Remote Sensing

23 pages, 34661 KiB

Open AccessArticle

SSN: Scale Selection Network for Multi-Scale Object Detection in Remote Sensing Images

by Zhili Lin and Biao Leng

Remote Sens. 2024, 16(19), 3697; https://doi.org/10.3390/rs16193697 - 4 Oct 2024

The rapid growth of deep learning technology has made object detection in remote sensing images an important aspect of computer vision, finding applications in military surveillance, maritime rescue, and environmental monitoring. Nonetheless, the capture of remote sensing images at high altitudes causes significant [...] Read more.

The rapid growth of deep learning technology has made object detection in remote sensing images an important aspect of computer vision, finding applications in military surveillance, maritime rescue, and environmental monitoring. Nonetheless, the capture of remote sensing images at high altitudes causes significant scale variations, resulting in a heterogeneous range of object scales. These varying scales pose significant challenges for detection algorithms. To solve the scale variation problem, traditional detection algorithms compute multi-layer feature maps. However, this approach introduces significant computational redundancy. Inspired by the mechanism of cognitive scaling mechanisms handling multi-scale information, we propose a novel Scale Selection Network (SSN) to eliminate computational redundancy through scale attentional allocation. In particular, we have devised a lightweight Landmark Guided Scale Attention Network, which is capable of predicting potential scales in an image. The detector only needs to focus on the selected scale features, which greatly reduces the inference time. Additionally, a fast Reversible Scale Semantic Flow Preserving strategy is proposed to directly generate multi-scale feature maps for detection. Experiments demonstrate that our method facilitates the acceleration of image pyramid-based detectors by approximately 5.3 times on widely utilized remote sensing object detection benchmarks. Full article

(This article belongs to the Section Remote Sensing Image Processing)

25 pages, 5898 KiB

Open AccessArticle

Progressive Unsupervised Domain Adaptation for Radio Frequency Signal Attribute Recognition across Communication Scenarios

by Jing Xiao, Hang Zhang, Zeqi Shao, Yikai Zheng and Wenrui Ding

Remote Sens. 2024, 16(19), 3696; https://doi.org/10.3390/rs16193696 - 4 Oct 2024

Abstract

As the development of low-altitude economies and aerial countermeasures continues, the safety of unmanned aerial vehicles becomes increasingly critical, making emitter identification in remote sensing practices more essential. Effective recognition of radio frequency (RF) signal attributes is a prerequisite for identifying emitters. However, [...] Read more.

As the development of low-altitude economies and aerial countermeasures continues, the safety of unmanned aerial vehicles becomes increasingly critical, making emitter identification in remote sensing practices more essential. Effective recognition of radio frequency (RF) signal attributes is a prerequisite for identifying emitters. However, due to diverse wireless communication environments, RF signals often face challenges from complex and time-varying wireless channel conditions. These challenges lead to difficulties in data collection and annotation, as well as disparities in data distribution across different communication scenarios. To address this issue, this paper proposes a progressive maximum similarity-based unsupervised domain adaptation (PMS-UDA) method for RF signal attribute recognition. First, we introduce a noise perturbation consistency optimization method to enhance the robustness of the PMS-UDA method under low signal-to-noise conditions. Subsequently, a progressive label alignment training method is proposed, combining sample-level maximum correlation with distribution-level maximum similarity optimization techniques to enhance the similarity of cross-domain features. Finally, a domain adversarial optimization method is employed to extract domain-independent features, reducing the impact of channel scenarios. The experimental results demonstrate that the PMS-UDA method achieves superior recognition performance in automatic modulation recognition and RF fingerprint identification tasks, as well as across both ground-to-ground and air-to-ground scenarios, compared to baseline methods. Full article

(This article belongs to the Special Issue Signal Processing Theory and Methods in Remote Sensing (Second Edition))

19 pages, 5136 KiB

Open AccessArticle

Multi-Window Fusion Spatial-Frequency Joint Self-Attention for Remote-Sensing Image Super-Resolution

by Ziang Li, Wen Lu, Zhaoyang Wang, Jian Hu, Zeming Zhang and Lihuo He

Remote Sens. 2024, 16(19), 3695; https://doi.org/10.3390/rs16193695 - 4 Oct 2024

Abstract

Remote-sensing images typically feature large dimensions and contain repeated texture patterns. To effectively capture finer details and encode comprehensive information, feature-extraction networks with larger receptive fields are essential for remote-sensing image super-resolution tasks. However, mainstream methods based on stacked Transformer modules suffer from [...] Read more.

Remote-sensing images typically feature large dimensions and contain repeated texture patterns. To effectively capture finer details and encode comprehensive information, feature-extraction networks with larger receptive fields are essential for remote-sensing image super-resolution tasks. However, mainstream methods based on stacked Transformer modules suffer from limited receptive fields due to fixed window sizes, impairing long-range dependency capture and fine-grained texture reconstruction. In this paper, we propose a spatial-frequency joint attention network based on multi-window fusion (MWSFA). Specifically, our approach introduces a multi-window fusion strategy, which merges windows with similar textures to allow self-attention mechanisms to capture long-range dependencies effectively, therefore expanding the receptive field of the feature extractor. Additionally, we incorporate a frequency-domain self-attention branch in parallel with the original Transformer architecture. This branch leverages the global characteristics of the frequency domain to further extend the receptive field, enabling more comprehensive self-attention calculations across different frequency bands and better utilization of consistent frequency information. Extensive experiments on both synthetic and real remote-sensing datasets demonstrate that our method achieves superior visual reconstruction effects and higher evaluation metrics compared to other super-resolution methods. Full article

► Show Figures

Figure 1

17 pages, 6526 KiB

Open AccessArticle

A New Method for Top-Down Inversion Estimation of Carbon Dioxide Flux Based on Deep Learning

by Hui Wang, Dan Li, Ruilin Zhou, Xiaoyu Hu, Leyi Wang and Lang Zhang

Remote Sens. 2024, 16(19), 3694; https://doi.org/10.3390/rs16193694 - 3 Oct 2024

Abstract

Estimation of anthropogenic carbon dioxide (CO₂) emission sources and natural sinks (i.e., CO₂ fluxes) is essential for the development of climate policies. Satellite observations provide an opportunity for top-down inversion of CO₂ fluxes, which can be used to improve [...] Read more.

Estimation of anthropogenic carbon dioxide (CO₂) emission sources and natural sinks (i.e., CO₂ fluxes) is essential for the development of climate policies. Satellite observations provide an opportunity for top-down inversion of CO₂ fluxes, which can be used to improve the results of bottom-up estimation. This study proposes to develop a new top-down CO₂ flux estimation method based on deep learning, as well as satellite observations, and an atmospheric chemical transport model. This method utilizes two deep learning models: the concentration correction model and the concentration–flux inversion model. The former optimizes the GEOS-Chem-simulated CO₂ concentration using Orbiting Carbon Observatory-2 (OCO-2) satellite observations, while the latter establishes the complicated relationship between CO₂ concentration and CO₂ flux. Results showed that both deep learning models demonstrated excellent prediction performance, with a mean bias of 0.461 ppm for the concentration correction model and an annual mean correlation coefficient of 0.920 for the concentration–flux inversion model. A posterior CO₂ flux was obtained through a two-step optimization process using these well-trained models. Our findings indicate that the posterior estimations of CO₂ flux sources in eastern China and northern Europe have been significantly reduced compared to the prior estimations. This study provides a new perspective on top-down CO₂ flux inversion using satellite observation. With advancements in deep learning algorithms and increased satellite observations, this method may become an effective approach for CO₂ flux inversion in the future. Full article

► Show Figures

Figure 1

16 pages, 14213 KiB

Open AccessArticle

Bridging the Terrestrial Water Storage Anomalies between the GRACE/GRACE-FO Gap Using BEAST + GMDH Algorithm

by Nijia Qian, Jingxiang Gao, Zengke Li, Zhaojin Yan, Yong Feng, Zhengwen Yan and Liu Yang

Remote Sens. 2024, 16(19), 3693; https://doi.org/10.3390/rs16193693 - 3 Oct 2024

Abstract

Regarding the terrestrial water storage anomaly (TWSA) gap between the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (-FO) gravity satellite missions, a BEAST (Bayesian estimator of abrupt change, seasonal change and trend)+GMDH (group method of data handling) gap-filling scheme driven by [...] Read more.

Regarding the terrestrial water storage anomaly (TWSA) gap between the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (-FO) gravity satellite missions, a BEAST (Bayesian estimator of abrupt change, seasonal change and trend)+GMDH (group method of data handling) gap-filling scheme driven by hydrological and meteorological data is proposed. Considering these driving data usually cannot fully capture the trend changes of the TWSA time series, we propose first to use the BEAST algorithm to perform piecewise linear detrending for the TWSA series and then fill the gap of the detrended series using the GMDH algorithm. The complete gap-filling TWSAs can be readily obtained after adding back the previously removed piecewise trend. By comparing the simulated gap filled by BEAST + GMDH using Multiple Linear Regression and Singular Spectrum Analysis with reference values, the results show that the BEAST + GMDH scheme is superior to the latter two in terms of the correlation coefficient, Nash-efficiency coefficient, and root-mean-square error. The real GRACE/GFO gap filled by BEAST + GMDH is consistent with those from hydrological models, Swarm TWSAs, and other literature regarding spatial distribution patterns. The correlation coefficients there between are, respectively, above 0.90, 0.80, and 0.90 in most of the global river basins. Full article

(This article belongs to the Section Earth Observation Data)

► Show Figures

Figure 1

24 pages, 3511 KiB

Open AccessArticle

Enhanced Impacts of Extreme Weather Events on Forest: The Upper Valtellina (Italy) Case Study

by Blanka Barbagallo, Nicolò Rocca, Lorenzo Cresi, Guglielmina Adele Diolaiuti and Antonella Senese

Remote Sens. 2024, 16(19), 3692; https://doi.org/10.3390/rs16193692 - 3 Oct 2024

Abstract

Extreme weather events are increasingly recognized as major stress factors for forest ecosystems, causing both immediate and long-term effects. This study focuses on the impacts experienced by the forests of Valdisotto, Valfurva, and Sondalo (28% of the total area is covered by forests) [...] Read more.

Extreme weather events are increasingly recognized as major stress factors for forest ecosystems, causing both immediate and long-term effects. This study focuses on the impacts experienced by the forests of Valdisotto, Valfurva, and Sondalo (28% of the total area is covered by forests) in Upper Valtellina (Italy) due to the Vaia storm that occurred in October 2018. To define the immediate impacts of Vaia, we assess the economic value of forest ecosystem services (ESs), particularly those provided by timber production and carbon sequestration, pre- and post-Vaia and during the emergency period. We used the market price method to assess the economic values of timber production and carbon sequestration, as these are considered to be marketable goods. Based on data processed from Sentinel-2 satellite images (with a spatial resolution of 10 m), our results show that, despite the reduction in forest area (−2.02%) and timber stock (−2.38%), the economic value of the timber production increased after Vaia due to higher timber prices (i.e., from a total of €124.97 million to €130.72 million). However, considering the whole emergency period (2019–2020), the total losses are equal to €5.10 million for Valdisotto, €0.32 million for Valfurva, and €0.43 million for Sondalo. Instead, an economic loss of 2.88% is experienced for carbon sequestration, with Valdisotto being the more affected municipality (−4.48% of the pre-Vaia economic value). In terms of long-term impacts, we discuss the enhanced impacts due to the spread of the bark beetle Ips typopgraphus. Full article

(This article belongs to the Special Issue Remote Sensing for Forest Morphological and Physiological Traits Monitoring)

27 pages, 5530 KiB

Open AccessArticle

Marine Radar Constant False Alarm Rate Detection in Generalized Extreme Value Distribution Based on Space-Time Adaptive Filtering Clutter Statistical Analysis

by Baotian Wen, Zhizhong Lu and Bowen Zhou

Remote Sens. 2024, 16(19), 3691; https://doi.org/10.3390/rs16193691 - 3 Oct 2024

Abstract

The performance of marine radar constant false alarm rate (CFAR) detection method is significantly influenced by the modeling of sea clutter distribution and detector decision rules. The false alarm rate and detection rate are therefore unstable. In order to address low CFAR detection [...] Read more.

The performance of marine radar constant false alarm rate (CFAR) detection method is significantly influenced by the modeling of sea clutter distribution and detector decision rules. The false alarm rate and detection rate are therefore unstable. In order to address low CFAR detection performance and the modeling problem of non-uniform, non-Gaussian, and non-stationary sea clutter distribution in marine radar images, in this paper, a CFAR detection method in generalized extreme value distribution modeling based on marine radar space-time filtering background clutter is proposed. Initially, three-dimensional (3D) frequency wave-number (space-time) domain adaptive filter is employed to filter the original radar image, so as to obtain uniform and stable background clutter. Subsequently, generalized extreme value (GEV) distribution is introduced to integrally model the filtered background clutter. Finally, Inclusion/Exclusion (IE) with the best performance under the GEV distribution is selected as the clutter range profile CFAR (CRP-CFAR) detector decision rule in the final detection. The proposed method is verified by utilizing real marine radar image data. The results indicate that when the

P_{f a}

is set at 0.0001, the proposed method exhibits an average improvement in

P_{D}

of 2.3% compared to STAF-RCBD-CFAR, and a 6.2% improvement compared to STCS-WL-CFAR. When the

P_{f a}

is set at 0.001, the proposed method exhibits an average improvement in

P_{D}

of 6.9% compared to STAF-RCBD-CFAR, and a 9.6% improvement compared to STCS-WL-CFAR. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series — Recent Advances in Ocean Radar)

25 pages, 6686 KiB

Open AccessArticle

Comparative Analysis and Optimal Selection of Calibration Functions in Pure Rotational Raman Lidar Technique

by Yinghong Yu, Siying Chen, Wangshu Tan, Rongzheng Cao, Yixuan Xie, He Chen, Pan Guo, Jie Yu, Rui Hu, Haokai Yang and Xin Li

Remote Sens. 2024, 16(19), 3690; https://doi.org/10.3390/rs16193690 - 3 Oct 2024

Abstract

The pure rotational Raman (PRR) lidar technique relies on calibration functions (CFs) to extract temperature information from raw detection data. The choice of CF significantly impacts the accuracy of the retrieved temperature. In this study, we propose a method that combines multiple Monte [...] Read more.

The pure rotational Raman (PRR) lidar technique relies on calibration functions (CFs) to extract temperature information from raw detection data. The choice of CF significantly impacts the accuracy of the retrieved temperature. In this study, we propose a method that combines multiple Monte Carlo simulation experiments with a statistical analysis, and we first conduct simulated comparisons of the calibration effects of different CFs while considering the impact of noise. We categorized ten common CFs into four groups based on their functional form and the number of calibration coefficients. Based on functional form, specifically, we defined 1/T = f(lnQ) as a forward calibration function (FCF) and lnQ = g(1/T) as a backward calibration function (BCF). Here, T denotes temperature, and Q denotes the signal intensity ratio. Their performance within and outside the calibration interval is compared across different integration times, smoothing methods, and reference temperature ranges. The results indicate that CFs of the same category exhibit similar calibration effects, while those of different categories exhibit notable differences. Within the calibration interval, the FCF performs better, especially with more coefficients. However, outside the calibration interval, the linear calibration function (which can be considered a two-coefficient FCF) has an obvious advantage. Conclusions based on the simulation results are validated with actual data, and the factors influencing calibration errors are discussed. Utilizing these findings to guide CF selection can enhance the accuracy and stability of PRR lidar detection. Full article

(This article belongs to the Special Issue Feature Papers of Section “Atmospheric Remote Sensing” (Second Edition))

19 pages, 8705 KiB

Open AccessArticle

The First Validation of Aerosol Optical Parameters Retrieved from the Terrestrial Ecosystem Carbon Inventory Satellite (TECIS) and Its Application

by Yijie Ren, Binglong Chen, Lingbing Bu, Gen Hu, Jingyi Fang and Pasindu Liyanage

Remote Sens. 2024, 16(19), 3689; https://doi.org/10.3390/rs16193689 - 3 Oct 2024

Abstract

In August 2022, China successfully launched the Terrestrial Ecosystem Carbon Inventory Satellite (TECIS). The primary payload of this satellite is an onboard multi-beam lidar system, which is capable of observing aerosol optical parameters on a global scale. This pioneering study used the Fernald [...] Read more.

In August 2022, China successfully launched the Terrestrial Ecosystem Carbon Inventory Satellite (TECIS). The primary payload of this satellite is an onboard multi-beam lidar system, which is capable of observing aerosol optical parameters on a global scale. This pioneering study used the Fernald forward integration method to retrieve aerosol optical parameters based on the Level 2 data of the TECIS, including the aerosol depolarization ratio, aerosol backscatter coefficient, aerosol extinction coefficient, and aerosol optical depth (AOD). The validation of the TECIS-retrieved aerosol optical parameters was conducted using CALIPSO Level 1 and Level 2 data, with relative errors within 30%. A comparison of the AOD retrieved from the TECIS with the AERONET and MODIS AOD products yielded correlation coefficients greater than 0.7 and 0.6, respectively. The relative error of aerosol optical parameter profiles compared with ground-based measurements for CALIPSO was within 40%. Additionally, the correlation coefficients R² with MODIS and AERONET AOD were approximately between 0.5 and 0.7, indicating the high accuracy of TECIS retrievals. Utilizing the TECIS retrieval results, combined with ground air quality monitoring data and HYSPLIT outcomes, a typical dust transport event was analyzed from 2 to 7 April 2023. The results indicate that dust was transported from the Taklamakan Desert in Xinjiang, China, to Henan and Anhui provinces, with a gradual decrease in the aerosol depolarization ratio and backscatter coefficient during the transport process, causing varying degrees of pollution in the downstream regions. This research verifies the accuracy of the retrieval algorithm through multi-source data comparison and demonstrates the potential application of the TECIS in the field of aerosol science for the first time. It enables the fine-scale regional monitoring of atmospheric aerosols and provides reliable data support for the three-dimensional distribution of global aerosols and related scientific applications. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

23 pages, 7292 KiB

Open AccessArticle

Coastal Vulnerability Index (CVI) Assessment: Evaluating Risks Associated with Human-Made Activities along the Limassol Coastline, Cyprus

by Christos Theocharidis, Marina Doukanari, Eleftheria Kalogirou, Demetris Christofi, Christodoulos Mettas, Charalampos Kontoes, Diofantos Hadjimitsis, Athanasios V. Argyriou and Marinos Eliades

Remote Sens. 2024, 16(19), 3688; https://doi.org/10.3390/rs16193688 - 3 Oct 2024

Abstract

Coastal risk assessment is crucial for coastal management and decision making, especially in areas already experiencing the negative impacts of climate change. This study aims to investigate the coastal vulnerability due to climate change and human activities in an area west of the [...] Read more.

Coastal risk assessment is crucial for coastal management and decision making, especially in areas already experiencing the negative impacts of climate change. This study aims to investigate the coastal vulnerability due to climate change and human activities in an area west of the Limassol district’s coastline, in Cyprus, on which there have been limited studies. Furthermore, an analysis is conducted utilising the Coastal Vulnerability Index (CVI) by exploiting eight key parameters: land cover, coastal slope, shoreline erosion rates, tidal range, significant wave height, coastal elevation, sea-level rise, and coastal geomorphology. These parameters were assessed utilising remote sensing (RS) data and Geographical Information Systems (GISs) along a 36.1 km stretch of coastline. The results exhibited varying risk levels of coastal vulnerability, mainly highlighting a coastal area where the Kouris River estuary is highly vulnerable. The study underscores the need for targeted coastal management strategies to address the risks associated with coastal erosion. Additionally, the CVI developed in this study can be exploited as a tool for decision makers, empowering them to prioritise areas for intervention and bolster the resilience of coastal areas in the face of environmental changes. Full article

(This article belongs to the Section Environmental Remote Sensing)

20 pages, 5131 KiB

Open AccessArticle

Enhancing Accuracy in Historical Forest Vegetation Mapping in Yunnan with Phenological Features, and Climatic and Elevation Variables

by Jianbo Yang, Detuan Liu, Qian Li, Dhanushka N. Wanasinghe, Deli Zhai, Gaojuan Zhao and Jianchu Xu

Remote Sens. 2024, 16(19), 3687; https://doi.org/10.3390/rs16193687 - 3 Oct 2024

Abstract

Human activities have both positive and negative impacts on forests, altering the extent and composition of various forest vegetation types, and increasing uncertainty in ecological management. A detailed understanding of the historical distribution of forest vegetation is crucial for local conservation efforts. In [...] Read more.

Human activities have both positive and negative impacts on forests, altering the extent and composition of various forest vegetation types, and increasing uncertainty in ecological management. A detailed understanding of the historical distribution of forest vegetation is crucial for local conservation efforts. In this study, we integrated phenological features with climatic and terrain variables to enhance the mapping accuracy of forest vegetation in Yunnan. We mapped the historical distributions of five forest vegetation type groups and nine specific forest vegetation types for 2001, 2010, and 2020. Our findings revealed that: (1) rubber plantations can be effectively distinguished from other forest vegetation using phenological features, coniferous forests and broad-leaved forests can be differentiated using visible spectral bands, and environmental variables (temperature, precipitation, and elevation) are effective in differentiating forest vegetation types under varying climate conditions; (2) the overall accuracy and kappa coefficient increased by 14.845% and 20.432%, respectively, when climatic variables were combined with phenological features, and by 13.613% and 18.902%, respectively, when elevation was combined with phenological features, compared to using phenological features alone; (3) forest cover in Yunnan increased by 2.069 × 10⁴ km² (10.369%) between 2001 and 2020. This study highlights the critical role of environmental variables in improving the mapping accuracy of forest vegetation in mountainous regions. Full article

(This article belongs to the Special Issue Remote Sensing Based Forest Inventories from Landscape to Global Scale II)

► Show Figures

Figure 1

19 pages, 24337 KiB

Open AccessArticle

A 40-Year Time Series of Land Surface Emissivity Derived from AVHRR Sensors: A Fennoscandian Perspective

by Mira Barben, Stefan Wunderle and Sonia Dupuis

Remote Sens. 2024, 16(19), 3686; https://doi.org/10.3390/rs16193686 - 2 Oct 2024

Abstract

Accurate land surface temperature (LST) retrieval depends on precise knowledge of the land surface emissivity (LSE). Neglecting or inaccurately estimating the emissivity introduces substantial errors and uncertainty in LST measurements. The emissivity, which varies across different surfaces and land uses, reflects material composition [...] Read more.

Accurate land surface temperature (LST) retrieval depends on precise knowledge of the land surface emissivity (LSE). Neglecting or inaccurately estimating the emissivity introduces substantial errors and uncertainty in LST measurements. The emissivity, which varies across different surfaces and land uses, reflects material composition and surface roughness. Satellite data offer a robust means to determine LSE at large scales. This study utilises the Normalised Difference Vegetation Index Threshold Method (NDVI^THM) to produce a novel emissivity dataset spanning the last 40 years, specifically tailored for the Fennoscandian region, including Norway, Sweden, and Finland. Leveraging the long and continuous data series from the Advanced Very High Resolution Radiometer (AVHRR) sensors aboard the NOAA and MetOp satellites, an emissivity dataset is generated for 1981–2022. This dataset incorporates snow-cover information, enabling the creation of annual emissivity time series that account for winter conditions. LSE time series were extracted for six 15 × 15 km study sites and compared against the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD11A2 LSE product. The intercomparison reveals that, while both datasets generally align, significant seasonal differences exist. These disparities are attributable to differences in spectral response functions and temporal resolutions, as well as the method considering fixed values employed to calculate the emissivity. This study presents, for the first time, a 40-year time series of the emissivity for AVHRR channels 4 and 5 in Fennoscandia, highlighting the seasonal variability, land-cover influences, and wavelength-dependent emissivity differences. This dataset provides a valuable resource for future research on long-term land surface temperature and emissivity (LST&E) trends, as well as land-cover changes in the region, particularly with the use of Sentinel-3 data and upcoming missions such as EUMETSAT’s MetOp Second Generation, scheduled for launch in 2025. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing Technologies for Environmental Monitoring)

► Show Figures

Graphical abstract

23 pages, 3109 KiB

Open AccessArticle

Phase Error Correction in Sparse Linear MIMO Radar Based on the Equivalent Phase Center Principle

by Wenyuan Shao, Jianmin Hu, Yicai Ji, Jun Pan and Guangyou Fang

Remote Sens. 2024, 16(19), 3685; https://doi.org/10.3390/rs16193685 - 2 Oct 2024

Abstract

Multiple-input multiple-output (MIMO) technology is widely used in the field of radar imaging. Array sparse optimization reduces the hardware cost of MIMO radar, while virtual aperture and the equivalent phase center (EPC) principle simplify the radar signal model and reduce the computation and [...] Read more.

Multiple-input multiple-output (MIMO) technology is widely used in the field of radar imaging. Array sparse optimization reduces the hardware cost of MIMO radar, while virtual aperture and the equivalent phase center (EPC) principle simplify the radar signal model and reduce the computation and complexity of imaging algorithms. However, the application of sparse array structure and the EPC principle produces a non-negligible phase error, which affects the imaging quality. This paper simplifies the MIMO radar signal model based on the phase center approximation, analyzes the phase error generated by this method, and proposes an improved phase error correction method to solve the problem that the target cannot be well-focused at non-reference distance during imaging. In addition, this paper designs a sparse linear MIMO array with a periodic structure, which reduces the number of transmitting and receiving units, system complexity, and hardware costs. The proposed phase correction method was combined with the wavenumber domain algorithm to simulate and experiment on the designed antenna array, and good experimental results were obtained to verify the effectiveness of the proposed method. Full article

► Show Figures

Figure 1

28 pages, 7255 KiB

Open AccessArticle

Text-Guided Multi-Class Multi-Object Tracking for Fine-Grained Maritime Rescue

by Shuman Li, Zhipeng Lin, Haotian Wang, Wenjing Yang and Hengzhu Liu

Remote Sens. 2024, 16(19), 3684; https://doi.org/10.3390/rs16193684 - 2 Oct 2024

Abstract

The rapid development of remote sensing technology has provided new sources of data for marine rescue and has made it possible to find and track survivors. Due to the requirement of tracking multiple survivors at the same time, multi-object tracking (MOT) has become [...] Read more.

The rapid development of remote sensing technology has provided new sources of data for marine rescue and has made it possible to find and track survivors. Due to the requirement of tracking multiple survivors at the same time, multi-object tracking (MOT) has become the key subtask of marine rescue. However, there exists a significant gap between fine-grained objects in realistic marine rescue remote sensing data and the fine-grained object tracking capability of existing MOT technologies, which mainly focuses on coarse-grained object scenarios and fails to track fine-grained instances. Such a gap limits the practical application of MOT in realistic marine rescue remote sensing data, especially when rescue forces are limited. Given the promising fine-grained classification performance of recent text-guided methods, we delve into leveraging labels and attributes to narrow the gap between MOT and fine-grained maritime rescue. We propose a text-guided multi-class multi-object tracking (TG-MCMOT) method. To handle the problem raised by fine-grained classes, we design a multi-modal encoder by aligning external textual information with visual inputs. We use decoding information at different levels, simultaneously predicting the category, location, and identity embedding features of objects. Meanwhile, to improve the performance of small object detection, we also develop a data augmentation pipeline to generate pseudo-near-infrared images based on RGB images. Extensive experiments demonstrate that our TG-MCMOT not only performs well on typical metrics in the maritime rescue task (SeaDronesSee dataset), but it also effectively tracks open-set categories on the BURST dataset. Specifically, on the SeaDronesSee dataset, the Higher Order Tracking Accuracy (HOTA) reached a score of 58.8, and on the BURST test dataset, the HOTA score for the unknown class improved by 16.07 points. Full article

21 pages, 5109 KiB

Open AccessArticle

Response of NO 5.3 μm Emission to the Geomagnetic Storm on 24 April 2023

by Hongshan Liu, Hong Gao, Zheng Li, Jiyao Xu, Weihua Bai, Longchang Sun and Zhongmu Li

Remote Sens. 2024, 16(19), 3683; https://doi.org/10.3390/rs16193683 - 2 Oct 2024

Abstract

The response of NO emission at 5.3 μm in the thermosphere to the geomagnetic storm on 24 April 2023 is analyzed using TIMED/SABER observations and TIEGCM simulations. Both the observations and the simulations indicate a significant enhancement in NO emission during the storm. [...] Read more.

The response of NO emission at 5.3 μm in the thermosphere to the geomagnetic storm on 24 April 2023 is analyzed using TIMED/SABER observations and TIEGCM simulations. Both the observations and the simulations indicate a significant enhancement in NO emission during the storm. Observations show two peaks around 50°S/N in the altitude–latitude distribution of NO emission and its relative variation. Additionally, the peak emission and enhancement are stronger on the nightside compared with the dayside. The peak altitude in the Northern Hemisphere is approximately 2–10 km higher than in the Southern Hemisphere; meanwhile, the peak altitude on the dayside is approximately 2–8 km higher than that on the nightside. Simulations reveal three peaks around 50°S, the equator, and 65°N, with peak altitudes at higher latitudes being slightly lower than those observed. In general, the altitude–latitude distribution structure of the relative variation in simulated NO emission matches observations, with two peaks around 50°S/N. TIEGCM simulations suggest that the increase in NO density and temperature during a geomagnetic storm can lead to an increase in NO emission at most altitudes and latitudes. Furthermore, the significant enhancement around 50°S/N is mainly attributed to the changes in NO density. Full article

(This article belongs to the Special Issue Processes and Effects of Atmospheric and Ionospheric Dynamics Based on Remote Sensing Observations)

18 pages, 16454 KiB

Open AccessTechnical Note

Annotated Dataset for Training Cloud Segmentation Neural Networks Using High-Resolution Satellite Remote Sensing Imagery

by Mingyuan He, Jie Zhang, Yang He, Xinjie Zuo and Zebin Gao

Remote Sens. 2024, 16(19), 3682; https://doi.org/10.3390/rs16193682 - 2 Oct 2024

Abstract

The integration of satellite data with deep learning has revolutionized various tasks in remote sensing, including classification, object detection, and semantic segmentation. Cloud segmentation in high-resolution satellite imagery is a critical application within this domain, yet progress in developing advanced algorithms for this [...] Read more.

The integration of satellite data with deep learning has revolutionized various tasks in remote sensing, including classification, object detection, and semantic segmentation. Cloud segmentation in high-resolution satellite imagery is a critical application within this domain, yet progress in developing advanced algorithms for this task has been hindered by the scarcity of specialized datasets and annotation tools. This study addresses this challenge by introducing CloudLabel, a semi-automatic annotation technique leveraging region growing and morphological algorithms including flood fill, connected components, and guided filter. CloudLabel v1.0 streamlines the annotation process for high-resolution satellite images, thereby addressing the limitations of existing annotation platforms which are not specifically adapted to cloud segmentation, and enabling the efficient creation of high-quality cloud segmentation datasets. Notably, we have curated the Annotated Dataset for Training Cloud Segmentation (ADTCS) comprising 32,065 images (512 × 512) for cloud segmentation based on CloudLabel. The ADTCS dataset facilitates algorithmic advancement in cloud segmentation, characterized by uniform cloud coverage distribution and high image entropy (mainly 5–7). These features enable deep learning models to capture comprehensive cloud characteristics, enhancing recognition accuracy and reducing ground object misclassification. This contribution significantly advances remote sensing applications and cloud segmentation algorithms. Full article

(This article belongs to the Special Issue Advances in High-Resolution Satellite Remote Sensing Image Processing and Classification)

► Show Figures

Figure 1

23 pages, 15519 KiB

Open AccessArticle

Coastal Storm-Induced Sinkholes: Insights from Unmanned Aerial Vehicle Monitoring

by Alice Busetti, Christian Leone, Amerigo Corradetti, Saverio Fracaros, Sebastian Spadotto, Pietro Rai, Luca Zini and Chiara Calligaris

Remote Sens. 2024, 16(19), 3681; https://doi.org/10.3390/rs16193681 - 2 Oct 2024

Abstract

In recent decades, the scientific community has increasingly focused on extreme events linked to climate change, which are leading to more intense and frequent natural disasters. The Mediterranean can be considered a hotspot where the effects of these changes are expected to be [...] Read more.

In recent decades, the scientific community has increasingly focused on extreme events linked to climate change, which are leading to more intense and frequent natural disasters. The Mediterranean can be considered a hotspot where the effects of these changes are expected to be more intense compared to other regions of the planet. Italy is not exempt; in fact, with its extensive shoreline, it is particularly vulnerable, especially to high sea levels and coastal erosions. In this framework, from late October to early November 2023, six storm surges occurred in the Gulf of Trieste (NE Italy). These events, characterized by winds from 190°N to 220°N and the significant wave height, which reached up to 1.81 m nearshore—an uncommon meteorological condition in the northern Adriatic Sea—caused the occurrence of eight coastal sinkholes and substantial damages to man-made structures. Thanks to Unmanned Aerial Vehicles (UAVs) and their derived products (high-resolution orthomosaics, Digital Elevation Models—DEMs, and point clouds), it was possible to study these features over time, enabling long-term coastal dynamics monitoring, which can be crucial for timely and effective response and restoration efforts. Full article

(This article belongs to the Special Issue Application of Digital Aerial Photogrammetry in Geomorphological Studies)

► Show Figures

Figure 1

30 pages, 23417 KiB

Open AccessArticle

Enhanced Blue Band Vegetation Index (the Re-Modified Anthocyanin Reflectance Index (RMARI)) for Accurate Farmland Shelterbelt Extraction

by Xinle Zhang, Jiming Liu, Linghua Meng, Chuan Qin, Zeyu An, Yihao Wang and Huanjun Liu

Remote Sens. 2024, 16(19), 3680; https://doi.org/10.3390/rs16193680 - 2 Oct 2024

Abstract

Farmland shelterbelts are aimed at farmland protection and productivity improvement, environmental protection and ecological balance, as well as land use planning and management. Farmland shelterbelts play a vital role in determining the structural integrity and overall effectiveness of farmland, and assessing the dynamic [...] Read more.

Farmland shelterbelts are aimed at farmland protection and productivity improvement, environmental protection and ecological balance, as well as land use planning and management. Farmland shelterbelts play a vital role in determining the structural integrity and overall effectiveness of farmland, and assessing the dynamic changes within these protective forests accurately and swiftly is essential to maintaining their protective functions as well as for policy formulation and effectiveness evaluation in relevant departments. Traditional methods for extracting farmland shelterbelt information have faced significant challenges due to the large workload required and the inconsistencies in the accuracy of existing methods. For example, the existing vegetation index extraction methods often have significant errors, which remain unresolved. Therefore, developing a more efficient extraction method with greater accuracy is imperative. This study focused on Youyi Farm in Heilongjiang Province, China, utilizing satellite data with spatial resolutions ranging from 0.8 m (GF-7) to 30 m (Landsat). By taking into account the growth cycles of farmland shelterbelts and variations in crop types, the optimal temporal window for extraction is identified based on phenological analysis. The study introduced a new index—the Re-Modified Anthocyanin Reflectance Index (RMARI)—which is an improvement on existing vegetation indexes, such as the NDVI and the improved original ARI. Both the accuracy and extraction results showed significant improvements, and the feasibility of the RMARI was confirmed. The study proposed four extraction schemes for farmland shelterbelts: (1) spectral feature extraction, (2) extraction using vegetation indexes, (3) random forest extraction, and (4) RF combined with characteristic index bands. The extraction process was implemented on the GEE platform, and results from different spatial resolutions were compared. Results showed that (1) the bare soil period in May is the optimal time period for extracting farmland shelterbelts; (2) the RF method combined with characteristic index bands produces the best extraction results, effectively distinguishing shelterbelts from other land features; (3) the RMARI reduces background noise more effectively than the NDVI and ARI, resulting in more comprehensive extraction outcomes; and (4) among the satellite images analyzed—GF-7, Planet, Sentinel-2, and Landsat OLI 8—GF-7 achieves the highest extraction accuracy (with a Kappa coefficient of 0.95 and an OA of 0.97), providing the most detailed textural information. However, comprehensive analysis suggests that Sentinel-2 is more suitable for large-scale farmland shelterbelt information extraction. This study provides new approaches and technical support for periodic dynamic forestry surveys, providing valuable reference points for agricultural ecological research. Full article

(This article belongs to the Special Issue Mapping Essential Elements of Agricultural Land Using Remote Sensing)

26 pages, 2315 KiB

Open AccessArticle

Comparison and Optimization of Light Use Efficiency-Based Gross Primary Productivity Models in an Agroforestry Orchard

by Ningbo Cui, Ziling He, Mingjun Wang, Wenjiang Zhang, Lu Zhao, Daozhi Gong, Jun Li and Shouzheng Jiang

Remote Sens. 2024, 16(19), 3679; https://doi.org/10.3390/rs16193679 - 2 Oct 2024

Abstract

The light-use efficiency-based gross primary productivity (LUE-GPP) model is widely utilized for simulating terrestrial ecosystem carbon exchanges owing to its perceived simplicity and reliability. Variations in cloud cover and aerosol concentrations can affect ecosystem LUE, thereby influencing the performance of the LUE-GPP model, [...] Read more.

The light-use efficiency-based gross primary productivity (LUE-GPP) model is widely utilized for simulating terrestrial ecosystem carbon exchanges owing to its perceived simplicity and reliability. Variations in cloud cover and aerosol concentrations can affect ecosystem LUE, thereby influencing the performance of the LUE-GPP model, particularly in humid regions. In this study, the performance of six big-leaf LUE-GPP models and one two-leaf LUE-GPP model were evaluated in a humid agroforestry ecosystem from 2018–2020. All big-leaf LUE-GPP models yielded GPP values consistent with that derived from the eddy covariance system (GPP_EC), with R² ranging from 0.66–0.73 and RMSE ranging from 1.81–3.04 g C m⁻² d⁻¹. Differences in model performance were attributed to the differences in the quantification of temperature (T_s) and moisture constraints (W_s) and their combination forms in the models. The T_s and W_s algorithms in the eddy covariance-light-use efficiency (EF-LUE) model well characterized the environmental constraints on LUE. Simulation accuracy under the common limitation of T_s and W_s (T_s× W_s) was higher than the maximum limitation of T_s or W_s (Min (T_s, W_s)), and the combination of the T_s algorithm in the Carnegie–Ames–Stanford Approach (CASA) and the W_s algorithm in the EF-LUE model was optimized in combination forms, thereby constraining LUE for GPP estimates (GPP_BLO, R²= 0.76). Various big-leaf LUE-GPP models overestimated or underestimated GPP on sunny or cloudy days, respectively, while the two-leaf LUE-GPP model, which considered the transmission of diffuse radiation and the difference in photosynthetic capacity of canopy leaves, performed well (R²= 0.72, p < 0.01). Nevertheless, the underestimation/overestimation for shaded/sunlit leaves remained under different weather conditions. Then, the clearness index (K_t) was introduced to calculate the dynamic LUE in the big-leaf and two-leaf LUE-GPP models in the form of exponential or power functions, resulting in consistent performance even in different weather conditions and an overall higher simulation accuracy. This study confirmed the potential applicability of different LUE-GPP models and emphasized the importance of dynamic LUE on model performance. Full article

Journal Description

Remote Sensing

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI