IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing最新文献

{"title":"Multilevel Differential Aggregation With Gated Discrimination Network for Hyperspectral-LiDAR Joint Land Cover Classification","authors":"Haizhu Pan;Bopeng Ren;Xuehu Li;Haimiao Ge;Cuiping Shi;Moqi Liu;Chunxue Xia;Jingshu Lv","doi":"10.1109/JSTARS.2025.3604076","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3604076","url":null,"abstract":"With the rapid development of Earth observation technologies, the integration of multisource remote sensing data provides great potential for land cover classification. Hyperspectral images (HSI) contain abundant spectralspatial information, while light detection and ranging (LiDAR) offers accurate 3-D structural data. Their effective fusion can significantly enhance classification accuracy. However, existing methods often retain redundant information during feature extraction and fusion, and such redundancy weakens the discriminative power of networks. Moreover, the irregular spatial distributions in multisource data make rulebased meshing ineffective for modeling complex geometric structures, limiting the capture of subtle cross-domain spatial features. To address these challenges, we propose a multilevel differential aggregation with gated discriminative network (MDAGNet) based on dynamic feature selection for joint HSILiDAR classification. First, a multilevel differential aggregation module separately extracts HSI spectral features and LiDAR elevation features to generate discriminative multilevel representations. Second, an adaptive discrimination module with a gating mechanism distills redundant information through multiscale discriminative operations, achieving deep fusion of critical features. Third, a morphological strip convolution module captures irregular geometric distributions in HSI, enhancing fine-grained feature perception. Finally, adaptive multimodal fusion is achieved through weight coupling and dynamic fusion strategies. Experiments on three public datasets demonstrate that the proposed method achieves superior accuracy and generalization compared with state-of-the-art approaches.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"22349-22371"},"PeriodicalIF":5.3,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11144477","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Class-Aware Semi-Supervised Framework for Semantic Segmentation of High-Resolution Remote Sensing Imagery","authors":"Shirou Wang;Cheng Su;Xiaocan Zhang","doi":"10.1109/JSTARS.2025.3601148","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3601148","url":null,"abstract":"Advancements in deep learning have significantly improved the semantic segmentation of high-resolution remote sensing imagery (HRSI). However, these approaches typically rely on large-scale, pixel-wise annotations, the acquisition of which is both time-consuming and labor-intensive. This challenge is further amplified in remote sensing, where massive data volumes, strong spatiotemporal heterogeneity, and frequent updates pose additional difficulties. Semi-supervised learning (SSL) has emerged as a promising alternative by utilizing a small amount of labeled data in conjunction with abundant unlabeled data. Despite its potential, the effectiveness of SSL in HRSI is often hindered by two key factors: class imbalance induced by scale variations among objects, and class confusion caused by high intraclass variability and low interclass separability. This study proposes a class-aware semi-supervised semantic segmentation framework that incorporates two key modules: the stratified selection module (SSM) and the category contrastive module (CCM). The SSM adopts a dynamic adaptive thresholding strategy to guide class-specific pseudolabel selection, thus alleviating the impact of class imbalance. The CCM introduces a pixel-to-prototype contrastive mechanism leveraging category-level guidance to improve feature separability and mitigate confusion between similar classes. Experimental results on the ISPRS Vaihingen, Potsdam, and LoveDA datasets validate the effectiveness of the proposed framework under varying annotation ratios. In comparison with state-of-the-art methods, the framework consistently demonstrates superior performance, achieving overall higher accuracy and stability. In particular, it shows robust performance in low-label settings, highlighting its potential for practical applications where annotation resources are limited.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"22372-22391"},"PeriodicalIF":5.3,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11143061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Robust Multimodal Wide-Field Satellite Image Registration Algorithm Based on Weighted Random Partition Optimization","authors":"Da He;Jiabei Zhang;Zhao Tan;Jiwei Deng;Guangshuai Wang;Yongxiang Yao;Yi Wan;Jiangping Chen;Yansheng Li;Yongjun Zhang","doi":"10.1109/JSTARS.2025.3603306","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3603306","url":null,"abstract":"Continuous advancements in sensor technology have expanded the dimensions and diversity of satellite imagery, yet intensified geometric processing challenges for multimodal remote sensing images (MRSIs). Enlarged image scales amplify susceptibility to suboptimal alignments under heterogeneous correspondence point distributions, manifesting as localized misregistration and structural artifacts. To address these challenges, this article presents a weighted random partition optimization (WRPO) method for the robust registration of wide-field MRSI. The method contributes primarily in two ways. 1) The construction of a strong-weak information interfeeding randomized optimization block model, which divides the wide-field image into subregions and ranks local strong and weak feature blocks. Random sampling and overlapping of strong and weak blocks are employed to ensure a uniform distribution of corresponding points. 2) The development of multichannel directional gradient feature descriptors, which enable efficient multidirectional feature matching for fast registration. The experimental framework employed four MRSI datasets to benchmark WRPO against six state-of-the-art algorithms. Quantitative results establish key advantages of WRPO: 1) The proposed method achieves a 400% higher matching efficiency compared to five benchmark methods; 2) Its subpixel matching precision effectively eliminates local misregistration and artifacts; 3) WRPO uniquely succeeds in processing wide-field images. The algorithm exhibits exceptional generalization—integration with baseline methods enhances their performance. WRPO achieves efficient high-precision registration with dense correspondence points, while maintaining strong transferability across heterogeneous satellite data.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"22334-22348"},"PeriodicalIF":5.3,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142738","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145043857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semiautomatic Workflow for Accurate LiDAR-Derived DSM Retrieval in Aquatic Scenarios via Water Surface Mapping","authors":"Erika Piaser;Andrea Berton;Giovanna Sona;Paolo Villa","doi":"10.1109/JSTARS.2025.3602156","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3602156","url":null,"abstract":"High-resolution, 3-D water surface mapping in aquatic environments is critical for evaluating complex interactions between human activities and environmental dynamics. Despite the overall potential of LiDAR data to generate 3-D point clouds, providing the accurate and complete digital surface models (DSMs) at the interface between terrestrial and aquatic ecosystems is still a significantly challenging task. In fact, due to water’s strong near-infrared absorption and its near specularity, LiDAR often results in weak or missing signal returns. In addition, direct linear interpolation for gap filling can introduce biases in the DSM reconstruction, especially near shorelines. This study proposes a four-step semiautomatic, open-source workflow for high-resolution DSM reconstruction in aquatic scenarios, using unsupervised machine learning for land–water classification based on optimized LiDAR-derived features. Mean water-level surface elevation, extracted from binary scene clustering, was used to fill DSM gaps over water via ad hoc gap filling. The accuracy of the resulting “water-filled” DSM (WFDSM) was evaluated across six diverse real-world aquatic scenarios with a range of challenging conditions (e.g., presence of aquatic vegetation, detached ponds, man-made structures, and land depressions) and compared against open-source products. Unsupervised clustering combining radiometric and geometric features achieved high classification accuracy (<italic>F</i>-score > 0.97) for “water-level” targets, with negligible commission errors. Unlike standard products, WFDSM effectively handles variations in terrain and surface, maintaining low elevation biases (< 25 cm) even in areas with complex vegetation and fine-scale anthropogenic structures, thus demonstrating high suitability in both transitional and open-water areas.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"22673-22689"},"PeriodicalIF":5.3,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11141703","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145051016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Extraction of Impervious Surface Area Using Hyperlocal Samples From Multisource Data Fusion Across Economic–Geographic Zones","authors":"Nan Wang;Yunlan Guan;Yuqian Wang;Qirui Fang;Zixuan Li;Jian Dong;Jing Luo","doi":"10.1109/JSTARS.2025.3602036","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3602036","url":null,"abstract":"Due to their complex spatial structure and spectral heterogeneity, mapping impervious surface area (ISA) at large scales with high resolution remains challenging. Key issues include labor-intensive sample collection, suboptimal feature selection that often fails to account for regional climate and urban development differences, and the lack of up-to-date ISA products at 10 m resolution. To address these limitations, we propose an automated ISA extraction method based on economic–geographic zoning and multisource data fusion using hyperlocal samples. The study area is first divided into zones based on climate zones and urban development levels. Within each zone, hexagonal units are defined to enable localized sample selection and feature optimization. Training samples are automatically generated using rule-based and threshold-based methods, incorporating land use, land cover, and human activity information from OpenStreetMap, Sentinel-2 imagery, and Black Marble nighttime light (NTL) imagery. A total of 45 features are constructed, with NTL features playing an innovative role in improving classification performance. The recursive feature elimination with cross-validation algorithm is applied to select the most relevant features for each zone. Based on the Google Earth engine platform, a regionally adaptive random forest model is implemented to produce a 10-m-resolution ISA map of the study area. The results show that the proposed hyperlocal sampling strategy significantly improves sample quality. Zonal feature selection not only simplifies the model but also enhances interpretability and classification accuracy. Quantitative analysis confirms that NTL features are among the most significant inputs. The proposed method achieves an overall accuracy of 90.72%, outperforming existing ISA products GAIA, FROM_GLC10, GHSL, and ESRI_LandCover by 7.12%, 3.02%, 5.07%, and 2.47%, respectively, demonstrating its effectiveness.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"22602-22619"},"PeriodicalIF":5.3,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134770","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LAI Inversion for Winter Wheat From UAV Hyperspectral Data Considering Crop Growth Heterogeneity","authors":"Jiahui Feng;Zhaozhao Zeng;Yuyun Liang;Jun Li","doi":"10.1109/JSTARS.2025.3602113","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3602113","url":null,"abstract":"Leaf area index (LAI) is a key parameter for reflecting crop canopy structure and growth status, and crucial for precision agricultural management. Due to high acquisition costs and limitations of obtaining ground observation data, the radiative transfer models are widely used to generate simulated spectral data for inversion models. However, neglecting variations caused by factors like irrigation, fertilization, and soil differences may lead to some errors in inversion results. To address this problem, we proposed a stratified hybrid inversion method based on growth heterogeneity (GH-SHI). First, a simple linear iterative clustering algorithm was applied for superpixel-level image segmentation. Then, a growth status index was proposed to characterize the wheat growth heterogeneity and was used to classify the segmented regions into five winter wheat growth stages. For each category, simulated samples were generated using the coupled PROSPECT+SAIL (PROSAIL) radiative transfer model with the stratified parameters ranges to train BP neural network models and obtain LAI inversion results. Results show that the GH-SHI method achieves higher LAI inversion accuracy on the measured site data compared with the traditional hybrid method, with about a 50% reduction in RMSE (from 0.86 to 0.40), an improvement in <italic>R</i><inline-formula><tex-math>$^{2}$</tex-math></inline-formula> by 0.48 (from 0.13 to 0.61), and an increase in the correlation coefficient <italic>R</i> by 0.2 (from 0.64 to 0.84). Meanwhile, the correlation between the normalized difference vegetation index (NDVI) and LAI was used to further validate the effectiveness of the proposed method. The results show that, compared to the traditional approach, the LAI estimated by the GH-SHI method exhibits a stronger correlation with NDVI, further confirming the reliability of its inversion performance. In summary, the proposed method not only improves the accuracy of LAI inversion, but also demonstrates adaptability to different growth stages and diverse crop conditions, showing potential for application in large-scale, site-free regions.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"21578-21592"},"PeriodicalIF":5.3,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134771","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Joint Real- and Complex-Valued Network for Classification of Pol(In)SAR Images","authors":"Yaobin Ma;Hossein Aghababaei;Ling Chang;Xiaohua Deng;Jingbo Wei","doi":"10.1109/JSTARS.2025.3602161","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3602161","url":null,"abstract":"Synthetic aperture radar (SAR) systems capture both amplitude and phase information, producing complex-valued images widely used in Earth observation applications. Among SAR modalities, polarimetric SAR and polarimetric interferometric SAR systems leverage polarimetric and interferometric information, typically represented by a data coherency matrix comprising real-valued diagonal elements and complex-valued off-diagonal elements. Existing deep learning approaches process the entire coherency matrix either through purely real-valued or purely complex-valued networks, which fails to fully exploit its heterogeneous structure. In this article, we propose a structurally decoupled modeling strategy for coherency matrices, which explicitly separates and processes diagonal and off-diagonal components based on their distinct structural roles. A real-valued network is employed to model the diagonal scattering power components, while a complex-valued network is used to capture the cross-channel correlation, orientation, and coherence structures from the off-diagonal components. This design aligns well with the inherent organization of the SAR coherency matrix, enabling more targeted and effective feature learning. The extracted real and complex features are subsequently fused via a cross-domain enhancement fusion block to achieve robust representation learning. Experiments on DLR’s FSAR dataset demonstrate that the proposed method consistently outperforms six state-of-the-art techniques across three different SAR modalities, achieving superior performance in both quantitative accuracy and qualitative robustness.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"22256-22270"},"PeriodicalIF":5.3,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134815","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145051046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A DCT-Based Local Contrast Enhancement Algorithm in SAR Image Target Detection","authors":"Ran An;Weibo Huo;Yujie Zhang;Jifang Pei;Yin Zhang;Yulin Huang","doi":"10.1109/JSTARS.2025.3602128","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3602128","url":null,"abstract":"Synthetic aperture radar (SAR) has become an indispensable remote sensing technology for maritime surveillance. Due to the influence of sea clutter, ship targets may be submerged in background noise, making it difficult for SAR ship target detection. In order to solve this problem, a discrete cosine transform (DCT)-based local contrast enhancement algorithm (DCT-LCE) is proposed in this article. By integrating DCT with sliding window, this algorithm innovatively transforms the SAR image into the DCT domain for processing. A weighted alternating current coefficients calculation method is designed to characterize statistical features within the sliding window, providing a quantitative method for distinguishing between targets and backgrounds. In addition, as optimization and improvement of DCT-LCE, multiscale DCT local contrast enhancement (MDCT-LCE) is proposed to enhance the detailed morphological information of ship targets. Experimental simulations demonstrate that the proposed algorithms can effectively enhance ship targets. Moreover, compared with other sliding window-based algorithms, the proposed algorithms have better detection performance both in accuracy and morphological features under different levels of complexity background.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"21688-21699"},"PeriodicalIF":5.3,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134819","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DHLNet: A Dynamic Hierarchical Lightweight Network for Enhanced Ship Detection in Remote Sensing Images","authors":"Jinyu Ou;Yijun Shen;Yanlian Du","doi":"10.1109/JSTARS.2025.3601579","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3601579","url":null,"abstract":"Ship detection in remote sensing images is essential for maritime surveillance and environmental monitoring. Traditional methods often struggle to accurately identify ships in complex scenes or when targets are small, and recent deep learning approaches, while promising, still face tradeoffs between detection accuracy, inference speed, and computational complexity. To overcome these limitations, we propose dynamic hybrid convolutional network (DHLNet), a novel detection model comprising three specialized modules. DHLNet includes a dynamic hybrid block module that adaptively selects convolutional kernels for multiscale feature extraction, a faster hierarchical attention fusion block that integrates local details with global context through a multilevel attention mechanism, and a lightweight quality estimation BN head that balances spatial, channel, and scale features for efficient decoding. These innovations collectively enhance feature representation and improve detection performance without significantly increasing the computational cost. Extensive experiments on a self-collected ship dataset and public benchmarks (DOTA-Ship and VisDrone2019) validate the effectiveness of DHLNet. The proposed model outperforms state-of-the-art detectors (e.g., YOLOv8, YOLO-KAN, Mamba) in both mAP50 and F1-score metrics. For example, on our dataset, DHLNet achieves an mAP50 of 91.4%, which is 2.7% higher than that of YOLO-KAN. These results demonstrate that DHLNet effectively handles complex backgrounds and small targets, offering significant improvements in detection accuracy and efficiency for remote sensing-based ship detection.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"21783-21806"},"PeriodicalIF":5.3,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134558","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-Time Observation-Based Multiattitude ISAR Imaging Method for Moving Ships","authors":"Zhifeng Xie;Tao Lai;Qingyuan Shen;Xiaoqing Wang;Zhibing Wang","doi":"10.1109/JSTARS.2025.3601604","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3601604","url":null,"abstract":"Long-time synthetic aperture radar observation facilitates the tracking and identification of moving targets. However, continuous changes in the attitude of a moving target during long-duration observations cause the azimuth signal’s time–frequency (TF) trajectory to curve. The curvature of the TF trajectory leads to image defocusing. The varying degrees of trajectory curvature for each scatterer caused by attitude changes prevent the traditional inverse synthetic aperture radar (ISAR) autofocusing method from achieving ideal focusing results. To address this issue, we propose an innovative ISAR imaging method based on TF trajectory extraction and compensation. This method divides the observation into subtime intervals for imaging, allowing the capture of various motion attitudes of the target. By transforming the TF trajectories with varying curvature into horizontal trajectories, the proposed method effectively handles nonstationary intervals, enabling attitude image acquisition during these periods. Experimental results demonstrate that our algorithm can produce multiple attitude images of ships during long-time observations, delivering clearer imagery even in complex motion scenarios.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"21822-21839"},"PeriodicalIF":5.3,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134582","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145011286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}