Pub Date : 2025-08-25DOI: 10.1109/LGRS.2025.3602095
Yinan Ye;Nicholas C. Coops;Txomin Hermosilla;Michael A. Wulder;Sarah E. Gergel
The object-based image segmentation techniques are widely utilized in environmental disciplines to partition remotely sensed imagery into objects representing distinct conditions, such as vegetation structure or landform. However, most approaches are applied to a single temporal snapshot, limiting their ability to update polygons over time. To address this, we proposed a temporally consistent segmentation algorithm based on a two-phase region growing approach designed to be applied to time series of annual Landsat surface reflectance composites. We developed and demonstrated this new approach over six fire-disturbed forested study areas in British Columbia, Canada, to dynamically delineate polygons over time as they underwent land cover change. Our approach maintained the existing boundaries for forest polygons with no land cover change while updating those subject to change as forest regenerated and followed successional processes. Rapidly recovering areas, such as Cariboo and Fraser-Fort George, showed increases in mean segment area from 12 to 21 and 14 to 25 ha, respectively, approaching or exceeding predisturbance values. Additionally, segment shape complexity increased over time, reflecting the structural development of recovering stands. This work demonstrated the potential of utilizing Landsat surface reflectance data to update forest polygons over time with reference to forest development and increasing maturity.
{"title":"Temporally Consistent Forest Stand Segmentation Using Landsat Imagery","authors":"Yinan Ye;Nicholas C. Coops;Txomin Hermosilla;Michael A. Wulder;Sarah E. Gergel","doi":"10.1109/LGRS.2025.3602095","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3602095","url":null,"abstract":"The object-based image segmentation techniques are widely utilized in environmental disciplines to partition remotely sensed imagery into objects representing distinct conditions, such as vegetation structure or landform. However, most approaches are applied to a single temporal snapshot, limiting their ability to update polygons over time. To address this, we proposed a temporally consistent segmentation algorithm based on a two-phase region growing approach designed to be applied to time series of annual Landsat surface reflectance composites. We developed and demonstrated this new approach over six fire-disturbed forested study areas in British Columbia, Canada, to dynamically delineate polygons over time as they underwent land cover change. Our approach maintained the existing boundaries for forest polygons with no land cover change while updating those subject to change as forest regenerated and followed successional processes. Rapidly recovering areas, such as Cariboo and Fraser-Fort George, showed increases in mean segment area from 12 to 21 and 14 to 25 ha, respectively, approaching or exceeding predisturbance values. Additionally, segment shape complexity increased over time, reflecting the structural development of recovering stands. This work demonstrated the potential of utilizing Landsat surface reflectance data to update forest polygons over time with reference to forest development and increasing maturity.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11137370","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-25DOI: 10.1109/LGRS.2025.3602123
Bingquan Han;Chen Yu;Zhenhong Li;Chuang Song;Xiaoning Hu;Jie Li
Accurately measuring surface deformation velocity using interferometric synthetic aperture radar (InSAR) is crucial for understanding geophysical processes. However, traditional methods often face challenges in capturing subtle deformations over long distances, as errors introduced during unwrapping can accumulate overextended spatial extents. This study introduces a multiarc adjustment (MAA) method aimed at mitigating these errors, especially in high-precision monitoring scenarios, where velocities are sensitive to the location of the reference point. Simulation results demonstrate that the MAA method significantly outperforms the traditional method, achieving substantial reductions in rms under noisy conditions and complex phase unwrapping scenarios. Furthermore, integrating the MAA method into fault slip inversion improves the accuracy of slip distribution estimations. Applications to real datasets from the southern Tibet region and the San Andreas Fault further validate the MAA method’s effectiveness. These findings underscore the MAA method’s potential to enhance deformation velocity measurements in challenging environments, establishing it as a valuable tool for geodetic and tectonic studies.
{"title":"A Multiarc Adjustment Method for Interferometric Synthetic Aperture Radar Time-Series Analysis","authors":"Bingquan Han;Chen Yu;Zhenhong Li;Chuang Song;Xiaoning Hu;Jie Li","doi":"10.1109/LGRS.2025.3602123","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3602123","url":null,"abstract":"Accurately measuring surface deformation velocity using interferometric synthetic aperture radar (InSAR) is crucial for understanding geophysical processes. However, traditional methods often face challenges in capturing subtle deformations over long distances, as errors introduced during unwrapping can accumulate overextended spatial extents. This study introduces a multiarc adjustment (MAA) method aimed at mitigating these errors, especially in high-precision monitoring scenarios, where velocities are sensitive to the location of the reference point. Simulation results demonstrate that the MAA method significantly outperforms the traditional method, achieving substantial reductions in rms under noisy conditions and complex phase unwrapping scenarios. Furthermore, integrating the MAA method into fault slip inversion improves the accuracy of slip distribution estimations. Applications to real datasets from the southern Tibet region and the San Andreas Fault further validate the MAA method’s effectiveness. These findings underscore the MAA method’s potential to enhance deformation velocity measurements in challenging environments, establishing it as a valuable tool for geodetic and tectonic studies.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036196","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}
In seismic exploration, the multiple suppression is crucial for accurate subsurface imaging and resource identification. Internal multiples, generated by multiple reflections at impedance interfaces, act as interference signals that can mislead resource exploration. Compared to traditional methods, the conventional Marchenko multiple elimination (C-MME) method allows for the direct extraction of primary waves from seismic records without requiring a macro velocity model or predictive subtraction, thereby preserving effective signals. However, challenges, such as low signal-to-noise ratios (SNRs) and high-density sampling requirements, have hindered its application to field land seismic data. To address these challenges of C-MME in field seismic data processing, we propose a compressive sensing-based Marchenko multiple elimination (CS-MME) method, which incorporates efficient denoising, reconstruction, and deconvolution capabilities. In this study, the CS-MME method has demonstrated exceptional performance in processing field land seismic data, successfully overcoming the aforementioned challenges. marking the first successful implementation of Marchenko multiple elimination (MME) on field land data.
{"title":"Compressive Sensing-Marchenko Multiple Elimination in Complex Field Land Seismic Data","authors":"Haoxin Zhu;Zhangqing Sun;Jianwei Nie;Bin Hu;Fei Jiang;Fuxing Han;Yang Zhang;Mingchen Liu;Zhenghui Gao","doi":"10.1109/LGRS.2025.3601629","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3601629","url":null,"abstract":"In seismic exploration, the multiple suppression is crucial for accurate subsurface imaging and resource identification. Internal multiples, generated by multiple reflections at impedance interfaces, act as interference signals that can mislead resource exploration. Compared to traditional methods, the conventional Marchenko multiple elimination (C-MME) method allows for the direct extraction of primary waves from seismic records without requiring a macro velocity model or predictive subtraction, thereby preserving effective signals. However, challenges, such as low signal-to-noise ratios (SNRs) and high-density sampling requirements, have hindered its application to field land seismic data. To address these challenges of C-MME in field seismic data processing, we propose a compressive sensing-based Marchenko multiple elimination (CS-MME) method, which incorporates efficient denoising, reconstruction, and deconvolution capabilities. In this study, the CS-MME method has demonstrated exceptional performance in processing field land seismic data, successfully overcoming the aforementioned challenges. marking the first successful implementation of Marchenko multiple elimination (MME) on field land data.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914217","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 : 2025-08-22DOI: 10.1109/LGRS.2025.3601670
Jinliang An;Longlong Dai;Muzi Wang;Weidong Zhang
Recently, transformer-based approaches have emerged as powerful tools for hyperspectral image (HSI) classification. HSI inherently exhibits low-rank and sparse properties due to spatial continuity and spectral redundancy. However, most existing methods directly adopt standard transformer architectures, overlooking the distinctive priors inherent in HSI, which limits the classification performance and modeling efficiency. To address these challenges, this letter proposes a multiscale low-rank and sparse transformer (MLSFormer) that effectively integrates both low-rank and sparse priors. Specifically, we leverage tensor low-rank decomposition (TLRD) to factorize the query, key, and value matrices into low-rank tensor products, capturing dominant low-rank structures. In parallel, we introduce a sparse attention mechanism to retain only the most important connections. Furthermore, a multiscale attention mechanism is designed to hierarchically partition attention heads into global, medium, and local groups, each assigned tailored decomposition ranks and sparsity ratios, enabling comprehensive multiscale feature extraction. Extensive experiments on three benchmark datasets demonstrate that MLSFormer achieves superior classification performance compared to state-of-the-art methods.
{"title":"Multiscale Low-Rank and Sparse Attention-Based Transformer for Hyperspectral Image Classification","authors":"Jinliang An;Longlong Dai;Muzi Wang;Weidong Zhang","doi":"10.1109/LGRS.2025.3601670","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3601670","url":null,"abstract":"Recently, transformer-based approaches have emerged as powerful tools for hyperspectral image (HSI) classification. HSI inherently exhibits low-rank and sparse properties due to spatial continuity and spectral redundancy. However, most existing methods directly adopt standard transformer architectures, overlooking the distinctive priors inherent in HSI, which limits the classification performance and modeling efficiency. To address these challenges, this letter proposes a multiscale low-rank and sparse transformer (MLSFormer) that effectively integrates both low-rank and sparse priors. Specifically, we leverage tensor low-rank decomposition (TLRD) to factorize the query, key, and value matrices into low-rank tensor products, capturing dominant low-rank structures. In parallel, we introduce a sparse attention mechanism to retain only the most important connections. Furthermore, a multiscale attention mechanism is designed to hierarchically partition attention heads into global, medium, and local groups, each assigned tailored decomposition ranks and sparsity ratios, enabling comprehensive multiscale feature extraction. Extensive experiments on three benchmark datasets demonstrate that MLSFormer achieves superior classification performance compared to state-of-the-art methods.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914211","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 : 2025-08-22DOI: 10.1109/LGRS.2025.3601600
Yuquan Gan;Xingyu Li;Siyu Wu;Mengjiao Wang
Hyperspectral anomaly detection (HAD) aims to identify anomalous targets that differ from the background in high-dimensional spectral images, and is widely applied in fields such as military reconnaissance and environmental monitoring. However, the diversity of anomaly scales, interference from complex backgrounds, and redundancy of spectral information pose significant challenges to achieving high detection accuracy. To address these issues, this letter proposes a multiscale attention-guided context network (MACNet) to enhance the perception of anomalous regions. MACNet consists of three components: a multiscale local feature extractor (MSLFE) that effectively captures edge structures and subtle anomalies at different scales, a global context awareness module (GCAM) that fuses local and global contextual information to improve discrimination under complex backgrounds, and a refined reconstruction and contrast enhancement module (RRCE) that employs channel attention and spatial reconstruction mechanisms to enhance the response differences between anomalies and background. Experiments on four publicly available hyperspectral datasets demonstrate that MACNet achieves superior detection accuracy compared to existing mainstream methods, validating the effectiveness of the proposed approach.
{"title":"MACNet: A Multiscale Attention-Guided Contextual Network for Hyperspectral Anomaly Detection","authors":"Yuquan Gan;Xingyu Li;Siyu Wu;Mengjiao Wang","doi":"10.1109/LGRS.2025.3601600","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3601600","url":null,"abstract":"Hyperspectral anomaly detection (HAD) aims to identify anomalous targets that differ from the background in high-dimensional spectral images, and is widely applied in fields such as military reconnaissance and environmental monitoring. However, the diversity of anomaly scales, interference from complex backgrounds, and redundancy of spectral information pose significant challenges to achieving high detection accuracy. To address these issues, this letter proposes a multiscale attention-guided context network (MACNet) to enhance the perception of anomalous regions. MACNet consists of three components: a multiscale local feature extractor (MSLFE) that effectively captures edge structures and subtle anomalies at different scales, a global context awareness module (GCAM) that fuses local and global contextual information to improve discrimination under complex backgrounds, and a refined reconstruction and contrast enhancement module (RRCE) that employs channel attention and spatial reconstruction mechanisms to enhance the response differences between anomalies and background. Experiments on four publicly available hyperspectral datasets demonstrate that MACNet achieves superior detection accuracy compared to existing mainstream methods, validating the effectiveness of the proposed approach.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934530","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}
Most lightweight super-resolution networks are designed to improve performance by introducing an attention mechanism and to reduce model parameters by designing lightweight convolutional layers. However, the introduction of the attention mechanism often leads to an increase in the number of parameters. In addition, the lightweight convolutional layer has a limited receptive field and cannot effectively capture long-range dependencies. In this letter, we design a novel lightweight base module called partial attention convolution (PAConv) and develop three variants of PAConv with different receptive fields to collaboratively exploit nonlocal information. Based on PAConv, we further propose a lightweight super-resolution network called partial attention feature aggregation network (PAFAN). Specifically, we arrange the PAConv variants in a progressive iterative manner to form the attention progressive feature distillation block (APFDB), which aims to gradually optimize the distilled features. Furthermore, we construct a multilevel aggregation spatial attention (MASA) via a stacking of the PAConv variants to systematically coordinate multiscale structural information. Extensive experiments conducted on benchmark datasets show that PAFAN achieves an optimal balance between reconstruction quality and computational efficiency. In particular, with only 123 K parameters and 0.49G FLOPs, PAFAN can maintain a performance comparable to that of SOTA methods.
{"title":"Partial Attention Feature Aggregation Network for Lightweight Remote Sensing Image Super-Resolution","authors":"Wei Xue;Tiancheng Shao;Mingyang Du;Xiao Zheng;Ping Zhong","doi":"10.1109/LGRS.2025.3601595","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3601595","url":null,"abstract":"Most lightweight super-resolution networks are designed to improve performance by introducing an attention mechanism and to reduce model parameters by designing lightweight convolutional layers. However, the introduction of the attention mechanism often leads to an increase in the number of parameters. In addition, the lightweight convolutional layer has a limited receptive field and cannot effectively capture long-range dependencies. In this letter, we design a novel lightweight base module called partial attention convolution (PAConv) and develop three variants of PAConv with different receptive fields to collaboratively exploit nonlocal information. Based on PAConv, we further propose a lightweight super-resolution network called partial attention feature aggregation network (PAFAN). Specifically, we arrange the PAConv variants in a progressive iterative manner to form the attention progressive feature distillation block (APFDB), which aims to gradually optimize the distilled features. Furthermore, we construct a multilevel aggregation spatial attention (MASA) via a stacking of the PAConv variants to systematically coordinate multiscale structural information. Extensive experiments conducted on benchmark datasets show that PAFAN achieves an optimal balance between reconstruction quality and computational efficiency. In particular, with only 123 K parameters and 0.49G FLOPs, PAFAN can maintain a performance comparable to that of SOTA methods.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914216","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 : 2025-08-22DOI: 10.1109/LGRS.2025.3601585
Zhilin Qu;Mingzhe Li;Chenggong Wang;Zehua Chen
Road extraction from remote sensing imagery plays a pivotal role in a wide range of geospatial and urban applications. Nevertheless, this task remains inherently challenging due to the intricate morphological variations of roads and frequent occlusions or interference caused by complex background environments. To address these challenges, we propose a road extraction network based on gated global–local linear attention (G$^2$ L$^2$ Attention). First, we introduce a linear deformable convolution and design a linear input-dependent deformable convolution (LID2Conv), which adaptively modulates convolution offsets and weights in a content-aware manner. In addition, we design a top-K-based sparse gated weight (TGW). We use this gated mechanism as a shared weight to multiply with local and global information to achieve G2L2Attention. Local information is obtained by LID2Conv, and we gain global information by introducing 2-D selective scan (SS2D). These two pathways are integrated through the proposed G2L2Attention, enabling an efficient and consistent fusion of hierarchical spatial features. The extracted features are passed to the decoder. This approach improves road detail representation and provides accurate contextual information. Experiments conducted on three public road datasets demonstrate that G2L2Net outperforms the existing methods in various evaluation metrics. Our source code is available at https://github.com/ZehuaChenLab
从遥感影像中提取道路在广泛的地理空间和城市应用中起着关键作用。然而，由于道路复杂的形态变化和复杂背景环境引起的频繁遮挡或干扰，这项任务仍然具有固有的挑战性。为了解决这些挑战，我们提出了一个基于门控全局-局部线性关注（G $^2$ L $^2$ attention）的道路提取网络。首先，我们引入了一个线性可变形卷积，并设计了一个线性输入相关的可变形卷积（LID2Conv），该卷积以内容感知的方式自适应调节卷积偏移量和权重。此外，我们设计了一个基于顶部的稀疏门控权（TGW）。我们使用这种门控机制作为共享权值与局部和全局信息相乘来实现G2L2Attention。局部信息由LID2Conv获取，全局信息由二维选择性扫描（SS2D）获取。通过提出的G2L2Attention将这两条路径整合在一起，实现了分层空间特征的高效一致融合。提取的特征被传递给解码器。这种方法改进了道路细节表示，并提供了准确的上下文信息。在三个公共道路数据集上进行的实验表明，G2L2Net在各种评估指标上优于现有方法。我们的源代码可从https://github.com/ZehuaChenLab获得
{"title":"G2L2Net: A Road Extraction Method for Remote Sensing Images via Gated Global–Local Linear Attention","authors":"Zhilin Qu;Mingzhe Li;Chenggong Wang;Zehua Chen","doi":"10.1109/LGRS.2025.3601585","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3601585","url":null,"abstract":"Road extraction from remote sensing imagery plays a pivotal role in a wide range of geospatial and urban applications. Nevertheless, this task remains inherently challenging due to the intricate morphological variations of roads and frequent occlusions or interference caused by complex background environments. To address these challenges, we propose a road extraction network based on gated global–local linear attention (G<inline-formula> <tex-math>$^2$ </tex-math></inline-formula>L<inline-formula> <tex-math>$^2$ </tex-math></inline-formula>Attention). First, we introduce a linear deformable convolution and design a linear input-dependent deformable convolution (LID2Conv), which adaptively modulates convolution offsets and weights in a content-aware manner. In addition, we design a top-K-based sparse gated weight (TGW). We use this gated mechanism as a shared weight to multiply with local and global information to achieve G2L2Attention. Local information is obtained by LID2Conv, and we gain global information by introducing 2-D selective scan (SS2D). These two pathways are integrated through the proposed G2L2Attention, enabling an efficient and consistent fusion of hierarchical spatial features. The extracted features are passed to the decoder. This approach improves road detail representation and provides accurate contextual information. Experiments conducted on three public road datasets demonstrate that G2L2Net outperforms the existing methods in various evaluation metrics. Our source code is available at <uri>https://github.com/ZehuaChenLab</uri>","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061937","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 : 2025-08-21DOI: 10.1109/LGRS.2025.3601112
Jin Xing;Feng Wang;Dongkai Yang;Chuanrui Tan;Xiangchao Ma;Wenqian Chen;Guangmiao Ji
Global navigation satellite system-reflectometry (GNSS-R) provides an effective remote sensing technique for accurate retrieval of sea surface height (SSH) measurements. However, accuracy is severely affected by environmental disturbances such as wind-induced sea clutter and wave interference, degrading delay–Doppler map (DDM)-derived measurements. In this study, we propose an advanced trajectory-based deep learning model, Crossformer, explicitly designed to capture temporal dependencies inherent in GNSS-R sequential data. The method leverages five distinct DDM features: peak power point (PPP), maximum slope point (MSP), center pixel intensity (CPI), average power point (APP), and kurtosis (KUR). A dimension-segmentwise (DSW) embedding technique combined with a two-stage attention (TSA) mechanism effectively models both temporal and cross-dimensional correlations. Evaluation using CYGNSS data validated against Jason-3 Level 2 measurements demonstrates the superior performance of our approach, yielding a root mean square error (RMSE) of 0.93 m, mean absolute error (MAE) of 0.65 m, and a coefficient of determination ($R^{2}$ ) of 0.9901. Comparative analyses with baseline methods confirm significant improvements in robustness and predictive accuracy, particularly across varying sea states. This research underscores the potential of advanced temporal modeling techniques in GNSS-R altimetry applications.
{"title":"A Crossformer-Based Method for Sea Surface Height Prediction Using Delay–Doppler Map Feature Points","authors":"Jin Xing;Feng Wang;Dongkai Yang;Chuanrui Tan;Xiangchao Ma;Wenqian Chen;Guangmiao Ji","doi":"10.1109/LGRS.2025.3601112","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3601112","url":null,"abstract":"Global navigation satellite system-reflectometry (GNSS-R) provides an effective remote sensing technique for accurate retrieval of sea surface height (SSH) measurements. However, accuracy is severely affected by environmental disturbances such as wind-induced sea clutter and wave interference, degrading delay–Doppler map (DDM)-derived measurements. In this study, we propose an advanced trajectory-based deep learning model, Crossformer, explicitly designed to capture temporal dependencies inherent in GNSS-R sequential data. The method leverages five distinct DDM features: peak power point (PPP), maximum slope point (MSP), center pixel intensity (CPI), average power point (APP), and kurtosis (KUR). A dimension-segmentwise (DSW) embedding technique combined with a two-stage attention (TSA) mechanism effectively models both temporal and cross-dimensional correlations. Evaluation using CYGNSS data validated against Jason-3 Level 2 measurements demonstrates the superior performance of our approach, yielding a root mean square error (RMSE) of 0.93 m, mean absolute error (MAE) of 0.65 m, and a coefficient of determination (<inline-formula> <tex-math>$R^{2}$ </tex-math></inline-formula>) of 0.9901. Comparative analyses with baseline methods confirm significant improvements in robustness and predictive accuracy, particularly across varying sea states. This research underscores the potential of advanced temporal modeling techniques in GNSS-R altimetry applications.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934401","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 : 2025-08-21DOI: 10.1109/LGRS.2025.3601131
Jingang Wang;Shikai Wu;Peng Liu
Maritime monitoring is crucial in both civilian and military applications, with shore-based radar and visual systems widely used due to their cost effectiveness. However, single-sensor methods have notable limitations: radar systems, while offering wide detection coverage, suffer from high false alarm rates and lack detailed target information, whereas visual systems provide rich details but perform poorly in adverse weather conditions such as rain and fog. To address these issues, this letter proposes a progressive radar–vision fusion method for surface target detection. Due to the significant differences in data characteristics between radar and visual sensors, direct fusion is nearly infeasible. Instead, the proposed method adopts a stepwise fusion strategy, consisting of coordinate calibration, shallow feature fusion, and deep feature integration. Experimental results show that this approach achieves an $text {mAP}_{50}$ of 86.7% and an $text {mAP}_{75}$ of 54.5%, outperforming YOLOv10 by 1.0% and 1.5%, respectively. Moreover, the proposed method significantly surpasses existing state-of-the-art radar–vision fusion approaches, demonstrating its superior effectiveness in complex environments.
{"title":"ProFus: Progressive Radar–Vision Heterogeneous Modality Fusion for Maritime Target Detection","authors":"Jingang Wang;Shikai Wu;Peng Liu","doi":"10.1109/LGRS.2025.3601131","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3601131","url":null,"abstract":"Maritime monitoring is crucial in both civilian and military applications, with shore-based radar and visual systems widely used due to their cost effectiveness. However, single-sensor methods have notable limitations: radar systems, while offering wide detection coverage, suffer from high false alarm rates and lack detailed target information, whereas visual systems provide rich details but perform poorly in adverse weather conditions such as rain and fog. To address these issues, this letter proposes a progressive radar–vision fusion method for surface target detection. Due to the significant differences in data characteristics between radar and visual sensors, direct fusion is nearly infeasible. Instead, the proposed method adopts a stepwise fusion strategy, consisting of coordinate calibration, shallow feature fusion, and deep feature integration. Experimental results show that this approach achieves an <inline-formula> <tex-math>$text {mAP}_{50}$ </tex-math></inline-formula> of 86.7% and an <inline-formula> <tex-math>$text {mAP}_{75}$ </tex-math></inline-formula> of 54.5%, outperforming YOLOv10 by 1.0% and 1.5%, respectively. Moreover, the proposed method significantly surpasses existing state-of-the-art radar–vision fusion approaches, demonstrating its superior effectiveness in complex environments.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914173","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 : 2025-08-21DOI: 10.1109/LGRS.2025.3601152
Xiaorun Li;Jinhui Li;Shuhan Chen;Zeyu Cao
In recent years, self-supervised learning has made significant strides in hyperspectral image classification (HSIC). However, different approaches come with distinct strengths and limitations. Contrastive learning excels at extracting key information from large volumes of redundant data, but its training objective can inadvertently increase intraclass feature distance. To address this limitation, we leverage diffusion models (DMs) for their proven ability to refine and aggregate features by modeling complex data distributions. Specifically, DMs’ inherent denoising and generative processes are theoretically well-suited to enhance intraclass compactness by learning to reconstruct clean, representative features from perturbed inputs. We propose the new method—ContrastDM. This approach generates synthetic features, improving and enriching feature representation, and partially addressing the issue of sample sparsity. Classification experiments on three publicly available datasets demonstrate that ContrastDM significantly outperforms state-of-the-art methods.
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