network (MGCNN-A) was therefore constructed to enhance the generalization capability of MGCNN. The additional experiments indicate that the incorporation of the attention mechanism to MGCNN slightly improved the accuracy of scene classification, but the robustness of the proposed network was enhanced considerably in remote sensing image classifications. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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22 pages, 8979 KiB  
Article
The Extraction of Street Curbs from Mobile Laser Scanning Data in Urban Areas
by Leyang Zhao, Li Yan and Xiaolin Meng
Remote Sens. 2021, 13(12), 2407; https://doi.org/10.3390/rs13122407 - 19 Jun 2021
Cited by 11 | Viewed by 3749
Abstract
The demand for mobile laser scanning in urban areas has grown in recent years. Mobile-based light detection and ranging (LiDAR) technology can be used to collect high-precision digital information on city roads and building façades. However, due to the small size of curbs, [...] Read more.
The demand for mobile laser scanning in urban areas has grown in recent years. Mobile-based light detection and ranging (LiDAR) technology can be used to collect high-precision digital information on city roads and building façades. However, due to the small size of curbs, the information that can be used for curb detection is limited. Moreover, occlusion may cause the extraction method unable to correctly capture the curb area. This paper presents the development of an algorithm for extracting street curbs from mobile-based LiDAR point cloud data to support city managers in street deformation monitoring and urban street reconstruction. The proposed method extracts curbs in three complex scenarios: vegetation covering the curbs, curved street curbs, and occlusion curbs by vehicles, pedestrians. This paper combined both spatial information and geometric information, using the spatial attributes of the road boundary. It can adapt to different heights and different road boundary structures. Analyses of real study sites show the rationality and applicability of this method for obtaining accurate results in curb-based street extraction from mobile-based LiDAR data. The overall performance of road curb extraction is fully discussed, and the results are shown to be promising. Both the completeness and correctness of the extracted left and right road edges are greater than 98%. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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18 pages, 4945 KiB  
Article
Remote Sensing Based Yield Estimation of Rice (Oryza Sativa L.) Using Gradient Boosted Regression in India
by Ponraj Arumugam, Abel Chemura, Bernhard Schauberger and Christoph Gornott
Remote Sens. 2021, 13(12), 2379; https://doi.org/10.3390/rs13122379 - 18 Jun 2021
Cited by 39 | Viewed by 6328
Abstract
Accurate and spatially explicit yield information is required to ensure farmers’ income and food security at local and national levels. Current approaches based on crop cutting experiments are expensive and usually too late for timely income stabilization measures like crop insurances. We, therefore, [...] Read more.
Accurate and spatially explicit yield information is required to ensure farmers’ income and food security at local and national levels. Current approaches based on crop cutting experiments are expensive and usually too late for timely income stabilization measures like crop insurances. We, therefore, utilized a Gradient Boosted Regression (GBR), a machine learning technique, to estimate rice yields at ~500 m spatial resolution for rice-producing areas in India with potential application for near real-time estimates. We used resampled intermediate resolution (~5 km) images of the Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and observed yields at the district level in India for calibrating GBR models. These GBRs were then used to downscale district yields to 500 m resolution. Downscaled yields were re-aggregated for validation against out-of-sample district yields not used for model training and an additional independent data set of block-level (below district-level) yields. Our downscaled and re-aggregated yields agree well with reported district-level observations from 2003 to 2015 (r = 0.85 & MAE = 0.15 t/ha). The model performance improved further when estimating separate models for different rice cropping densities (up to r = 0.93). An additional out-of-sample validation for the years 2016 and 2017, proved successful with r = 0.84 and r = 0.77, respectively. Simulated yield accuracy was higher in water-limited, rainfed agricultural systems. We conclude that this downscaling approach of rice yield estimation using GBR is feasible across India and may complement current approaches for timely rice yield estimation required by insurance companies and government agencies. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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17 pages, 5961 KiB  
Article
Integration of InSAR and LiDAR Technologies for a Detailed Urban Subsidence and Hazard Assessment in Shenzhen, China
by Yufang He, Guochang Xu, Hermann Kaufmann, Jingtao Wang, Hua Ma and Tong Liu
Remote Sens. 2021, 13(12), 2366; https://doi.org/10.3390/rs13122366 - 17 Jun 2021
Cited by 28 | Viewed by 5477
Abstract
Spaceborne interferometric synthetic aperture radar (InSAR) methodology has been widely successfully applied to measure urban surface micro slow subsidence. However, the accuracy is still limited by the spatial resolution of currently operating SAR systems and the lacking precision of geolocation of the respective [...] Read more.
Spaceborne interferometric synthetic aperture radar (InSAR) methodology has been widely successfully applied to measure urban surface micro slow subsidence. However, the accuracy is still limited by the spatial resolution of currently operating SAR systems and the lacking precision of geolocation of the respective scatters. In this context, high-precision urban models, as provided by the active laser point cloud methodology through light detection and ranging (LiDAR) techniques, can assist in improving the geolocation quality of InSAR-derived permanent scatters (PS) and provide the precise contour of buildings for hazard analysis. This paper proposes to integrate InSAR and LiDAR technologies for an improved detailed analysis of subsidence levels and a hazard assessment for buildings in the urban environment. By the use of LiDAR data, most building contours in the main subsidence area were extracted and SAR positioning of buildings via PS points was refined more precisely. The workflow for the proposed method includes the monitoring of land subsidence by the TS-InSAR technique, the geolocation improvement of InSAR-derived PS, and building contour extraction by LiDAR data. Furthermore, a reasonable hazard assessment system of land subsidence was developed. Significant vertical subsidence of ?40 to 12 mm per year was detected by the analysis of multisensor SAR images. The land subsidence rates in the Shenzhen District obviously follow certain spatial patterns. Most stable areas are located in the middle and northeast of Shenzhen except for some areas in Houhai, the Qianhai Bay, and the Wankeyuncheng. An additional hazard assessment of land subsidence reveals that the subsidence of buildings is mainly caused by the construction of new buildings and some by underground activities. The research results of this paper can provide a useful synoptic reference for urban planning and help reducing land subsidence in Shenzhen. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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23 pages, 178132 KiB  
Article
Tests with SAR Images of the PAZ Platform Applied to the Archaeological Site of Clunia (Burgos, Spain)
by Ignacio Fiz, Rosa Cuesta, Eva Subias and Pere Manel Martin
Remote Sens. 2021, 13(12), 2344; https://doi.org/10.3390/rs13122344 - 15 Jun 2021
Cited by 6 | Viewed by 2999
Abstract
This article presents the first results obtained from the use of high-resolution images from the SAR-X sensor of the PAZ satellite platform. These are in result of the application of various radar image-treatment techniques, with which we wanted to carry out a non-invasive [...] Read more.
This article presents the first results obtained from the use of high-resolution images from the SAR-X sensor of the PAZ satellite platform. These are in result of the application of various radar image-treatment techniques, with which we wanted to carry out a non-invasive exploration of areas of the archaeological site of Clunia (Burgos, Spain). These areas were analyzed and contrasted with other sources from high-resolution multispectral images (TripleSat), or from digital surface models obtained from Laser Imaging Detection and Ranging (LiDAR) data from the National Plan for Aerial Orthophotography (PNOA), and treated with image enhancement functions (Relief Visualization Tools (RVT)). Moreover, they were compared with multispectral images created from the Infrared Red Blue (IRRB) data contained in the same LiDAR points. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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