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

{"title":"A Multiscale Vision Transformer-Based Multimodal GeoAI Model for Mapping Arctic Permafrost Thaw","authors":"Wenwen Li;Chia-Yu Hsu;Sizhe Wang;Zhining Gu;Yili Yang;Brendan M. Rogers;Anna Liljedahl","doi":"10.1109/JSTARS.2025.3564310","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3564310","url":null,"abstract":"Retrogressive Thaw Slumps (RTS) in Arctic regions are distinct permafrost landforms with significant environmental impacts. Mapping these RTS is crucial because their appearance serves as a clear indication of permafrost thaw. However, their small scale compared to other landform features, vague boundaries, and spatiotemporal variation pose significant challenges for accurate detection. In this article, we extend a state-of-the-art deep learning model to delineate RTS features across the Arctic in a multimodal setting. Two new strategies were introduced to optimize multimodal learning and enhance the model's predictive performance: 1) a feature-level, residual cross-modality attention fusion strategy, which effectively integrates feature maps from multiple modalities to capture complementary information and improve the model's ability to understand complex patterns and relationships within the data; 2) pretrained unimodal learning followed by multimodal fine-tuning to alleviate high computing demand while achieving strong model performance. Experimental results demonstrated that our approach outperformed existing models adopting data-level fusion, feature-level convolutional fusion, and various attention fusion strategies, providing valuable insights into the efficient utilization of multimodal data for RTS mapping. This research contributes to our understanding of permafrost landforms and their environmental implications.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"12209-12223"},"PeriodicalIF":4.7,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979371","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125644","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":"Demonstrating How FPCA Can Leverage SAR Time-Series Information to Distinguish Wetlands and Uplands Based on Seasonal Backscatter Trends","authors":"Sarah N. Banks;Amir Behnamian;Kenneth C. K. Chu;Ryan Hamilton;Jason Duffe;Jon Pasher","doi":"10.1109/JSTARS.2025.3564835","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3564835","url":null,"abstract":"Wetlands are important but vulnerable ecosystems that must be accurately mapped and monitored to effectively guide restoration and conservation planning. In this study, we used functional principal component analysis (FPCA) to leverage synthetic aperture radar (SAR) time-series information and explore whether common wetlands and uplands can be separated based on seasonal trends in backscatter intensity. To contextualize the results, we first identify the drivers of change and analyze variations in seasonal backscatter intensity trends using four years of <inline-formula><tex-math>$C$</tex-math></inline-formula>-band Sentinel-1 VV and VH data. We then trained an FPCA-based feature extraction engine to, first, evaluate the potential of FPCA to improve SAR time series handling and interpretation, second, evaluate the spatio-temporal consistency and separability of derived scores, and third, investigate whether adaptive training can improve the predictive power of FPCA scores. The results showed that microwave-surface interactions vary seasonally between classes. This was primarily due to changes in phenology and hydrology, whose effects on backscatter varied depending on target characteristics such as plant functional type. On the other hand, scores were relatively consistent within each specified class, though shifted according to some significant changes in target characteristics. Classification of scores using random forest demonstrated that the method was effective in generating discriminant features. Independent overall accuracies ranged from 83% to 89% even when the model was applied to unseen data and in spite of inherent difficulties distinguishing between swamp and forest. Retraining and reapplying FPCA to better capture the variation specific to these classes also demonstrated that the predictive power of scores remained constrained by the inherent limitations of <inline-formula><tex-math>$C$</tex-math></inline-formula>-band VV and VH polarized data for detecting surface water in forested areas. Overall, these findings highlight the potential of FPCA to improve the handling and interpretation of SAR time series, and that seasonal backscatter intensity trends, captured by FPCA scores, can effectively separate multiple common wetlands and uplands.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"14416-14437"},"PeriodicalIF":4.7,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979204","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144299060","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":"Volcano-Seismic Event Detection and Clustering","authors":"Joe Carthy;Pablo Rey-Devesa;Manuel Titos;Carmen Benitez","doi":"10.1109/JSTARS.2025.3559412","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559412","url":null,"abstract":"This study looks into unsupervised and supervised methods for detecting events in volcano-seismic time series data, segmenting the data, and clustering the segments where there is activity. This two-stage pipeline allows for the analysis of the signals without requiring the type of event to be identified at the offset and reduces the manpower required to analyze new data. Due to the resource intensive labeling process required to understand volcano-seismic signals it is important to explore unsupervised analysis techniques in this domain. The unsupervised methods are evaluated using supervised metrics including completeness, homogeneity, and V-measure scores. Alongside the unsupervised investigation, the use of intersection-based metrics that offer a clearer performance evaluation of the event segmentation task is motivated and the potential of gradient boosted trees for event detection is tested.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11276-11289"},"PeriodicalIF":4.7,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10978850","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900503","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 Learning-Based Dual-Scale Enhanced Confidence for DSM Fusion in 3-D Reconstruction of Multiview Satellite Images","authors":"Shuting Yang;Hao Chen;Fachuan He;Wen Chen;Ting Chen;Jianjun He","doi":"10.1109/JSTARS.2025.3564378","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3564378","url":null,"abstract":"Compared to two-view reconstruction, multiview imagery leverages redundant information to mitigate the effects of occlusion and noise. Deep-learning-based multiview stereo (MVS) methods are primarily applicable to tristereo data captured simultaneously and rely heavily on training samples. Traditional MVS methods typically rely on simple filtering and weighting techniques for digital surface model (DSM) fusion based on image pair selection and pairwise stereo matching, which are usually affected by poor image pairs and fail to fully exploit the complementary advantages of DSMs. To address these limitations, this article proposes a novel DSM fusion method incorporating learning-based dual-scale enhanced confidence for three-dimensional reconstruction from multiview satellite imagery. First, a generalized stereo matching method is adopted, which considers radiometric differences and small feature variations. Next, auxiliary information generated during pairwise stereo reconstruction is utilized to construct a high-dimensional confidence vector that includes classical confidence measures and a newly designed topological structure relationship consistency measure. Then, a guided regularized random forest regressor is employed to identify influential confidence measures and establish their correlation with reconstruction accuracy, leading to the estimation of enhanced confidence. Additionally, to preserve fine details and boundary information, the dual-scale enhanced confidence is introduced to facilitate cross-scale DSM fusion. Finally, each view is sequentially treated as the master view to obtain DSMs, which are then fused to produce the final DSM. Experimental results demonstrate that the proposed method achieves superior performance across various datasets, including tristereo Beijing-3 data acquired nearly simultaneously, multiview WorldView-3 data captured at different times, and self-made tristereo data collected from different sensors at different times. The proposed method achieves an average MAE of 1.14 m, RMSE of 2.16 m, median height error of 0.47 m, and COMP of 75.13%, outperforming several mainstream methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11767-11786"},"PeriodicalIF":4.7,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10977986","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084789","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":"Advancing Shallow Water Bathymetry Estimation in Coral Reef Areas via Stacking Ensemble Machine Learning Approach","authors":"Jian Cheng;Sensen Chu;Liang Cheng","doi":"10.1109/JSTARS.2025.3564362","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3564362","url":null,"abstract":"Satellite-derived bathymetry technology plays a pivotal role in estimating shallow water depths. Although traditional machine learning (ML) models are extensively applied in water depth inversion, they frequently exhibit inconsistent performance across various environments, highlighting the challenge of constructing a model with high precision and robustness. This study proposed an innovative stacking ensemble ML (SEML) model, integrating the advantages of various mainstream ML algorithms to address this challenge. We evaluated the bathymetric performance of the SEML model by combining multitemporal Sentinel-2 imagery and sonar data from Houteng Reef and Wufang Reef in the Spratly Islands. The findings showed the performance rankings of these models at Houteng Reef were SEML, K-nearest neighbor (KNN), support vector machine (SVM), multilayer perceptron (MLP), and RF, while at Wufang Reef, they shifted to SEML, SVM, MLP, KNN, and RF. By contrast, the SEML outperformed traditional ML models in accuracy and robustness. At Houteng Reef, the SEML achieved an RMSE of 0.46 m, representing a 13.21% decrease compared to KNN. Similarly, at Wufang Reef, the RMSE of the SEML model was 0.75 m, achieving a 5.06% decrease compared to SVM. The SEML model significantly enhances the accuracy and robustness of water depth estimation, providing a new perspective for accurately mapping coral reef bathymetry.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"12511-12530"},"PeriodicalIF":4.7,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10976543","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125414","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":"More Accurate Constraints for Self-Supervised Learning in Remote Sensing Images-Based Object Detection","authors":"Shangdong Zheng;Zebin Wu;Yang Xu;Qian Liu;Zhihui Wei","doi":"10.1109/JSTARS.2025.3564368","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3564368","url":null,"abstract":"Self-supervised learning (SSL) automatically generates internal labels by exploring the potential auxiliary task of the network itself, and trains the same model through these annotations to learn the latent representation of the data, which greatly improves the accuracy of object detection in remote sensing images (RSIs). However, most existing methods suffer from guaranteeing the quality of the generated SSL pseudoannotations, and the constructed auxiliary tasks are not detection-oriented, which is difficult to enhance the feature representations that are beneficial for object detection. In this article, we focus on generating more accurate constraints by excavating the intercorrelation between fully and weakly supervised learning (WSL) to improve the performance of object detection in RSIs. Initially, WSL assigns the pseudoinstance-level annotations for the high-scoring positive bags to model the detector, which can be regarded as a weakened version of the region proposal network (RPN). Fortunately, RPN can be constrained by the ground truth (GT) of bounding boxes in fully supervised learning (FSL), and the high-quality supervisions it provides are unavailable in any WSL methods. Moreover, we construct a proposal generation module, which further filters the unreliable bounding boxes, predicted by RPN, to supplement high-quality constraints into the GT and SS generated candidate boxes to supervise the optimization of WSL branch. By constructing an interactive learning paradigm of WSL and FSL, the former has more accurate constraints to learn an efficient auxiliary task, while the latter enjoys a richer representation form of data provided by WSL, which is undoubtedly a win–win process. Finally, we cascade the losses of WSL and FSL to further explore the intrinsic correlation between them by sharing the same feature extraction network. Experimental comparisons on DOTA and DIOR datasets demonstrate that our method achieves superior performance than many recent object detection approaches by the significant margin.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"12303-12314"},"PeriodicalIF":4.7,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10976539","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123344","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":"Evaluating Multiparameter Response to Seismic Thermal Anomalies From Global Major Earthquakes","authors":"Meng Jiang;Feng Jing;Lu Zhang","doi":"10.1109/JSTARS.2025.3563992","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3563992","url":null,"abstract":"Thermal anomalies (TAs) associated with earthquake activity have been widely studied by analyzing various satellite thermal infrared datasets. However, there is a significant lack of research on key response parameters. In the present work, based on <italic>M</i>≥7.0 earthquakes worldwide from 2007 to 2022, we evaluate multiple parameters for the detection of seismic TAs by analyzing the significant sequence of TAs and the defined performance indicators. We considered seven thermal-related parameters, including skin temperature, surface latent heat flux, surface net thermal radiation (STR), and 2 m temperature (T2m) from ERA5; clear-sky outgoing longwave radiation (ClrOLR) and outgoing longwave radiation from AIRS and NOAA (AIRS-OLR and NOAA-OLR). The classification evaluation was conducted based on focal mechanisms (normal fault, thrust fault, and strike-slip fault) and earthquake locations (continental and oceanic earthquake). Our results show that ClrOLR and T2m have remarkable effectiveness in detecting seismic TAs. From the perspective of focal mechanism, T2m performs best for the earthquakes triggered by normal faults and also good for thrust fault events, and ClrOLR is most effective for those triggered by strike-slip faults. In terms of location, ClrOLR preforms best for continental earthquakes, while T2m is most effective for oceanic earthquakes. By conducting a comparative analysis using a synthetic earthquake catalog and modifying the nonseismic time window, we have demonstrated that our results are robust. Our results provide valuable information for the application of thermal-related parameters in earthquake prediction.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11835-11850"},"PeriodicalIF":4.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10976362","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073018","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":"Spatiotemporal Soil Moisture Prediction Using a Causal-Guided Deep Learning Model","authors":"Tingtao Wu;Lei Xu;Ziwei Pan;Ruinan Cai;Jin Dai;Shuang Yang;Xihao Zhang;Xi Zhang;Nengcheng Chen","doi":"10.1109/JSTARS.2025.3564182","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3564182","url":null,"abstract":"The spatiotemporal prediction of RZSM refers to the process of estimating its future spatial distribution and temporal variations using predictive models. The accurate spatiotemporal predictions of soil moisture provide insights into future conditions, supporting decision making in applications, such as crop yield optimization, irrigation planning, and drought management. However, existing models face limitations in capturing complex spatiotemporal dependencies and dynamic causal interactions. This article proposes a spatiotemporal prediction framework that integrates causal inference with deep learning, termed the causal-guided spatiotemporal Swin transformer (Causal ST-SwinT). The model introduces a dynamic causal weight adjustment mechanism to adaptively optimize the causal relationship intensity between variables and adopts a hierarchical multilevel feature extraction strategy to effectively capture complex spatiotemporal dependencies, thereby enhancing prediction accuracy and model interpretability. The proposed method is validated on the ERA5 and soil moisture active passive (SMAP) datasets over the Tibetan Plateau and compared with multiple models. Experimental results show that Causal ST-SwinT significantly outperforms the classical convolutional long short-term memory model, reducing mean absolute error from 0.0146 to 0.0055 m³/m³ on the ERA5 dataset and from 0.0088 to 0.0046 m³/m³ on the SMAP dataset. Robustness analysis reveals that Causal ST-SwinT maintains high prediction accuracy under various environmental conditions. Ablation experiments further confirm the critical role of the causal attention module in improving model performance. These findings demonstrate that integrating causal knowledge with deep learning models effectively enhances the modeling capabilities of complex spatiotemporal systems, providing a novel solution for broader spatiotemporal prediction tasks.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"12166-12179"},"PeriodicalIF":4.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10976363","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123345","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":"kLCRNet: Fast Road Network Extraction via Keypoint-Driven Local Connectivity Exploration","authors":"Mingming Zhang;Bin Wang;Shuai Yang;Qingjie Liu;Yunhong Wang","doi":"10.1109/JSTARS.2025.3564060","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3564060","url":null,"abstract":"Road network extraction from remote sensing images has been extensively studied in recent decades. While many approaches output road networks in vector format, most are not fully end-to-end, requiring time consuming postprocessing steps. In addition, challenges like isomorphic encoding limit the flexibility of these methods. In this article, we present kLCRNet, an efficient road network extraction framework that overcomes these limitations by leveraging keypoint-driven local connectivity exploration. kLCRNet consists of two key components: A keypoint detection module that identifies road keypoints via heatmap-based detection and refines them using bipartite matching, and a local connectivity exploration module that samples local connection relationships to directly construct connectivity between detected keypoints. Experiments on the CityScale and SpaceNet datasets demonstrate that kLCRNet outperforms state-of-the-art methods in topological accuracy and connectivity. In addition, kLCRNet significantly improves inference speed by up to 25 times, highlighting its efficiency and effectiveness.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"12074-12089"},"PeriodicalIF":4.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10976365","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131456","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":"Automatic Detection for Mining Subsidence Areas Using the CBAM-Enhanced VGG-UNet Model With Long Time Series InSAR Interferograms","authors":"Kegui Jiang;Keming Yang;Mengting Gao;Liuguo Zhu;Chuang Jiang","doi":"10.1109/JSTARS.2025.3563770","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3563770","url":null,"abstract":"The technical theories of monitoring and preventing mining subsidence have long been key challenges and research priorities in the mining field. The rapid advancements in remote sensing technology and deep learning algorithms have enabled significant breakthroughs in monitoring and accurately identifying mining subsidence. In this article, a novel automatic detection method for mining subsidence is proposed using interferometric synthetic aperture radar (InSAR) wrapped interferograms. First, this study designs a VGG-UNet model enhanced by an attention mechanism module to learn and detect mining subsidence areas. This enhancement improves the feature representation and perception capabilities of the model. Second, to address the scarcity of real InSAR data in the training set, an efficient dataset simulation strategy is established. This strategy incorporates the realistic scenarios of monitoring the environment to improve the effect of model training. Finally, a complete workflow for model training and detection application is developed. The results demonstrate that the detection model achieves a precision of 92.55%, a recall of 90.43%, an accuracy of 93.37%, an F₁-score of 91.46%, and an intersection over union of 84.25% on the validation set. The model was applied to mining subsidence detection in the Huaibei–Yongcheng mining area, China, from June 2017 to July 2024. A total of 103 mining subsidence sites were identified, and their long-time series characteristics and the spatial distribution pattern of subsidence accumulation duration were analyzed. The findings offer critical technical support for sustainable mining management and land resource protection at the regional scale.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11926-11940"},"PeriodicalIF":4.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10974639","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084790","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}