Journal Description
Remote Sensing
Remote Sensing
is an international, peer-reviewed, open access journal about the science and application of remote sensing technology, and is published semimonthly online by MDPI. The Remote Sensing Society of Japan (RSSJ) and the Japan Society of Photogrammetry and Remote Sensing (JSPRS) are affiliated with Remote Sensing, and their members receive a discount on the article processing charge.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, PubAg, GeoRef, Astrophysics Data System, Inspec, dblp, and other databases.
- Journal Rank: JCR - Q1 (Geosciences, Multidisciplinary) / CiteScore - Q1 (General Earth and Planetary Sciences)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 23 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journal: Geomatics
Impact Factor:
5.0 (2022);
5-Year Impact Factor:
5.6 (2022)
Latest Articles
Examining the Capability of the VLF Technique for Nowcasting Solar Flares Based on Ground Measurements in Antarctica
Remote Sens. 2024, 16(12), 2092; https://doi.org/10.3390/rs16122092 (registering DOI) - 9 Jun 2024
Abstract
Measurements of Very-Low-Frequency (VLF) transmitter signals have been widely used to investigate the effects of various space weather events on the D-region ionosphere, including nowcasting solar flares. Previous studies have established a method to nowcast solar flares using VLF measurements, but only using
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Measurements of Very-Low-Frequency (VLF) transmitter signals have been widely used to investigate the effects of various space weather events on the D-region ionosphere, including nowcasting solar flares. Previous studies have established a method to nowcast solar flares using VLF measurements, but only using measurements from dayside propagation paths, and there remains limited focus on day–night mixed paths, which are important for method applicability. Between March and May of 2022, the Sun erupted a total of 56 M-class and 6 X-class solar flares, all of which were well captured by our VLF receiver in Antarctica. Using these VLF measurements, we reexamine the capability of the VLF technique to nowcast solar flares by including day–night mixed propagation paths and expanding the path coverage in longitude compared to that in previous studies. The amplitude and phase maximum changes are generally positively correlated with X-ray fluxes, whereas the time delay is negatively correlated. The curve-fitting parameters that we obtain for the X-ray fluxes and VLF signal maximum changes are consistent with those in previous studies for dayside paths, even though different instruments are used, supporting the flare-nowcasting method. Moreover, the present results show that, for day–night mixed paths, the amplitude and phase maximum changes also scale linearly with the logarithm of the flare X-ray fluxes, but the level of change is notably different from that for dayside paths. The coefficients used in the flare-nowcasting method need to be updated for mixed propagation paths.
Full article
Open AccessArticle
Detecting Melt Pond Onset on Landfast Arctic Sea Ice Using a Dual C-Band Satellite Approach
by
Syeda Shahida Maknun, Torsten Geldsetzer, Vishnu Nandan, John Yackel and Mallik Mahmud
Remote Sens. 2024, 16(12), 2091; https://doi.org/10.3390/rs16122091 (registering DOI) - 9 Jun 2024
Abstract
The presence of melt ponds on the surface of Arctic Sea ice affects its albedo, thermal properties, and overall melting rate; thus, the detection of melt pond onset is of significant importance for understanding the Arctic’s changing climate. This study investigates the utility
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The presence of melt ponds on the surface of Arctic Sea ice affects its albedo, thermal properties, and overall melting rate; thus, the detection of melt pond onset is of significant importance for understanding the Arctic’s changing climate. This study investigates the utility of a novel method for detecting the onset of melt ponds on sea ice using a satellite-based, dual-sensor C-band approach, whereby Sentinel-1 provides horizontally polarized (HH) data and Advanced SCATterometer (ASCAT) provides vertically polarized (VV) data. The co-polarized ratio (VV/HH) is used to detect the presence of melt ponds on landfast sea ice in the Canadian Arctic Archipelago in 2017 and 2018. ERA-5 air temperature and wind speed re-analysis datasets are used to establish the VV/HH threshold for pond onset detection, which have been further validated by Landsat-8 reflectance. The co-polarized ratio threshold of three standard deviations from the late winter season (April) mean co-pol ratio values are used for assessing pond onset detection associated with the air temperature and wind speed data, along with visual observations from Sentinel-1 and cloud-free Sentinel-2 imagery. In 2017, the pond onset detection rates were 70.59% for FYI and 92.3% for MYI. Results suggest that this method, because of its dual-platform application, has potential for providing large-area coverage estimation of the timing of sea ice melt pond onset using different earth observation satellites.
Full article
Open AccessArticle
An Automated Approach for Mapping Mining-Induced Fissures Using CNNs and UAS Photogrammetry
by
Kun Wang, Bowei Wei, Tongbin Zhao, Gengkun Wu, Junyang Zhang, Liyi Zhu and Letian Wang
Remote Sens. 2024, 16(12), 2090; https://doi.org/10.3390/rs16122090 (registering DOI) - 9 Jun 2024
Abstract
Understanding the distribution and development patterns of mining-induced fissures is crucial for environmental protection and geological hazard prevention. To address labor-intensive manual inspection, an automated approach leveraging Convolutional Neural Networks (CNNs) and Unmanned Aerial System Photogrammetry (UASP) is proposed for fissure identification and
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Understanding the distribution and development patterns of mining-induced fissures is crucial for environmental protection and geological hazard prevention. To address labor-intensive manual inspection, an automated approach leveraging Convolutional Neural Networks (CNNs) and Unmanned Aerial System Photogrammetry (UASP) is proposed for fissure identification and mapping. Initially, the ResNet-50 network was employed for the binary classification of the cropped UASP orthophoto images. A comparative analysis was conducted to determine the optimal model between DeepLabv3+ and U-Net. Subsequently, the identified fissures were mosaicked and spatially projected onto the original orthophoto image, incorporating precise projection data, thereby furnishing a spatial reference for environmental governance. The results indicate a classification accuracy of 93% for the ResNet-50 model, with the U-Net model demonstrating a superior identification performance. Fissure orientation and distribution patterns are influenced by the mining direction, ground position of the mining workface, and topographic undulations. Enhancing the CNN performance can be achieved by incorporating variables such as slope indices, vegetation density, and mining workface locations. Lastly, a remote unmanned approach is proposed for the automated mapping of mining-induced fissures, integrated with UAS automated charging station technology. This study contributes to the advancement of intelligent, labor-saving, and unmanned management approaches advocated by the mining industry, with potential for broad applications in mining environmental protection efforts.
Full article
(This article belongs to the Special Issue Latest Improvements and Applications of Ground Deformation Monitoring Based on Remote Sensing Data)
Open AccessArticle
A Spectral and Spatial Comparison of Satellite-Based Hyperspectral Data for Geological Mapping
by
Rupsa Chakraborty, Imane Rachdi, Samuel Thiele, René Booysen, Moritz Kirsch, Sandra Lorenz, Richard Gloaguen and Imane Sebari
Remote Sens. 2024, 16(12), 2089; https://doi.org/10.3390/rs16122089 (registering DOI) - 9 Jun 2024
Abstract
The new generation of satellite hyperspectral (HS) sensors provides remarkable potential for regional-scale mineralogical mapping. However, as with any satellite sensor, mapping results are dependent on a typically complex correction procedure needed to remove atmospheric, topographic and geometric distortions before accurate reflectance spectra
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The new generation of satellite hyperspectral (HS) sensors provides remarkable potential for regional-scale mineralogical mapping. However, as with any satellite sensor, mapping results are dependent on a typically complex correction procedure needed to remove atmospheric, topographic and geometric distortions before accurate reflectance spectra can be retrieved. These are typically applied by the satellite operators but use different approaches that can yield different results. In this study, we conduct a comparative analysis of PRISMA, EnMAP, and EMIT hyperspectral satellite data, alongside airborne data acquired by the HyMap sensor, to investigate the consistency between these datasets and their suitability for geological mapping. Two sites in Namibia were selected for this comparison, the Marinkas-Quellen and Epembe carbonatite complexes, based on their geological significance, relatively good exposure, arid climate and data availability. We conducted qualitative and three different quantitative comparisons of the hyperspectral data from these sites. These included correlative comparisons of (1) the reflectance values across the visible-near infrared (VNIR) to shortwave infrared (SWIR) spectral ranges, (2) established spectral indices sensitive to minerals we expect in each of the scenes, and (3) spectral abundances estimated using linear unmixing. The results highlighted a notable shift in inter-sensor consistency between the VNIR and SWIR spectral ranges, with the VNIR range being more similar between the compared sensors than the SWIR. Our qualitative comparisons suggest that the SWIR spectra from the EnMAP and EMIT sensors are the most interpretable (show the most distinct absorption features) but that latent features (i.e., endmember abundances) from the HyMap and PRISMA sensors are consistent with geological variations. We conclude that our results reinforce the need for accurate radiometric and topographic corrections, especially for the SWIR range most commonly used for geological mapping.
Full article
(This article belongs to the Topic Advances in Earth Observation and Geosciences)
Open AccessTechnical Note
Integration of Handheld and Airborne Lidar Data for Dicranopteris Dichotoma Biomass Estimation in a Subtropical Region of Fujian Province, China
by
Xiaoxue Li, Juan Wu, Shunfa Lu, Dengqiu Li and Dengsheng Lu
Remote Sens. 2024, 16(12), 2088; https://doi.org/10.3390/rs16122088 (registering DOI) - 9 Jun 2024
Abstract
Dicranopteris dichotoma is a pioneer herbaceous plant species that is tolerant to barrenness and drought. Mapping its biomass spatial distribution is valuable for understanding its important role in reducing soil erosion and restoring ecosystems. This research selected Luodihe watershed in Changting County, Fujian
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Dicranopteris dichotoma is a pioneer herbaceous plant species that is tolerant to barrenness and drought. Mapping its biomass spatial distribution is valuable for understanding its important role in reducing soil erosion and restoring ecosystems. This research selected Luodihe watershed in Changting County, Fujian Province, China, where soil erosion has been a severe problem for a long time, as a case study to explore the method to estimate biomass, including total and aboveground biomass, through the integration of field measurements, handheld laser scanning (HLS), and airborne laser scanning (ALS) data. A stepwise regression model and an allometric equation form model were used to develop biomass estimation models based on Lidar-derived variables at typical areas and at a regional scale. The results indicate that at typical areas, both total and aboveground biomass were best estimated using an allometric equation form model when HLS-derived height and density variables were extracted from a window size of 6 m × 6 m, with the coefficients of determination (R2) of 0.64 and 0.58 and relative root mean square error (rRMSE) of 28.2% and 35.8%, respectively. When connecting HLS-estimated biomass with ALS-derived variables at a regional scale, total and aboveground biomass were effectively predicted with rRMSE values of 17.68% and 17.91%, respectively. The HLS data played an important role in linking field measurements and ALS data. This research provides a valuable method to map Dicranopteris biomass distribution using ALS data when other remotely sensed data cannot effectively estimate the understory vegetation biomass. The estimated biomass spatial pattern will be helpful to understand the role of Dicranopteris in reducing soil erosion and improving the degraded ecosystem.
Full article
(This article belongs to the Special Issue Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Monitoring)
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Open AccessArticle
MFPANet: Multi-Scale Feature Perception and Aggregation Network for High-Resolution Snow Depth Estimation
by
Liling Zhao, Junyu Chen, Muhammad Shahzad, Min Xia and Haifeng Lin
Remote Sens. 2024, 16(12), 2087; https://doi.org/10.3390/rs16122087 (registering DOI) - 9 Jun 2024
Abstract
Accurate snow depth estimation is of significant importance, particularly for preventing avalanche disasters and predicting flood seasons. The predominant approaches for such snow depth estimation, based on deep learning methods, typically rely on passive microwave remote sensing data. However, due to the low
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Accurate snow depth estimation is of significant importance, particularly for preventing avalanche disasters and predicting flood seasons. The predominant approaches for such snow depth estimation, based on deep learning methods, typically rely on passive microwave remote sensing data. However, due to the low resolution of passive microwave remote sensing data, it often results in low-accuracy outcomes, posing considerable limitations in application. To further improve the accuracy of snow depth estimation, in this paper, we used active microwave remote sensing data. We fused multi-spectral optical satellite images, synthetic aperture radar (SAR) images and land cover distribution images to generate a snow remote sensing dataset (SRSD). It is a first-of-its-kind dataset that includes active microwave remote sensing images in high-latitude regions of Asia. Using these novel data, we proposed a multi-scale feature perception and aggregation neural network (MFPANet) that focuses on improving feature extraction from multi-source images. Our systematic analysis reveals that the proposed approach is not only robust but also achieves high accuracy in snow depth estimation compared to existing state-of-the-art methods, with RMSE of 0.360 and with MAE of 0.128. Finally, we selected several representative areas in our study region and applied our method to map snow depth distribution, demonstrating its broad application prospects.
Full article
(This article belongs to the Special Issue Monitoring Cold-Region Water Cycles Using Remote Sensing Big Data)
Open AccessArticle
Preliminary Exploration of Coverage for Moon-Based/HEO Spaceborne Bistatic SAR Earth Observation in Polar Regions
by
Ke Zhang, Huadong Guo, Di Jiang, Chunming Han and Guoqiang Chen
Remote Sens. 2024, 16(12), 2086; https://doi.org/10.3390/rs16122086 (registering DOI) - 9 Jun 2024
Abstract
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To address the challenge of achieving both temporal consistency and spatial continuity in Earth observation data of polar regions, this paper proposes an innovative concept of Moon-based/Highly Elliptical Orbit (HEO) Spaceborne Bistatic Synthetic Aperture Radar (MH-BiSAR), with transmitters on the Moon and receivers
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To address the challenge of achieving both temporal consistency and spatial continuity in Earth observation data of polar regions, this paper proposes an innovative concept of Moon-based/Highly Elliptical Orbit (HEO) Spaceborne Bistatic Synthetic Aperture Radar (MH-BiSAR), with transmitters on the Moon and receivers on HEO satellites. By utilizing ephemeris data and an orbit propagator, this study explores MH-BiSAR’s geometric coverage capabilities in polar regions and conducts a preliminary analysis of its characteristics. The findings reveal that MH-BiSAR could provide continuous multi-day revisit observations of polar regions within each sidereal month, presenting a significant advantage for monitoring high-dynamic and large-scale scientific phenomena, such as polar sea ice observations. This innovative observational method offers a new perspective for polar monitoring and is expected to deepen our understanding of polar phenomena.
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Open AccessArticle
Characterizing Canopy Structure Variability in Amazonian Secondary Successions with Full-Waveform Airborne LiDAR
by
Aline D. Jacon, Lênio Soares Galvão, Rorai Pereira Martins-Neto, Pablo Crespo-Peremarch, Luiz E. O. C. Aragão, Jean P. Ometto, Liana O. Anderson, Laura Barbosa Vedovato, Celso H. L. Silva-Junior, Aline Pontes Lopes, Vinícius Peripato, Mauro Assis, Francisca R. S. Pereira, Isadora Haddad, Catherine Torres de Almeida, Henrique L. G. Cassol and Ricardo Dalagnol
Remote Sens. 2024, 16(12), 2085; https://doi.org/10.3390/rs16122085 (registering DOI) - 9 Jun 2024
Abstract
Full-waveform LiDAR (FWF) offers a promising advantage over other technologies to represent the vertical canopy structure of secondary successions in the Amazon region, as the waveform encapsulates the properties of all elements intercepting the emitted beam. In this study, we investigated modifications in
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Full-waveform LiDAR (FWF) offers a promising advantage over other technologies to represent the vertical canopy structure of secondary successions in the Amazon region, as the waveform encapsulates the properties of all elements intercepting the emitted beam. In this study, we investigated modifications in the vertical structure of the Amazonian secondary successions across the vegetation gradient from early to advanced stages of vegetation regrowth. The analysis was performed over two distinct climatic regions (Drier and Wetter), designated using the Maximum Cumulative Water Deficit (MCWD). The study area was covered by 309 sample plots distributed along 25 LiDAR transects. The plots were grouped into three successional stages (early—SS1; intermediate—SS2; advanced—SS3). Mature Forest (MF) was used as a reference of comparison. A total of 14 FWF LiDAR metrics from four categories of analysis (Height, Peaks, Understory and Gaussian Decomposition) were extracted using the Waveform LiDAR for Forestry eXtraction (WoLFeX) software (v1.1.1). In addition to examining the variation in these metrics across different successional stages, we calculated their Relative Recovery (RR) with vegetation regrowth, and evaluated their ability to discriminate successional stages using Random Forest (RF). The results showed significant differences in FWF metrics across the successional stages, and within and between sample plots and regions. The Drier region generally exhibited more pronounced differences between successional stages and lower FWF metric values compared to the Wetter region, mainly in the category of height, peaks, and Gaussian decomposition. Furthermore, the Drier region displayed a lower relative recovery of metrics in the early years of succession, compared to the areas of MF, eventually reaching rates akin to those of the Wetter region as succession progressed. Canopy height metrics such as Waveform distance (WD), and Gaussian Decomposition metrics such as Bottom of canopy (BC), Bottom of canopy distance (BCD) and Canopy distance (CD), related to the height of the lower forest stratum, were the most important attributes in discriminating successional stages in both analyzed regions. However, the Drier region exhibited superior discrimination between successional stages, achieving a weighted F1-score of 0.80 compared to 0.73 in the Wetter region. When comparing the metrics from SS in different stages to MF, our findings underscore that secondary forests achieve substantial relative recovery of FWF metrics within the initial 10 years after land abandonment. Regions with potentially slower relative recovery (e.g., Drier regions) may require longer-term planning to ensure success in providing full potential ecosystem services in the Amazon.
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(This article belongs to the Special Issue Retrieving Leaf Area Index Using Remote Sensing)
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Open AccessArticle
Reconstruction of High-Resolution 3D GPR Data from 2D Profiles: A Multiple-Point Statistical Approach
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Chongmin Zhang, Mathieu Gravey, Grégoire Mariéthoz and James Irving
Remote Sens. 2024, 16(12), 2084; https://doi.org/10.3390/rs16122084 (registering DOI) - 8 Jun 2024
Abstract
Ground-penetrating radar (GPR) is a popular geophysical tool for mapping the underground. High-resolution 3D GPR data carry a large amount of information and can greatly help to interpret complex subsurface geometries. However, such data require a dense collection along closely spaced parallel survey
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Ground-penetrating radar (GPR) is a popular geophysical tool for mapping the underground. High-resolution 3D GPR data carry a large amount of information and can greatly help to interpret complex subsurface geometries. However, such data require a dense collection along closely spaced parallel survey lines, which is time consuming and costly. In many cases, for the sake of efficiency, a choice is made during 3D acquisitions to use a larger spacing between the profile lines, resulting in a dense measurement spacing along the lines but a much coarser one in the across-line direction. Simple interpolation methods are then commonly used to increase the sampling before interpretation, which can work well when the subsurface structures are already well sampled in the across-line direction but can distort such structures when this is not the case. In this work, we address the latter problem using a novel multiple-point geostatistical (MPS) simulation methodology. For a considered 3D GPR dataset with reduced sampling in the across-line direction, we attempt to reconstruct a more densely spaced, high-resolution dataset using a series of 2D conditional stochastic simulations in both the along-line and across-line directions. For these simulations, the existing profile data serve as training images from which complex spatial patterns are quantified and reproduced. To reduce discontinuities in the generated 3D spatial structures caused by independent 2D simulations, the target profile being simulated is chosen randomly, and simulations in the along-line and across-line directions are performed alternately. We show the successful application of our approach to 100 MHz synthetic and 200 MHz field GPR data under multiple decimation scenarios where survey lines are regularly deleted from a dense 3D reference dataset, and the corresponding reconstructions are compared with the original data.
Full article
(This article belongs to the Topic Ground Penetrating Radar (GPR) Techniques and Applications)
Open AccessTechnical Note
Wind Wave Effects on the Doppler Spectrum of the Ka-Band Spaceborne Doppler Measurement
by
Miaomiao Yu, Di Zhu and Xiaolong Dong
Remote Sens. 2024, 16(12), 2083; https://doi.org/10.3390/rs16122083 (registering DOI) - 8 Jun 2024
Abstract
Sea surface wind, waves, and currents are the three basic parameters that describe the dynamic process of sea surface, and they are coupled with each other. To more accurately describe large-scale ocean motion and extract the ocean dynamic parameters, we adopt the spaceborne
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Sea surface wind, waves, and currents are the three basic parameters that describe the dynamic process of sea surface, and they are coupled with each other. To more accurately describe large-scale ocean motion and extract the ocean dynamic parameters, we adopt the spaceborne Doppler measurement to estimate the radial Doppler velocity generated by the sea surface motion. Due to the presence of wind and waves, the Doppler spectrum will be formed, shifted and broadened. Pulse-pair phase interference is used to obtain the Doppler spectrum from the sea surface echo. We simulate the Doppler spectrum with different look geometry and ocean states in a spaceborne condition. In this paper, the Doppler centroid variations are estimated after reducing the platform Doppler velocity under different observation conditions. With the increase in wind speed, the measured Doppler shift increases, and the simulated Doppler centroid accuracy is estimated. In addition, the measurement error along the trace direction is at the maximum, and the error in the cross-track is the smallest. At moderate wind-wave conditions, the Doppler velocity offset can be less than 0.1 m/s.
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(This article belongs to the Special Issue Doppler Radar: Signal, Data and Applications)
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Open AccessArticle
LRMSNet: A New Lightweight Detection Algorithm for Multi-Scale SAR Objects
by
Hailang Wu, Hanbo Sang, Zenghui Zhang and Weiwei Guo
Remote Sens. 2024, 16(12), 2082; https://doi.org/10.3390/rs16122082 (registering DOI) - 8 Jun 2024
Abstract
In recent years, deep learning has found widespread application in SAR image object detection. However, when detecting multi-scale targets against complex backgrounds, these models often struggle to strike a balance between accuracy and speed. Furthermore, there is a continuous need to enhance the
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In recent years, deep learning has found widespread application in SAR image object detection. However, when detecting multi-scale targets against complex backgrounds, these models often struggle to strike a balance between accuracy and speed. Furthermore, there is a continuous need to enhance the performance of current models. Hence, this paper proposes LRMSNet, a new multi-scale target detection model designed specifically for SAR images in complex backgrounds. Firstly, the paper introduces an attention module designed to enhance contextual information aggregation and capture global features, which is integrated into a backbone network with an expanded receptive field for improving SAR image feature extraction. Secondly, this paper develops an information aggregation module to effectively fuse different feature layers of the backbone network. Lastly, to better integrate feature information at various levels, this paper designs a multi-scale aggregation network. We validate the effectiveness of our method on three different SAR object detection datasets (MSAR-1.0, SSDD, and HRSID). Experimental results demonstrate that LRMSNet achieves outstanding performance with a mean average accuracy (mAP) of 95.2%, 98.9%, and 93.3% on the MSAR-1.0, SSDD, and HRSID datasets, respectively, with only 3.46 M parameters and 12.6 G floating-point operation cost (FLOPs). When compared with existing SAR object detection models on the MSAR-1.0 dataset, LRMSNet achieves state-of-the-art (SOTA) performance, showcasing its superiority in addressing SAR detection challenges in large-scale complex environments and across various object scales.
Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Image Processing and Information Extraction)
Open AccessArticle
Depth-Guided Dehazing Network for Long-Range Aerial Scenes
by
Yihu Wang, Jilin Zhao, Liangliang Yao and Changhong Fu
Remote Sens. 2024, 16(12), 2081; https://doi.org/10.3390/rs16122081 (registering DOI) - 8 Jun 2024
Abstract
Over the past few years, the applications of unmanned aerial vehicles (UAVs) have greatly increased. However, the decrease in clarity in hazy environments is an important constraint on their further development. Current research on image dehazing mainly focuses on normal scenes at close
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Over the past few years, the applications of unmanned aerial vehicles (UAVs) have greatly increased. However, the decrease in clarity in hazy environments is an important constraint on their further development. Current research on image dehazing mainly focuses on normal scenes at close range or mid-range, while ignoring long-range scenes such as aerial perspective. Furthermore, based on the atmospheric scattering model, the inclusion of depth information is essential for the procedure of image dehazing, especially when dealing with images that exhibit substantial variations in depth. However, most existing models neglect this important information. Consequently, these state-of-the-art (SOTA) methods perform inadequately in dehazing when applied to long-range images. For the purpose of dealing with the above challenges, we propose the construction of a depth-guided dehazing network designed specifically for long-range aerial scenes. Initially, we introduce the depth prediction subnetwork to accurately extract depth information from long-range aerial images, taking into account the substantial variance in haze density. Subsequently, we propose the depth-guided attention module, which integrates a depth map with dehazing features through the attention mechanism, guiding the dehazing process and enabling the effective removal of haze in long-range areas. Furthermore, considering the unique characteristics of long-range aerial scenes, we introduce the UAV-HAZE dataset, specifically designed for training and evaluating dehazing methods in such scenarios. Finally, we conduct extensive experiments to test our method against several SOTA dehazing methods and demonstrate its superiority over others.
Full article
(This article belongs to the Topic Computer Vision and Image Processing, 2nd Edition)
Open AccessArticle
Identifying Plausible Labels from Noisy Training Data for a Land Use and Land Cover Classification Application in Amazônia Legal
by
Maximilian Hell and Melanie Brandmeier
Remote Sens. 2024, 16(12), 2080; https://doi.org/10.3390/rs16122080 (registering DOI) - 8 Jun 2024
Abstract
Most studies in the field of land use and land cover (LULC) classification in remote sensing rely on supervised classification, which requires a substantial amount of accurate label data. However, reliable data are often not immediately available, and are obtained through time-consuming manual
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Most studies in the field of land use and land cover (LULC) classification in remote sensing rely on supervised classification, which requires a substantial amount of accurate label data. However, reliable data are often not immediately available, and are obtained through time-consuming manual labor. One potential solution to this problem is the use of already available classification maps, which may not be the true ground truth and may contain noise from multiple possible sources. This is also true for the classification maps of the MapBiomas project , which provides land use and land cover (LULC) maps on a yearly basis, classifying the Amazon basin into more than 24 classes based on the Landsat data. In this study, we utilize the Sentinel-2 data with a higher spatial resolution in conjunction with the MapBiomas maps to evaluate a proposed noise removal method and to improve classification results. We introduce a novel noise detection method that relies on identifying anchor points in feature space through clustering with self-organizing maps (SOM). The pixel label is relabeled using nearest neighbor rules, or can be removed if it is unknown. A challenge in this approach is the quantification of noise in such a real-world dataset. To overcome this problem, highly reliable validation sets were manually created for quantitative performance assessment. The results demonstrate a significant increase in overall accuracy compared to MapBiomas labels, from 79.85% to 89.65%. Additionally, we trained the L2HNet using both MapBiomas labels and the filtered labels from our approach. The overall accuracy for this model reached 93.75% with the filtered labels, compared to the baseline of 74.31%. This highlights the significance of noise detection and filtering in remote sensing, and emphasizes the need for further research in this area.
Full article
(This article belongs to the Section AI Remote Sensing)
Open AccessArticle
Deep Learning Hyperspectral Pansharpening on Large-Scale PRISMA Dataset
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Simone Zini, Mirko Paolo Barbato, Flavio Piccoli and Paolo Napoletano
Remote Sens. 2024, 16(12), 2079; https://doi.org/10.3390/rs16122079 (registering DOI) - 8 Jun 2024
Abstract
Hyperspectral pansharpening is crucial for the improvement of the usability of images in various applications. However, it remains underexplored due to a scarcity of data. The primary goal of pansharpening is to enhance the spatial resolution of hyperspectral images by reconstructing missing spectral
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Hyperspectral pansharpening is crucial for the improvement of the usability of images in various applications. However, it remains underexplored due to a scarcity of data. The primary goal of pansharpening is to enhance the spatial resolution of hyperspectral images by reconstructing missing spectral information without compromising consistency with the original data. This paper addresses the data gap by presenting a new hyperspectral dataset specifically designed for pansharpening and the evaluation of several deep learning strategies using this dataset. The new dataset has two crucial features that make it invaluable for deep learning hyperspectral pansharpening research. (1) It presents the highest cardinality of images in the state of the art, making it the first statistically relevant dataset for hyperspectral pansharpening evaluation, and (2) it includes a wide variety of scenes, ensuring robust generalization capabilities for various approaches. The data, collected by the ASI PRISMA satellite, cover about 262,200 km and their heterogeneity is ensured by a random sampling of the Earth’s surface. The analysis of the deep learning methods consists in the adaptation of these approaches to the PRISMA hyperspectral data and the quantitative and qualitative evaluation of their performance in this new scenario. The investigation included two settings: Reduced Resolution (RR) to evaluate the techniques in a controlled environment and Full Resolution (FR) for a real-world evaluation. In addition, for the sake of completeness, we have also included machine-learning-free approaches in both scenarios. Our comprehensive analysis reveals that data-driven neural network methods significantly outperform traditional approaches, demonstrating a superior adaptability and performance in hyperspectral pansharpening under both RR and FR protocols.
Full article
(This article belongs to the Special Issue New Deep Learning Paradigms for Multisource Remote Sensing Data Fusion and Classification)
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Open AccessArticle
RadWet-L: A Novel Approach for Mapping of Inundation Dynamics of Forested Wetlands Using ALOS-2 PALSAR-2 L-Band Radar Imagery
by
Gregory Oakes, Andy Hardy, Pete Bunting and Ake Rosenqvist
Remote Sens. 2024, 16(12), 2078; https://doi.org/10.3390/rs16122078 (registering DOI) - 8 Jun 2024
Abstract
The ability to accurately map tropical wetland dynamics can significantly contribute to a number of areas, including food and water security, protection and enhancement of ecosystems, flood hazard management, and our understanding of natural greenhouse gas emissions. Yet currently, there is not a
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The ability to accurately map tropical wetland dynamics can significantly contribute to a number of areas, including food and water security, protection and enhancement of ecosystems, flood hazard management, and our understanding of natural greenhouse gas emissions. Yet currently, there is not a tractable solution for mapping tropical forested wetlands at high spatial and temporal resolutions at a regional scale. This means that we lack accurate and up-to-date information about some of the world’s most significant wetlands, including the Amazon Basin. RadWet-L is an automated machine-learning classification technique for the mapping of both inundated forests and open water using ALOS ScanSAR data. We applied and validated RadWet-L for the Amazon Basin. The proposed method is computationally light and transferable across the range of landscape types in the Amazon Basin allowing, for the first time, regional inundation maps to be produced every 42 days at 50 m resolution over the period 2019–2023. Time series estimates of inundation extent from RadWet-L were significantly correlated with NASA-GFZ GRACE-FO water thickness (Pearson’s r = 0.96, p < 0.01), USDA G-REALM lake hight (Pearson’s r between 0.63 and 0.91, p < 0.01), and in situ river stage measurements (Pearson’s r between 0.78 and 0.94, p < 0.01). Additionally, we conducted an evaluation of 11,162 points against the input ScanSAR data revealing spatial and temporal consistency in the approach (F1 score = 0.97). Serial classifications of ALOS-2 PALSAR-2 ScanSAR data by RadWet-L can provide unique insights into the spatio-temporal inundation dynamics within the Amazon Basin. Understanding these dynamics can inform policy in the sustainable use of these wetlands, as well as the impacts of inundation dynamics on biodiversity and greenhouse gas budgets.
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(This article belongs to the Special Issue Remote Sensing for Mapping and Monitoring Wetlands and Their Ecosystems)
Open AccessArticle
U-Net Ensemble for Enhanced Semantic Segmentation in Remote Sensing Imagery
by
Ivica Dimitrovski, Vlatko Spasev, Suzana Loshkovska and Ivan Kitanovski
Remote Sens. 2024, 16(12), 2077; https://doi.org/10.3390/rs16122077 (registering DOI) - 8 Jun 2024
Abstract
Semantic segmentation of remote sensing imagery stands as a fundamental task within the domains of both remote sensing and computer vision. Its objective is to generate a comprehensive pixel-wise segmentation map of an image, assigning a specific label to each pixel. This facilitates
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Semantic segmentation of remote sensing imagery stands as a fundamental task within the domains of both remote sensing and computer vision. Its objective is to generate a comprehensive pixel-wise segmentation map of an image, assigning a specific label to each pixel. This facilitates in-depth analysis and comprehension of the Earth’s surface. In this paper, we propose an approach for enhancing semantic segmentation performance by employing an ensemble of U-Net models with three different backbone networks: Multi-Axis Vision Transformer, ConvFormer, and EfficientNet. The final segmentation maps are generated through a geometric mean ensemble method, leveraging the diverse representations learned by each backbone network. The effectiveness of the base U-Net models and the proposed ensemble is evaluated on multiple datasets commonly used for semantic segmentation tasks in remote sensing imagery, including LandCover.ai, LoveDA, INRIA, UAVid, and ISPRS Potsdam datasets. Our experimental results demonstrate that the proposed approach achieves state-of-the-art performance, showcasing its effectiveness and robustness in accurately capturing the semantic information embedded within remote sensing images.
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(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science)
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Open AccessArticle
DAENet: Deformable Attention Edge Network for Automatic Coastline Extraction from Satellite Imagery
by
Buyun Kang, Jian Wu, Jinyong Xu and Changshang Wu
Remote Sens. 2024, 16(12), 2076; https://doi.org/10.3390/rs16122076 - 7 Jun 2024
Abstract
Sea–land segmentation (SLS) is a crucial step in coastline extraction. In CNN-based approaches for coastline feature extraction, downsampling is commonly used to reduce computational demands. However, this method may unintentionally discard small-scale features, hindering the capture of essential global contextual information and clear
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Sea–land segmentation (SLS) is a crucial step in coastline extraction. In CNN-based approaches for coastline feature extraction, downsampling is commonly used to reduce computational demands. However, this method may unintentionally discard small-scale features, hindering the capture of essential global contextual information and clear edge information necessary for SLS. To solve this problem, we propose a novel U-Net structure called Deformable Attention Edge Network (DAENet), which integrates edge enhancement algorithms and a deformable self-attention mechanism. First of all, we designed a multi-scale transformation (MST) to enhance edge feature extraction and model convergence through multi-scale transformation and edge detection, enabling the network to capture spatial–spectral changes more effectively. This is crucial because the deformability of the Deformable Attention Transformer (DAT) modules increases training costs for model convergence. Moreover, we introduced DAT, which leverages its powerful global modeling capabilities and deformability to enhance the model’s recognition of irregular coastlines. Finally, we integrated the Local Adaptive Multi-Head Attention-based Edge Detection (LAMBA) module to enhance the spatial differentiation of edge features. We designed each module to address the complexity of SLS. Experiments on benchmark datasets demonstrate the superiority of the proposed DAENet over state-of-the-art methods. Additionally, we conducted ablation experiments to evaluate the effectiveness of each module.
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(This article belongs to the Special Issue Artificial Intelligence for Ocean Remote Sensing)
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Open AccessArticle
Multi-Shot Simultaneous Deghosting for Virtual-Shot Gathers via Integrated Sparse and Nuclear Norm Constraint Inversion
by
Junming Zhang, Deli Wang, Bin Hu, Xiangbo Gong, Yifei Chen and Yang Zhang
Remote Sens. 2024, 16(12), 2075; https://doi.org/10.3390/rs16122075 - 7 Jun 2024
Abstract
Seismic interferometry is a key technology in geophysical exploration, having achieved significant developments in constructing virtual seismic responses, overcoming the limitation of traditional exploration. However, non-physical reflections in virtual-shot gathers pose challenges for data processing and interpretation. This study focuses on deghosting in
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Seismic interferometry is a key technology in geophysical exploration, having achieved significant developments in constructing virtual seismic responses, overcoming the limitation of traditional exploration. However, non-physical reflections in virtual-shot gathers pose challenges for data processing and interpretation. This study focuses on deghosting in virtual-shot gather data processing. We propose a novel method that integrates sparse and nuclear norm constraint inversion for multi-shot simultaneous deghosting. Initially, a pseudo 3D data cube is created to enhance computational efficiency and lay the foundation for subsequent continuity regularization. Subsequently, an inversion framework is constructed to improve deghosting precision and stability by combining sparse and nuclear norm constraint inversion. Both synthetic and field examples demonstrate the superiority of our method, offering a new paradigm for virtual-shot gather data processing, and representing a major advancement in overcoming the inherent limitations of seismic interferometry.
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(This article belongs to the Special Issue New Technologies, Methods and Studies for Seismic and Radar Subsurface Exploration)
Open AccessArticle
Characteristics of Cloud and Aerosol Derived from Lidar Observations during Winter in Lhasa, Tibetan Plateau
by
Xiang Jin, Siyang Cheng, Xiangdong Zheng, Jianzhong Ma, Zangjia Luo, Guangqiang Fan, Yan Xiang and Tianshu Zhang
Remote Sens. 2024, 16(12), 2074; https://doi.org/10.3390/rs16122074 - 7 Jun 2024
Abstract
In order to investigate the variations of cloud and aerosol vertical profiles over the Tibetan Plateau (TP) in winter, we performed ground-based lidar observations in Lhasa, a city on the TP, from November 2021 to January 2022. The profiles of extinction coefficient, depolarization
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In order to investigate the variations of cloud and aerosol vertical profiles over the Tibetan Plateau (TP) in winter, we performed ground-based lidar observations in Lhasa, a city on the TP, from November 2021 to January 2022. The profiles of extinction coefficient, depolarization ratio, and signal-to-noise ratio (SNR) were retrieved using the atmospheric echo signals collected by the lidar. Clouds were identified by the range-correction echo signals and classified into water clouds, mixed clouds, horizontally oriented ice crystal clouds (HOICC), and ice clouds by the depolarization ratio and the hourly temperature from the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5). The clouds mainly appeared at a height of 3~5 km from 14:00–22:00 Beijing Time throughout the field campaign. The height and frequency (~30%) for cloud appearance were significantly lower than that reported in previous studies in summer. The cloud categories were dominated by mixed clouds and ice clouds during the observation period. The proportions of ice clouds gradually increased with increasing heights. After eliminating profiles influenced by clouds, the aerosol extinction coefficient and depolarization ratio were obtained, and the atmospheric boundary layer height (ABLH) was calculated. The aerosol extinction coefficient decreased with increasing height in the ABLH, and there were no obvious changes for the aerosol extinction coefficient above the ABL. The aerosol extinction coefficients near the Earth’s surface presented two peaks, appearing in the morning and evening, respectively. The high aerosols at the surface in the morning continually spread upward for 4–5 h and finally reached an altitude of 1 km with the development of ABLH. In addition, the depolarization ratio of aerosols decreased slowly with increasing altitudes. There was no obvious diurnal variation for depolarization ratios, indicating partly that the source of aerosols did not change significantly. These results are beneficial in understanding the evolution of cloud and aerosol vertical profiles over the TP.
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(This article belongs to the Topic Accessing and Analyzing Air Quality and Atmospheric Environment)
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Open AccessArticle
Estimation of Rice Leaf Area Index Utilizing a Kalman Filter Fusion Methodology Based on Multi-Spectral Data Obtained from Unmanned Aerial Vehicles (UAVs)
by
Minglei Yu, Jiaoyang He, Wanyu Li, Hengbiao Zheng, Xue Wang, Xia Yao, Tao Cheng, Xiaohu Zhang, Yan Zhu, Weixing Cao and Yongchao Tian
Remote Sens. 2024, 16(12), 2073; https://doi.org/10.3390/rs16122073 - 7 Jun 2024
Abstract
The rapid and accurate estimation of leaf area index (LAI) through remote sensing holds significant importance for precise crop management. However, the direct construction of a vegetation index model based on multi-spectral data lacks robustness and spatiotemporal expansibility, making its direct application in
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The rapid and accurate estimation of leaf area index (LAI) through remote sensing holds significant importance for precise crop management. However, the direct construction of a vegetation index model based on multi-spectral data lacks robustness and spatiotemporal expansibility, making its direct application in practical production challenging. This study aimed to establish a simple and effective method for LAI estimation to address the issue of poor accuracy and stability that is encountered by vegetation index models under varying conditions. Based on seven years of field plot trials with different varieties and nitrogen fertilizer treatments, the Kalman filter (KF) fusion method was employed to integrate the estimated outcomes of multiple vegetation index models, and the fusion process was investigated by comparing and analyzing the relationship between fixed and dynamic variances alongside the fusion accuracy of optimal combinations during different growth stages. A novel multi-model integration fusion method, KF-DGDV (Kalman Filtering with Different Growth Periods and Different Vegetation Index Models), which combines the growth characteristics and uncertainty of LAI, was designed for the precise monitoring of LAI across various growth phases of rice. The results indicated that the KF-DGDV technique exhibits a superior accuracy in estimating LAI compared with statistical data fusion and the conventional vegetation index model method. Specifically, during the tillering to booting stage, a high R2 value of 0.76 was achieved, while at the heading to maturity stage, it reached 0.66. In contrast, within the framework of the traditional vegetation index model, the red-edge difference vegetation index (DVIREP) model demonstrated a superior performance, with an R2 value of 0.65, during tillering to booting stage, and 0.50 during the heading to maturity stage, respectively. The multi-model integration method (MME) yielded an R2 value of 0.67 for LAI estimation during the tillering to booting stage, and 0.53 during the heading to maturity stage. Consequently, KF-DGDV presented an effective and stable real-time quantitative estimation method for LAI in rice.
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(This article belongs to the Special Issue UAS Technology and Applications in Precision Agriculture)
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