Pub Date : 2025-11-14DOI: 10.1109/LGRS.2025.3632918
Tao Wang;Yong Fang;Shuangcheng Zhang;Bincai Cao;Qi Liu
The ice, cloud, and land elevation Satellite-2 (ICESat-2) has been operating continuously in orbit for nearly seven years. Its accuracy is crucial for ensuring the reliability of scientific applications. However, a few external studies have been conducted to assess the long-term consistency of ICESat-2 elevation measurements. In this letter, we evaluate the consistency of elevation accuracy through footprint-level crossover observations. This approach first extracts crossovers by averaging elevations within each ~12 m footprint, then analyzes their elevation differences using statistical and time-series approaches, and finally employs airborne LiDAR data for external validation. The results indicate that ICESat-2 elevation data exhibit excellent internal consistency over bare land areas from 2019 to 2024, with more than 40000 footprint-level crossovers, a mean elevation bias of 0.02 m, and a standard deviation of 0.22 m. The long-term drift of the elevation data is approximately 1.1 mm/yr, well within the mission’s scientific requirement of 4 mm/yr. Compared with airborne LiDAR, ICESat-2 maintains high external accuracy over long-term observations, with an overall root mean square error (RMSE) less than 0.38 m across 377 beam tracks. Overall, this study provides new and independent assessment of the consistency of ICESat-2 elevation data to date.
{"title":"Assessment of Long-Term Elevation Accuracy Consistency for ICESat-2/ATLAS Using Crossover Observations","authors":"Tao Wang;Yong Fang;Shuangcheng Zhang;Bincai Cao;Qi Liu","doi":"10.1109/LGRS.2025.3632918","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3632918","url":null,"abstract":"The ice, cloud, and land elevation Satellite-2 (ICESat-2) has been operating continuously in orbit for nearly seven years. Its accuracy is crucial for ensuring the reliability of scientific applications. However, a few external studies have been conducted to assess the long-term consistency of ICESat-2 elevation measurements. In this letter, we evaluate the consistency of elevation accuracy through footprint-level crossover observations. This approach first extracts crossovers by averaging elevations within each ~12 m footprint, then analyzes their elevation differences using statistical and time-series approaches, and finally employs airborne LiDAR data for external validation. The results indicate that ICESat-2 elevation data exhibit excellent internal consistency over bare land areas from 2019 to 2024, with more than 40000 footprint-level crossovers, a mean elevation bias of 0.02 m, and a standard deviation of 0.22 m. The long-term drift of the elevation data is approximately 1.1 mm/yr, well within the mission’s scientific requirement of 4 mm/yr. Compared with airborne LiDAR, ICESat-2 maintains high external accuracy over long-term observations, with an overall root mean square error (RMSE) less than 0.38 m across 377 beam tracks. Overall, this study provides new and independent assessment of the consistency of ICESat-2 elevation data to date.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-14DOI: 10.1109/LGRS.2025.3632889
Jingyao Zhang;Xuerong Cui;Juan Li;Song Dai;Bin Jiang;Lei Li
Subsea pipelines are prone to exposure due to natural factors such as earthquakes and vortices, which necessitates regular condition monitoring. Multibeam echo sounders (MBESs) can provide high-precision seabed topographic information, while side-scan sonar (SSS) excels at capturing high-resolution seabed texture features. The integration of these two data sources can complement each other, thereby improving the detection accuracy of subsea pipelines. To achieve effective fusion, high-precision spatial registration is required. However, existing registration algorithms still face challenges such as uneven feature point distribution, dependence on prior knowledge, and unstable matching. This letter proposes a multisource sonar image registration algorithm for subsea pipelines, named a hierarchical feature structure-driven method for multisource sonar image registration of subsea pipelines (HFSM). First, the method designs a grid-based multiscale corner detection (MS-CD), which effectively enhances the spatial distribution balance of feature points. Next, a multiwindow geometric–texture joint feature descriptor (MW-GTD) is proposed, which combines direction-sensitive curvature and spatial shadow distribution features within different scale windows. Finally, a multilayer coarse-to-fine guided matching (ML-CFGM) strategy is introduced to enhance the matching stability of images in feature-sparse regions and realize multilayer feature matching. The superiority of the proposed method is validated with real-world data, providing technical support for the efficient registration of MBES and SSS images and subsea pipeline detection.
{"title":"HFSM: A Hierarchical Feature Structure-Driven Method for Multisource Sonar Image Registration of Subsea Pipelines","authors":"Jingyao Zhang;Xuerong Cui;Juan Li;Song Dai;Bin Jiang;Lei Li","doi":"10.1109/LGRS.2025.3632889","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3632889","url":null,"abstract":"Subsea pipelines are prone to exposure due to natural factors such as earthquakes and vortices, which necessitates regular condition monitoring. Multibeam echo sounders (MBESs) can provide high-precision seabed topographic information, while side-scan sonar (SSS) excels at capturing high-resolution seabed texture features. The integration of these two data sources can complement each other, thereby improving the detection accuracy of subsea pipelines. To achieve effective fusion, high-precision spatial registration is required. However, existing registration algorithms still face challenges such as uneven feature point distribution, dependence on prior knowledge, and unstable matching. This letter proposes a multisource sonar image registration algorithm for subsea pipelines, named a hierarchical feature structure-driven method for multisource sonar image registration of subsea pipelines (HFSM). First, the method designs a grid-based multiscale corner detection (MS-CD), which effectively enhances the spatial distribution balance of feature points. Next, a multiwindow geometric–texture joint feature descriptor (MW-GTD) is proposed, which combines direction-sensitive curvature and spatial shadow distribution features within different scale windows. Finally, a multilayer coarse-to-fine guided matching (ML-CFGM) strategy is introduced to enhance the matching stability of images in feature-sparse regions and realize multilayer feature matching. The superiority of the proposed method is validated with real-world data, providing technical support for the efficient registration of MBES and SSS images and subsea pipeline detection.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Current convolutional neural network (CNN)-based tiny object detectors in remote sensing commonly face a resolution transform bottleneck, characterized by irreversible feature information loss during downsampling and reconstruction distortions during upsampling. To address this issue, we propose a lightweight one-stage detector, small-object-aware intelligent lightweight detector (SAILDet). Its core principle is to preserve information fidelity at the source rather than compensating for its loss in downstream stages. This is achieved through a paired design that employs Haar wavelet downsampling (HWD) to retain high-frequency details at the source and Content-Aware ReAssembly of FEatures (CARAFE) to perform artifact-free, fine-grained upsampling, thereby establishing a high-fidelity feature processing loop. Experiments on the DOTA dataset demonstrate that, compared to the baseline model, SAILDet reduces GFLOPs and parameters by 11.7% and 13.0%, respectively, while improving mAP@50–95 from 0.263 to 0.266 and mAP@50 from 0.411 to 0.422. In addition, consistent gains are also observed on AI-TOD, reinforcing that directly optimizing the resolution-transform operators is more effective than downstream compensation.
{"title":"SAILDet: Wavelet-Preserved Lightweight One-Stage Detector for Tiny Objects in Remote Sensing","authors":"Jiaqi Ma;Hui Wang;Tianyou Wang;Haotian Li;Ruixue Xiao","doi":"10.1109/LGRS.2025.3631843","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3631843","url":null,"abstract":"Current convolutional neural network (CNN)-based tiny object detectors in remote sensing commonly face a resolution transform bottleneck, characterized by irreversible feature information loss during downsampling and reconstruction distortions during upsampling. To address this issue, we propose a lightweight one-stage detector, small-object-aware intelligent lightweight detector (SAILDet). Its core principle is to preserve information fidelity at the source rather than compensating for its loss in downstream stages. This is achieved through a paired design that employs Haar wavelet downsampling (HWD) to retain high-frequency details at the source and Content-Aware ReAssembly of FEatures (CARAFE) to perform artifact-free, fine-grained upsampling, thereby establishing a high-fidelity feature processing loop. Experiments on the DOTA dataset demonstrate that, compared to the baseline model, SAILDet reduces GFLOPs and parameters by 11.7% and 13.0%, respectively, while improving mAP@50–95 from 0.263 to 0.266 and mAP@50 from 0.411 to 0.422. In addition, consistent gains are also observed on AI-TOD, reinforcing that directly optimizing the resolution-transform operators is more effective than downstream compensation.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-12DOI: 10.1109/LGRS.2025.3632153
Zhaoyu Liu;Wei Chen;Lixia Yang
To address core challenges in synthetic aperture radar (SAR) image target detection, including complex background interference, weak small-target features, and multiscale target coexistence, this study proposes the synthetic aperture-optimized real-time detection transformer (SA-RTDETR) model. The framework incorporates three core modules to enhance detection efficacy. First, the bidirectional receptive field boosting module synergistically integrates local details with global contextual information and substantially improves discriminative feature extraction while preserving spatial resolution. Second, the deformable attention-based intrascale feature interaction module employs adaptive sampling of critical scattering regions to address localization difficulties of small targets in SAR imagery. Third, the attention upsampling module mitigates detail loss and aliasing artifacts inherent in traditional interpolation methods through feature compensation strategies. Experimental results on the SARDet-100K dataset demonstrate that SA-RTDETR achieves 90.1% mAP@50, 56.0% mAP@50-95, and 84.7% recall rate representing improvements of 2.7%, 2.6%, and 2.2% over the baseline model, respectively. The end-to-end architecture enables high-precision SAR image analysis and offers considerable potential for military reconnaissance and maritime surveillance applications. The SA-RTDETR model establishes a novel technical paradigm for reliable all-weather remote sensing target detection by harmonizing feature robustness, scale adaptability, and operational efficiency.
{"title":"SA-RTDETR: A High-Precision Real-Time Detection Transformer Based on Complex Scenarios for SAR Object Detection","authors":"Zhaoyu Liu;Wei Chen;Lixia Yang","doi":"10.1109/LGRS.2025.3632153","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3632153","url":null,"abstract":"To address core challenges in synthetic aperture radar (SAR) image target detection, including complex background interference, weak small-target features, and multiscale target coexistence, this study proposes the synthetic aperture-optimized real-time detection transformer (SA-RTDETR) model. The framework incorporates three core modules to enhance detection efficacy. First, the bidirectional receptive field boosting module synergistically integrates local details with global contextual information and substantially improves discriminative feature extraction while preserving spatial resolution. Second, the deformable attention-based intrascale feature interaction module employs adaptive sampling of critical scattering regions to address localization difficulties of small targets in SAR imagery. Third, the attention upsampling module mitigates detail loss and aliasing artifacts inherent in traditional interpolation methods through feature compensation strategies. Experimental results on the SARDet-100K dataset demonstrate that SA-RTDETR achieves 90.1% mAP@50, 56.0% mAP@50-95, and 84.7% recall rate representing improvements of 2.7%, 2.6%, and 2.2% over the baseline model, respectively. The end-to-end architecture enables high-precision SAR image analysis and offers considerable potential for military reconnaissance and maritime surveillance applications. The SA-RTDETR model establishes a novel technical paradigm for reliable all-weather remote sensing target detection by harmonizing feature robustness, scale adaptability, and operational efficiency.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-12DOI: 10.1109/LGRS.2025.3631806
Haoxuan Xu;Meiguo Gao
Marine radar is widely employed in ocean monitoring systems. However, sea clutter significantly impairs radar data interpretability and degrades maritime target detection performance. Effective clutter suppression methods are thus essential to enhance target characteristics for improved detection. However, environmental sea clutter often exhibits complex statistical characteristics, causing traditional model-based methods to suffer from performance degradation. To address this challenge, this letter proposes a sea clutter suppression method based on a complex-valued neural network (CVNN). First, the network incorporates a wavelet convolution (WTConv) block to expand the receptive field. Second, complex-valued convolutional blocks integrated with an attention mechanism are designed to enhance latent feature extraction. Finally, the model’s performance is rigorously validated using real-measured data. Experimental results demonstrate that the proposed model achieves superior clutter suppression performance.
{"title":"An End-to-End Sea Clutter Suppression Method Using Wavelet Convolution-Enhanced Attentional Complex-Valued Neural Network","authors":"Haoxuan Xu;Meiguo Gao","doi":"10.1109/LGRS.2025.3631806","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3631806","url":null,"abstract":"Marine radar is widely employed in ocean monitoring systems. However, sea clutter significantly impairs radar data interpretability and degrades maritime target detection performance. Effective clutter suppression methods are thus essential to enhance target characteristics for improved detection. However, environmental sea clutter often exhibits complex statistical characteristics, causing traditional model-based methods to suffer from performance degradation. To address this challenge, this letter proposes a sea clutter suppression method based on a complex-valued neural network (CVNN). First, the network incorporates a wavelet convolution (WTConv) block to expand the receptive field. Second, complex-valued convolutional blocks integrated with an attention mechanism are designed to enhance latent feature extraction. Finally, the model’s performance is rigorously validated using real-measured data. Experimental results demonstrate that the proposed model achieves superior clutter suppression performance.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-12DOI: 10.1109/LGRS.2025.3631871
Haotian Li;Jiaqi Ma;Wenna Guo;Xiaoxia Li;Xiaohui Qin;Zhenhua Ma
With the rapid development of applications such as unmanned aerial vehicle (UAV)-based remote sensing, smart cities, and intelligent transportation, small-object detection has become increasingly important in the field of object recognition. However, existing methods often struggle to balance detection accuracy and inference efficiency under large-scale variations, dense small-object distributions, and complex background interference. To address these challenges, this letter proposes a lightweight perception subnetwork, RSNet-Lite. The network integrates a multiscale attention mechanism to enhance small-object perception, dynamic convolution, and long-range spatial modeling units to improve feature representation, and lightweight convolution with efficient sampling strategies to significantly reduce computational complexity. As a result, RSNet-Lite achieves real-time inference while maintaining high detection accuracy, striking a balance between speed and performance. Finally, the proposed method is validated on the Aerial Image–Tiny Object Detection (AI-TOD) and Vision Meets Drone (VisDrone) datasets, demonstrating its effectiveness and strong potential for small-object detection tasks.
{"title":"RSNet-Lite: A Lightweight Perception Subnetwork for Remote Sensing Object Detection","authors":"Haotian Li;Jiaqi Ma;Wenna Guo;Xiaoxia Li;Xiaohui Qin;Zhenhua Ma","doi":"10.1109/LGRS.2025.3631871","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3631871","url":null,"abstract":"With the rapid development of applications such as unmanned aerial vehicle (UAV)-based remote sensing, smart cities, and intelligent transportation, small-object detection has become increasingly important in the field of object recognition. However, existing methods often struggle to balance detection accuracy and inference efficiency under large-scale variations, dense small-object distributions, and complex background interference. To address these challenges, this letter proposes a lightweight perception subnetwork, RSNet-Lite. The network integrates a multiscale attention mechanism to enhance small-object perception, dynamic convolution, and long-range spatial modeling units to improve feature representation, and lightweight convolution with efficient sampling strategies to significantly reduce computational complexity. As a result, RSNet-Lite achieves real-time inference while maintaining high detection accuracy, striking a balance between speed and performance. Finally, the proposed method is validated on the Aerial Image–Tiny Object Detection (AI-TOD) and Vision Meets Drone (VisDrone) datasets, demonstrating its effectiveness and strong potential for small-object detection tasks.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-12DOI: 10.1109/LGRS.2025.3631867
Shangshang Zhang;Yulong Fan;Lin Sun
Accurate retrieval of the spatiotemporal distribution of atmospheric aerosols is essential for studying aerosolradiationcloud interactions, air-quality forecasting, and climate-change assessment. Although data-driven methods have significantly advanced aerosol retrieval, the existing models often neglect the influence of aerosol type on retrieval accuracy. To address this gap, this study presents an improved data-driven aerosol retrieval framework that explicitly incorporates aerosol type information into model training. Aerosol classification is performed using the $K$ -means unsupervised clustering algorithm to optimize training samples, thereby enhancing model adaptability and retrieval accuracy. The refined samples are then used to train an extremely randomized trees (ERTs) model, achieving an optimal balance between accuracy and computational efficiency. Validation results demonstrate strong performance, with a correlation coefficient of 0.93, a root mean square error (RMSE) of 0.072, and over 89% of results falling within the expected error range [(EE: ± (0.05+20% $times $ in situ observations)], better than that of the traditional model. The findings demonstrate that integrating aerosol-type information into data-driven retrievals substantially improves accuracy and applicability for aerosol remote sensing. Future research should focus on refining aerosol classification techniques and integrating multisource remote sensing data to enhance model robustness and global applicability further.
{"title":"K-Means Clustering for Improved Data-Driven Satellite Aerosol Retrieval","authors":"Shangshang Zhang;Yulong Fan;Lin Sun","doi":"10.1109/LGRS.2025.3631867","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3631867","url":null,"abstract":"Accurate retrieval of the spatiotemporal distribution of atmospheric aerosols is essential for studying aerosolradiationcloud interactions, air-quality forecasting, and climate-change assessment. Although data-driven methods have significantly advanced aerosol retrieval, the existing models often neglect the influence of aerosol type on retrieval accuracy. To address this gap, this study presents an improved data-driven aerosol retrieval framework that explicitly incorporates aerosol type information into model training. Aerosol classification is performed using the <inline-formula> <tex-math>$K$ </tex-math></inline-formula>-means unsupervised clustering algorithm to optimize training samples, thereby enhancing model adaptability and retrieval accuracy. The refined samples are then used to train an extremely randomized trees (ERTs) model, achieving an optimal balance between accuracy and computational efficiency. Validation results demonstrate strong performance, with a correlation coefficient of 0.93, a root mean square error (RMSE) of 0.072, and over 89% of results falling within the expected error range [(EE: ± (0.05+20% <inline-formula> <tex-math>$times $ </tex-math></inline-formula> in situ observations)], better than that of the traditional model. The findings demonstrate that integrating aerosol-type information into data-driven retrievals substantially improves accuracy and applicability for aerosol remote sensing. Future research should focus on refining aerosol classification techniques and integrating multisource remote sensing data to enhance model robustness and global applicability further.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Data gaps exist in the measured spectral reflectance and atmospheric data from the radiometric calibration network (RadCalNet) due to instrument malfunctions or weather-related interferences, which severely impedes the application of the data. Therefore, developing a method to fill these missing RadCalNet data is a pressing issue. This study focuses on four RadCalNet sites with distinct surface types and proposes a high-precision bottom-of-atmosphere (BOA) spectral reflectance model. With on-site atmospheric data from RadCalNet, the predicted results achieve a root mean square error (RMSE) of no more than 1.26%. In scenarios where in situ atmospheric conditions are completely missing, the ERA5 dataset is used as a substitute and validated with Landsat 8 surface reflectance products; the absolute errors for all sites did not exceed 4.58%, validating the proposed method’s effectiveness. Additionally, the importance of input parameters and the impact of their uncertainties on prediction accuracy are discussed.
{"title":"A Method for Reconstructing Surface Spectral Reflectance With Missing RadCalNet Data","authors":"Shutian Zhu;Qiyue Liu;Chuanzhao Tian;Hanlie Xu;Jie Han;Wenhao Zhang;Na Xu","doi":"10.1109/LGRS.2025.3631876","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3631876","url":null,"abstract":"Data gaps exist in the measured spectral reflectance and atmospheric data from the radiometric calibration network (RadCalNet) due to instrument malfunctions or weather-related interferences, which severely impedes the application of the data. Therefore, developing a method to fill these missing RadCalNet data is a pressing issue. This study focuses on four RadCalNet sites with distinct surface types and proposes a high-precision bottom-of-atmosphere (BOA) spectral reflectance model. With on-site atmospheric data from RadCalNet, the predicted results achieve a root mean square error (RMSE) of no more than 1.26%. In scenarios where in situ atmospheric conditions are completely missing, the ERA5 dataset is used as a substitute and validated with Landsat 8 surface reflectance products; the absolute errors for all sites did not exceed 4.58%, validating the proposed method’s effectiveness. Additionally, the importance of input parameters and the impact of their uncertainties on prediction accuracy are discussed.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep learning has emerged as the predominant approach for ship detection in synthetic aperture radar (SAR) imagery. Nevertheless, persistent challenges such as densely clustered vessels, intricate background complexity, and multiscale target variations often lead to incomplete feature extraction, resulting in false alarms and missed detections. To address these limitations, this study presents LD-YOLO, an enhanced model based on YOLOv8n, which incorporates three critical innovations. Dynamic convolution layers are strategically embedded within key backbone stages to adaptively adjust kernel parameters, enhancing multiscale feature discriminability while maintaining computational efficiency. The proposed C2f-LSK module combines decomposed large-kernel convolution with attention mechanisms, enabling dynamic optimization of receptive field contributions across different detection stages and effective modeling of global contextual information. Considering the characteristics of small vessels in SAR imagery and the impact of downsampling rates on image quality, a dedicated $160times 160$ detection head is further integrated to preserve fine-grained details of small targets, complemented by bidirectional feature fusion to strengthen semantic context propagation. Extensive experiments validate the model’s superiority, achieving 98.2% of AP50 and 73.1% of AP50-95 on the SSDD benchmark, with consistent performance improvements demonstrated on HRSID (94.6% AP50) datasets. These advancements position LD-YOLO as a robust solution for maritime surveillance applications requiring high-precision SAR image analysis under complex operational conditions.
{"title":"LD-YOLO: A Lightweight Dynamic Convolution-Based YOLOv8n Framework for Robust Ship Detection in SAR Imagery","authors":"Jiqiang Niu;Mengyang Li;Hao Lin;Yichen Liu;Zijian Liu;Hongrui Li;Shaomian Niu","doi":"10.1109/LGRS.2025.3630098","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3630098","url":null,"abstract":"Deep learning has emerged as the predominant approach for ship detection in synthetic aperture radar (SAR) imagery. Nevertheless, persistent challenges such as densely clustered vessels, intricate background complexity, and multiscale target variations often lead to incomplete feature extraction, resulting in false alarms and missed detections. To address these limitations, this study presents LD-YOLO, an enhanced model based on YOLOv8n, which incorporates three critical innovations. Dynamic convolution layers are strategically embedded within key backbone stages to adaptively adjust kernel parameters, enhancing multiscale feature discriminability while maintaining computational efficiency. The proposed C2f-LSK module combines decomposed large-kernel convolution with attention mechanisms, enabling dynamic optimization of receptive field contributions across different detection stages and effective modeling of global contextual information. Considering the characteristics of small vessels in SAR imagery and the impact of downsampling rates on image quality, a dedicated <inline-formula> <tex-math>$160times 160$ </tex-math></inline-formula> detection head is further integrated to preserve fine-grained details of small targets, complemented by bidirectional feature fusion to strengthen semantic context propagation. Extensive experiments validate the model’s superiority, achieving 98.2% of AP50 and 73.1% of AP50-95 on the SSDD benchmark, with consistent performance improvements demonstrated on HRSID (94.6% AP50) datasets. These advancements position LD-YOLO as a robust solution for maritime surveillance applications requiring high-precision SAR image analysis under complex operational conditions.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Coherent S-band radar has recently emerged as a promising technique for ocean surface wave and current detection. It can measure ocean surface current by estimating Doppler frequency shifts from sea surface signals. However, the conventional time averaging (TA) method neglects spatial dimension information and is unavailable under low wind speed conditions. Two algorithms for ocean current inversion are proposed in this letter: the spatial–temporal averaging (STA) method and the wavenumber--frequency (WF) method. In the STA method, the TA method is extended to the spatial–temporal domain. This approach fully exploits the spatial continuity of radar signals. In the WF method, a 2-D Fast Fourier Transform (2-D FFT) is applied to transform the spatial–temporal radial velocities into the WF domain. After employing dual filtering to eliminate nonlinear components, the radial current velocity is estimated through a modified dispersion relation fitting. The two methods are based on different physical mechanisms: the STA method measurements include wind drift components, while the WF method remains unaffected by wind drift. Therefore, wind drift can be effectively estimated by calculating the difference between the two methods’ measurements. Validation using observational data collected at Beishuang Island during Typhoon Catfish shows that the estimated wind drifts achieve a correlation coefficient (COR) of 0.90 with the “empirical model predictions.” This confirms the effectiveness of the proposed algorithms.
{"title":"Spatial–Temporal and Wavenumber--Frequency Inversion Algorithms for Ocean Surface Current Using Coherent S-Band Radar","authors":"Xinyu Fu;Chen Zhao;Zezong Chen;Sitao Wu;Fan Ding;Rui Liu;Guoxing Zheng","doi":"10.1109/LGRS.2025.3629684","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3629684","url":null,"abstract":"Coherent S-band radar has recently emerged as a promising technique for ocean surface wave and current detection. It can measure ocean surface current by estimating Doppler frequency shifts from sea surface signals. However, the conventional time averaging (TA) method neglects spatial dimension information and is unavailable under low wind speed conditions. Two algorithms for ocean current inversion are proposed in this letter: the spatial–temporal averaging (STA) method and the wavenumber--frequency (WF) method. In the STA method, the TA method is extended to the spatial–temporal domain. This approach fully exploits the spatial continuity of radar signals. In the WF method, a 2-D Fast Fourier Transform (2-D FFT) is applied to transform the spatial–temporal radial velocities into the WF domain. After employing dual filtering to eliminate nonlinear components, the radial current velocity is estimated through a modified dispersion relation fitting. The two methods are based on different physical mechanisms: the STA method measurements include wind drift components, while the WF method remains unaffected by wind drift. Therefore, wind drift can be effectively estimated by calculating the difference between the two methods’ measurements. Validation using observational data collected at Beishuang Island during Typhoon Catfish shows that the estimated wind drifts achieve a correlation coefficient (COR) of 0.90 with the “empirical model predictions.” This confirms the effectiveness of the proposed algorithms.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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