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

{"title":"PPDM-YOLO: A Lightweight Algorithm for SAR Ship Image Target Detection in Complex Environments","authors":"Hongjie He;Tianwen Hu;Sheng Xu;Hong Xu;Lin Song;Zhongpei Sun","doi":"10.1109/JSTARS.2025.3602497","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3602497","url":null,"abstract":"To address the critical challenges in synthetic aperture radar (SAR) ship target detection, including complex background speckle noise interference and the difficulty in balancing model lightweight design with detection accuracy, this article proposes an innovative PPDM-YOLO model. Through modular architecture design, we establish a four-part technical framework: First, a lightweight feature extraction module named PCA is developed to reduce computational complexity by analyzing feature map redundancy, effectively mitigating feature degradation caused by noise. Second, the noise-resistant enhancement module, PSA-G, integrates the multiscale adaptive gradient threshold module with a dynamic spatial attention mechanism. This integration enhances target feature representation while effectively suppressing noise interference. Third, DySample technology is employed in place of conventional upsampling methods to improve the quality of feature reconstruction and preserve spatial details. In addition, a multiscale fusion small target detection network is introduced to boost small object detection through cross-layer feature interaction. Experimental results on HRSID and SSDD datasets demonstrate that PPDM-YOLO achieves 93.7% mAP50 and 70.3% mAP50–95 on HRSID, while reaching 99.4% mAP50 and 78.7% mAP50–95 on SSDD, showing significant advantages over mainstream detection models. With 34.7% fewer parameters than YOLOv11n, our model achieves optimal balance among noise suppression, model lightweighting, and detection accuracy. This research provides an efficient and reliable technical solution for real-time SAR ship detection in complex marine environments.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"22690-22705"},"PeriodicalIF":5.3,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11141020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089979","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":"Framework for Organizing Viewsheds From Multiple Observation Points: Enabling Bidirectional Landscape Visibility Information Queries","authors":"Runzi Chen;Renjie Li;Pengcheng Deng;Baolei Sun","doi":"10.1109/JSTARS.2025.3602482","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3602482","url":null,"abstract":"Viewshed analysis holds significant value in landscape assessment and management. However, current research faces dual limitations: inefficient organization of viewsheds from multiple observation points and inability to analyze bidirectional visibility relationships between multiple observation points and ground cells. This study develops a 3-D tensor (observation point–row–column) that integrates detailed visibility relationships between landscape units and ground cells and proposes a framework for organizing viewsheds from multiple observation points that supports bidirectional queries. The framework implements a three-stage optimization strategy: 1) decimal encoding of observation point dimension data, 2) a mapping mechanism from ground cells to observation point sets, and 3) run-length encoding-based organization of mapping data. Experimental results demonstrate that when using ASCII storage format, the framework achieves a 98% storage space reduction compared to conventional viewshed matrix methods while maintaining comparable query efficiency, with storage requirements similar to those of NetCDF format. Moreover, with 7-Zip compression, the framework achieves further space savings: a 40% reduction relative to viewshed matrix methods and a 70% reduction compared to NetCDF format. This research not only enriches the theory of multiple-observation-point viewshed analysis but also provides technical reference for geographic information product development, offering important application value for landscape conservation and planning.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"22392-22402"},"PeriodicalIF":5.3,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11139115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090180","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":"Simulation and Validation of Real-Time UAV-Based GNSS-R Altimetry Across Diverse Landforms","authors":"Ziyin Xu;Xianyi Wang;Junming Xia;Zhenhe Zhai;Zhuoyan Wang;Cheng Liu;Yusen Tian;Tongsheng Qiu;Yueqiang Sun;Qifei Du;Weihua Bai;Feixiong Huang;Cong Yin","doi":"10.1109/JSTARS.2025.3602102","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3602102","url":null,"abstract":"Accurate measurement of land surface elevation is essential for applications, such as terrain mapping, environmental monitoring, and ecological assessment. However, conventional spaceborne and ground-based techniques face challenges in achieving both high accuracy and real-time performance. To address these limitations, this work proposes a BeiDou-based UAV global navigation satellite system reflectometry altimetry system capable of delivering real-time elevation estimates at a 5 Hz sampling rate. The system integrates a compact, low-power receiver with a real-time correction framework, thereby eliminating the need for postprocessing. Performance was evaluated through three experiments involving variable flight altitudes (10–110 m), low signal-to-noise ratio (SNR) conditions, and complex terrain scenarios (1300–1550 m). The system achieved an accuracy of 1.5 m under both low-altitude and low-SNR conditions, and approximately 1.854 m in complex terrain scenarios. These consistent results across diverse conditions indicate the system’s robustness and generalizability. To support realistic signal modeling, a 12.5 m resolution ALOS digital elevation model and the GSS-9000 simulator were employed. In addition, terrain undulation, vegetation characteristics, and surface roughness were incorporated into the modeling process. The results demonstrate that the system has strong potential for practical use in terrain mapping and disaster monitoring applications.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"22151-22164"},"PeriodicalIF":5.3,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134769","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061873","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":"Super-Resolution Reconstruction of SMOS Sea Surface Salinity from Multivariate Satellite Observations Based on Deep Learning","authors":"Zhenyu Liang;Senliang Bao;Weimin Zhang;Hengqian Yan;Boheng Duan;Huizan Wang","doi":"10.1109/JSTARS.2025.3602684","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3602684","url":null,"abstract":"Satellite sea surface salinity (SSS) observations play a critical role in the study of ocean circulation and climate regulation. However, mesoscale salinity dynamics (e.g., eddies, fronts) remain poorly resolved by current salinity satellites, such as soil moisture and ocean salinity (SMOS), due to their low effective resolution (>100 km). To address this, we proposed the SMOS SSS super-resolution reconstruction (S5R2) network. This deep learning framework achieved super-resolution (SR) reconstruction of the SMOS L3 SSS product from 1/4° to 1/12° by fusing multivariate satellite observations. Our approach integrated a land filtering mechanism into a hybrid transformer-CNN architecture, enhancing both global and local attention to ocean dynamics while suppressing interference from land-based information. Meanwhile, we improved the search efficiency of the optimal subset of input variables by guiding the search direction and step size using prior knowledge. The results demonstrated that S5R2 outperformed existing L3 and L4 satellite SSS products and mainstream SR algorithms. Compared to the input SMOS L3 SSS product, S5R2 achieved a 20% and 60% reduction in root mean square error in the Kuroshio Extension and Gulf Stream regions, respectively. In addition, it improved the effective resolution from 100 km to 20–30 km, enabling the dynamic tracking of mesoscale eddies. This advance provides a near-real-time solution for monitoring fine-scale ocean salinity processes, with practical implications for ocean dynamics research and the operational application of salinity satellite products.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"24251-24266"},"PeriodicalIF":5.3,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11141659","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141674","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":"Simulation of Long Time Series Spatial Distribution of PM2.5 in Beijing-Tianjin-Hebei Region Based on an Improved Machine Learning Method","authors":"Zheyuan Zhang;Huayang Song;Guang Tian;Hongyu Zhang;Jia Wang;Nina Xiong","doi":"10.1109/JSTARS.2025.3602240","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3602240","url":null,"abstract":"The Beijing-Tianjin-Hebei (BTH) region has long been facing serious fine particulate matter (PM2.5) pollution issues due to its geographical characteristics and industrial structure. In this study, we innovatively integrated STL-derived seasonal-trend parameters to replace the conventional time variables as inputs to XGBoost, combined with Bayesian optimization and hyperband (BOHB) for hyperparameter tuning. This integrated STL-XGBoost-BOHB framework significantly addressed the bottleneck of missing early monitoring data in long-term PM2.5 inversion. Through the STL time series decomposition method, seasonal trend parameters reflecting the variation of PM2.5 in the BTH region were obtained. These parameters were used as substitutes for time data, addressed the limitations of ground-based PM2.5 monitoring and overcoming the limitation of the lack of early PM2.5 monitoring data in China. The BOHB algorithm was chosen to comparison. The STL-XGBoost-BOHB model has a coefficient of determination (<italic>R</i>²) reaching 0.78 and root mean square error of 15.8 <italic>μ</i>g/m³, demonstrating outstanding performance in PM2.5 retrieval. Model results revealed a distinct spatial distribution of PM2.5, with concentrations decreasing from southeast to northwest. In terms of the temporal variation of PM2.5 concentration, there was a significant decrease in PM2.5 concentration in the BTH region from 2011 to 2020. However, combined with the PM2.5 pollution exposure study based on population data, it was found that the majority of the population in the region mainly concentrated in areas with higher PM2.5 concentrations, and the population-weighted PM2.5 concentration was significantly higher than the original PM2.5 concentration values without population weighting. This highlights the need for more targeted pollution control in densely populated areas.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"21985-21996"},"PeriodicalIF":5.3,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11141525","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061803","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 Monitoring of Oxygen-Consuming Organic Pollutants in the Pearl River Delta Estuaries Based on Sentinel-2 MSI Observations and Physics-Based Approach","authors":"Yiwei Ma;Li Zhuo;Ruru Deng;Junying Yang","doi":"10.1109/JSTARS.2025.3602464","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3602464","url":null,"abstract":"Estuarine ecosystems, influenced by anthropogenic activities and natural processes, require advanced monitoring for complex organic pollutions. The permanganate index (chemical oxygen demand using potassium permanganate (KMnO₄) as oxidant, COD_Mn) is a principal parameter in China’s environmental monitoring system, yet its application in estuarine environments remains challenging. Existing monitoring approaches are predominantly empirical and designed for inland water bodies and large-scale coastal regions, leaving estuarine environments characterized by complex hydrodynamic interactions and sediment dynamics insufficiently addressed. These approaches face significant limitations due to sampling data scarcity and poor representativeness under variable geomorphologic conditions. This study developed a physics-based inversion model, using Sentinel-2 multispectral data, to overcome these challenges. The model achieved robust performance (R² = 0.7838, mean absolute percentage difference (MAPD) = 13.9%, root-mean-square deviation (RMSD) = 0.3791 mg/L, mean normalized difference (MND) = −5.99%), and outperformed the comparison models. Spatiotemporal analysis across four major Pearl River estuaries first revealed a sharp decline in COD_Mn concentration from the near-mouth section toward the river-mouth section, with pronounced accumulation identified at the boundary between the river-mouth section and the mouth-outside seashore. Four driving factors (i.e., increased terrestrial organic matter inputs, seasonal variations in rainfall, inadequate urban wastewater infrastructure management, and estuarine engineering modifications) regulate the transport pathways and residence time of pollutants. This study provides new insights for the dynamic monitoring of organic pollution, supporting adaptive management strategies in transitional ecosystems, particularly for pollution source identification, remediation effectiveness assessment, and ecological restoration planning in anthropogenically stressed estuaries.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"22938-22950"},"PeriodicalIF":5.3,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11139124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090181","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 Extraction of 3-D Ground Control Points Using Stereo SAR from China’s GF3B and SVN2-01/02","authors":"Minzheng Mu;Zhiwei Li;Xin He;Qijin Han;Xiaoyu Shi;Ya Zhang;Yunce Su;Pengfei Li;Yan Zhu","doi":"10.1109/JSTARS.2025.3602096","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3602096","url":null,"abstract":"The extraction of ground control points (GCPs) from spaceborne synthetic aperture radar (SAR) is an effective technique for rapid, contactless, and globally available georeferencing. In recent years, China has made significant progress in the deployment and application of SAR satellites. However, the automatic derivation of high-precision GCPs remains challenging, primarily due to two key factors: 1) the need for high-accuracy calibration of SAR geometric observations and 2) the automatic detection and matching of candidate GCPs (CGCPs) across SAR images acquired from different look angles. This article proposed an improved algorithm framework for GCP extraction and demonstrated the application potential of China’s SAR satellites. The main improvements are as follows. 1) Beam-dependent biases are incorporated into the geometric calibration process to achieve higher ranging accuracy. 2) A CGCP detector is designed that leverages shape characteristics to robustly identify CGCPs without relying on any external data. 3) An integrated matching scheme is proposed, which combines preprocessing, feature description, and transformation-search strategies to enable effective pairing of CGCPs. SAR imagery from China’s Gaofen-3B and Superview Neo-2 01/02 satellites is utilized, and geometric calibration is performed using the open-access corner reflector array in Australia. GCP extraction experiments are conducted in the Suzhou area, with 7477 GCPs automatically extracted and a root mean square error better than 1 m.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"22029-22046"},"PeriodicalIF":5.3,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134818","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061861","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":"Using Monoscopic Multispectral Earth Observation Images to Predict Terrain Features With Deep Neural Networks","authors":"Kristoffer Langstad;Eleonora Jonasova Parelius;Stian Løvold;David Völgyes","doi":"10.1109/JSTARS.2025.3602630","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3602630","url":null,"abstract":"In the field of remote sensing and Earth observation, deep neural networks (DNNs) have established themselves as important tools for many different image analysis applications. Estimation of terrain features from optical satellite imagery is a rarely studied application for which DNNs are well suited because of their ability to extract and combine information at various scales. To predict terrain slopes in optical images, we propose an R2U-Net using global multispectral Sentinel-2 (S2) L2A images as input, and ALOS World 3-D DSM elevation data as target data. The R2U-Net takes advantage of a residual unit that benefits deep architecture training, and the recurrent residual convolutional layers provide better feature accumulation. Two models were experimented with; one model trained on only the optical RGB bands and one model trained on all S2 L2A bands. Evaluation of the multispectral- and RGB-trained models showed that the multispectral-trained model performs better than the RGB model, both during training and when evaluated on the test data. The multispectral model performs better overall than the RGB model in all the cases studied. Slope errors typically increase from low-gradient to high-gradient terrain, but not at the same rate as the slope steepness itself, while aspect errors decrease as the models struggle more to predict the slope aspect in low-gradient terrain. This highlights that, in this case, using more spectral bands when predicting terrain slopes helps improve the model predictions. The results have also been shown to depend on the incoming angle of the sunlight, which is mostly due to the topographic shadows that are being cast onto the terrain.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"21954-21966"},"PeriodicalIF":5.3,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11141022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061916","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":"SAR Aircraft Segmentation With SAR-to-Optical Image Translation and Segment Anything Model","authors":"Ruixi You;Feng Xu;Min Liu","doi":"10.1109/JSTARS.2025.3602288","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3602288","url":null,"abstract":"Instance segmentation in synthetic aperture radar (SAR) imagery has demonstrated notable success for certain target types such as vehicles and ships. However, accurate segmentation of aircraft in SAR images remains a significant challenge due to complex structural geometries, low-intensity and sparse backscattering, and frequent occurrences of incomplete or ambiguous contours. These inherent limitations hinder the generation of high-quality annotations and restrict downstream applications to coarse object detection tasks. To address these issues, this work proposes a two-stage framework combining SAR-to-optical image translation with an adapter-tuned segment anything model (SAM). In the first stage, a diffusion-based generative model first translates SAR aircraft slices into high-fidelity optical counterparts, enhancing structural visibility with continuous and interpretable contours. In the second stage, the SAM-adapter-based module produces instance-level and component-level masks on the translated images. In addition, to further improve alignment with SAR-specific characteristics, a scattering-aware refinement module refines the masks using the physical scattering distributions of the original SAR images. Experimental results demonstrate the effectiveness of the proposed framework in fine-grained segmentation and validate its strong zero-shot generalization ability, indicating its potential for scalable, automated mask annotation of aircraft in SAR imagery.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"22082-22093"},"PeriodicalIF":5.3,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134789","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061841","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}