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

{"title":"HoloMine: A Synthetic Dataset for Buried Landmines Recognition Using Microwave Holographic Imaging","authors":"Emanuele Vivoli;Lorenzo Capineri;Marco Bertini","doi":"10.1109/JSTARS.2025.3555442","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3555442","url":null,"abstract":"Detection and clearance of landmines is a complex and risky activity that requires advanced remote sensing techniques to reduce the risk to operators in the field. In this article, we propose a novel synthetic dataset for buried landmine detection to provide researchers with a valuable resource to observe, measure, locate, and address issues in landmine detection. The dataset consists of 41 800 microwave holographic images (2-D) and their holographic inverted scans (3-D) of different types of buried objects, including landmines, clutter, and pottery objects, and is collected by means of a microwave holography radar. We evaluate the performance of several state-of-the-art deep learning models trained on our synthetic dataset for various classification tasks. While the results do not yield yet high performances, showing the difficulty of the proposed task, we believe that our dataset has significant potential to drive progress in the field of landmine detection; thanks to the accuracy and resolution obtainable using the holographic radars. To the best of the authors' knowledge, the dataset is the first of its kind and will help drive further research on computer vision methods to automatize mine detection, with the overall goal of reducing the risks and the costs of the demining process.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9883-9892"},"PeriodicalIF":4.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10944297","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850911","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":"Improving Disparity Consistency With Self-Refined Cost Volumes for Deep Learning-Based Satellite Stereo Matching","authors":"Jiyong Kim;Seoyeon Cho;Minkyung Chung;Yongil Kim","doi":"10.1109/JSTARS.2025.3555424","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3555424","url":null,"abstract":"Stereo matching algorithms are considered one of the most important subtasks in 3-D reconstruction, as 3-D coordinates are derived from the disparity values of pixels obtained through stereo matching. Recently, deep learning-based satellite stereo matching algorithms have been widely investigated, as they can capture both deep and shallow features of complex satellite scenes. However, several problems in satellite stereo matching, due to the unique properties of satellite images, remain unsolved, particularly in textureless and repetitive regions. In these regions, a single object in a satellite image is likely to be matched with similar objects, causing multiple disparity probabilities and shifts in the disparity estimation. To address the problem of disparity shifts, we propose a novel cost volume refinement strategy (CVRS). CVRS introduces both left-right and left-left cost volumes, which work together to refine disparities and eliminate false matches in textureless or repetitive regions, while preserving the original disparity values. With CVRS, we propose a new model for satellite stereo matching, the self-refined cost volume network (SRCV-Net). We evaluated CVRS and SRCV-Net on the US3D and WHU-Stereo datasets, comparing it using the EPE and D1 metrics. The application of CVRS demonstrated performance improvements in all models, and SRCV-Net achieved superior accuracy in satellite stereo matching. Furthermore, CVRS can be easily applied to various models with minimal structural changes and a small increase in parameters. SRCV-Net, with its innovative CVRS, provides an effective solution to the challenges of satellite stereo matching, offering enhanced accuracy, efficiency, and adaptability.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9262-9278"},"PeriodicalIF":4.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10943281","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143818032","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":"SRFNet: Multimodal Based Selective Receptive Field Neural Network for Time Series Forecast of Flood Range","authors":"Zhiqing Li;Zeqiang Chen;Lai Chen;Xu Tang;Nengcheng Chen","doi":"10.1109/JSTARS.2025.3555400","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3555400","url":null,"abstract":"Flood disaster is a typical natural disaster that causes human casualties and property losses every year. Benefiting from powerful feature abstraction capabilities and automatic tuning characteristics, deep learning has become a powerful tool for disaster prediction. Nonetheless, many existing methods are developed for natural images and do not take into account the unique characteristics of remote-sensing images and other modal data. Furthermore, many methods are too complex to poor computational efficiency and interpretability. To this end, we proposed a multimodal based selective receptive field neural network (SRFNet). It fully adopts convolutional neural networks, which are simpler and more efficient compared to other state-of-the-art methods. It also incorporates selectively large kernel convolution for multiscale analysis of remote sensing images. In addition, the modal of rainfall and water level are also fully considered and exploited in the method to improve its performance. To verify the effectiveness and robustness of SRFNet, extensive and detailed experiments on the Dongting Lake and the Poyang Lake with the data range from year of 2010 to 2020 are conducted. As a result, our method outperforms the other seven state-of-the-art methods and can stably achieve structural similarity of more than 0.9 in multiple resolutions.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9340-9350"},"PeriodicalIF":4.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10943213","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817993","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":"Deep-Learning-Based Approach in Imaging Radiometry by Aperture Synthesis: Application to Real SMOS Data","authors":"Ali Khazâal;Richard Faucheron;Nemesio J. Rodríguez-Fernández;Eric Anterrieu","doi":"10.1109/JSTARS.2025.3555299","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3555299","url":null,"abstract":"A novel image reconstruction algorithm for aperture synthesis measurements using deep learning techniques was introduced recently. This algorithm is specifically designed to retrieve brightness temperature (BT) from interferometric data, similar to those collected by the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, launched in 2009. The algorithm employs a deep neural network (DNN) architecture that features a fully connected layer followed by a contracting and expansive path, enabling the network to effectively learn the relationship between simulated visibilities and BT maps. Validation with simulated data has confirmed that this approach aligns perfectly with the theoretical framework of the Van-Cittert Zernike theorem. In this study, a new DNN architecture better suited for real SMOS data is proposed. The new architecture integrates a priori information regarding the water content of each observed pixel. It also includes further enhancements to the previous DNN architecture to better accommodate real SMOS data by incorporating the effects of radiometric noise and the Faraday rotation angle, as well as selecting appropriate global BT maps for training. Finally, validation of the proposed DNN approach using large datasets of real SMOS data is presented and compared to the traditional algebraic approach. Globally, the results demonstrate a significant improvement in image quality, with a reduction in reconstruction error, better handling of residual foreign sources, such as radio frequency interference and direct solar radiation, and a notable reduction in land-sea and sea-ice contamination. Overall, the results suggest that the DNN-based approach provides substantial improvements over traditional methods, making it a promising technique for processing SMOS data.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9321-9332"},"PeriodicalIF":4.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10943169","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817869","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":"Multisensor Remote Sensing and AI-Driven Analysis for Coastal and Urban Resilience Classification","authors":"Sumei Ren;Bushra Ghaffar;Muhammad Mubbin;Muhammad Haseeb;Zainab Tahir;Sher Shah Hassan;Dmitry E. Kucher;Olga D. Kucher;M. Abdullah-Al-Wadud","doi":"10.1109/JSTARS.2025.3554793","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3554793","url":null,"abstract":"Urban resilience is essential for cities to endure and adjust to environmental and socioeconomic upheavals. The static indicators and rule-based spatial frameworks that are the mainstays of traditional resilience assessment models frequently fall short of capturing the dynamic character of coastal and urban resilience. This article suggests a deep learning-based categorization framework for identifying resilience levels in urban and coastal settings by combining long short-term memory (LSTM) networks with multisensor remote sensing data. The Copernicus Marine Data Service's spatiotemporal ocean physics data, namely the eastward (uo) and northward (vo) seawater velocity, are used in the model to increase the precision of resilience evaluations. The methodology includes a multistep deep learning pipeline, incorporating data preprocessing, feature extraction, class balancing with SMOTE, and LSTM-based classification. The proposed LSTM model is optimized to enhance performance with dropout regularization (0.3), an Adam optimizer (learning rate = 0.0003), and class weighting strategies. The model is evaluated using accuracy, F1-score, confusion matrices, and loss curves, ensuring reliable classification across different resilience categories. Results indicate that the framework achieves high classification accuracy (91.5%), demonstrating superior performance compared to traditional machine learning approaches. Regarding multisensor fusion and deep learning, this study provides a scalable, adaptive, and data-driven solution for resilience classification, supporting climate adaptation strategies, disaster risk management, and sustainable urban development. The proposed methodology offers a robust tool for policymakers and urban planners, enabling more effective resilience monitoring and decision-making in rapidly evolving urban and coastal environments.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9166-9180"},"PeriodicalIF":4.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938888","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835428","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":"Integrating Random Forest With Boundary Enhancement for Mapping Crop Planting Structure at the Parcel Level From Remote Sensing Images","authors":"Junyang Xie;Yan Li;Hao Wu;Ziwei Wu;Ruina Zhao;Anqi Lin;Marcos Adami;Guoqiang Li;Jian Zhang","doi":"10.1109/JSTARS.2025.3554922","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3554922","url":null,"abstract":"Accurately and efficiently obtaining crop planting structure information is critical for precision agriculture. However, the current methods for mapping crop planting structure primarily use image pixels as the classification units, easily leading to blurred and fragmented boundaries and the salt-and-pepper effect, which significantly limit the accuracy and reliability of the results. To address this challenge, we propose a novel framework for mapping crop planting structure, consisting of three key components: 1) farmland parcel extraction; 2) crop classification feature extraction; and 3) crop classification. First, a boundary-enhanced deep-learning model is introduced for farmland parcel extraction (FPENet) from Gaofen-2 data, based on the U-Net model, to accurately obtain farmland parcel data. Subsequently, crop classification features are extracted at the parcel level from both Sentinel-2 and Landsat 8 data. After selecting the optimal feature combination, crop classification is performed using the random forest model to map precise crop planting structure. The proposed framework was evaluated in Dangyang County, Hubei province, China, where it showed a superior performance in mapping crop planting structure. The FPENet model achieved an overall accuracy and <italic>F</i>1-score exceeding 92.5%, enabling complete and accurate extraction of farmland parcels. Comparative experiments with different convolutional neural networks further highlighted FPENet's exceptional capability. Furthermore, with the optimal feature combination, the classification accuracy for rice, corn, and wheat exceeded 94.5%, with spectral bands and vegetation indices being the key contributors to crop classification. In addition, comparisons with other methods further validated the effectiveness of this framework in mapping crop planting structure.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9934-9953"},"PeriodicalIF":4.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938894","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850913","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":"Adaptive Early Wildfire Monitoring Based on Spatiotemporal Prediction and Himawari 8/9","authors":"Zekun Xu;Zhaoming Zhang;Guojin He;Shuaizhang Zhang;Tengfei Long;Guizhou Wang","doi":"10.1109/JSTARS.2025.3554892","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3554892","url":null,"abstract":"The rapid advancement of deep learning (DL) technology significantly enhances early forest fire detection methods. However, traditional approaches often rely on fixed thresholds and supervised learning techniques, which may fail to account for the complex spatiotemporal dynamics associated with forest fire events. To overcome this limitation, an adaptive DL model is proposed and specifically designed for early forest fire monitoring. This model integrates Stacking ConvLSTM to forecast mid-infrared brightness temperatures and employs a nonparametric dynamic thresholding method based on Otsu's algorithm for spatiotemporal anomaly detection, facilitating the identification of potential hotspots. By effectively capturing complex dependencies within spatiotemporal dimensions, this method improves detection accuracy. Furthermore, high-confidence early fire points are determined through dual-band analysis and land cover detection. Comparative experiments utilizing Himawari-8/9 satellite data reveal that the proposed method outperforms traditional techniques as well as the latest temporal methods, achieving an accuracy of 0.995, precision of 0.985, recall of 0.946, and an F1 score of 0.965. In addition, our method demonstrates an average fire detection delay of just 7 min and an average runtime of 71 s, underscoring its effectiveness in early forest fire detection. This approach serves as a robust tool for enhancing forest fire monitoring systems, offering significant implications for reducing response times and mitigating fire-related damages.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9396-9408"},"PeriodicalIF":4.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938890","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835455","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":"CMCD: A Consistency Model-Based Change Detection Method for Remote Sensing Images","authors":"Xiongjie Li;Weiying Xie;Jiaqing Zhang;Yunsong Li","doi":"10.1109/JSTARS.2025.3554659","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3554659","url":null,"abstract":"Change detection is a key research area in remote sensing, focusing on identifying differences between images captured at different time points and generating change maps. While denoising diffusion probabilistic models have shown preliminary success in this area, the quality of the generated change maps remains unsatisfactory. Furthermore, these methods utilize diffusion networks to extract key features from dual-temporal remote images and generate change maps, yet they often overlook the model's parameter size and the time cost associated with iterative sampling. To address these challenges, we propose a novel consistency model-based change detection method (CMCD), which directly generates high-quality change detection maps in one or a few steps. Specifically, we employ dynamic time interval to prioritize the modeling of challenging image data distributions, enhancing the perception of dual-temporal remote sensing images. Then, we introduce a novel joint loss function to prevent the training collapse of the consistency model caused by errors accumulated from exponential moving average updates. In addition, we propose a new strategy for noise injection that concatenates with one remote sensing image rather than two, thereby reducing noise interference with feature information. We also develop a pruning strategy of skip connections and a top–down feature aggregation module to improve feature utilization efficiency. Extensive experiments demonstrate that CMCD significantly reduces computational complexity and inference time compared to existing diffusion model-based methods. Through extensive experiments on the LEVIR, WHU-CD, and SYSU datasets, our method achieved competitive results, with F1 scores of 91.60%, 92.66%, and 82.26%, respectively.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9009-9022"},"PeriodicalIF":4.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938396","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817902","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":"InSAR-Based Surface Deformation Analysis and Trend Prediction in Permafrost Areas Along the Qinghai-Tibet Railway Using Sentinel-1A and Environmental Factors","authors":"Tianbao Huo;Yi He;Yaoxiang Liu;Wang Yang;Lifeng Zhang;Hesheng Chen;Yuming Fang;Binghai Gao;Xiyin Zhang","doi":"10.1109/JSTARS.2025.3554388","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3554388","url":null,"abstract":"Global warming is accelerating the permafrost degradation along the Qinghai-Tibet railway (QTR), causing the surface deformation (SD) of the railway subgrade. Especially in the Salt Lake to Wuli section of the QTR, the permafrost is widely distributed, and the SD has been the most serious. However, the spatiotemporal characteristics and mechanism of SD are still unclear. In addition, it is very important to predict the future trend of SD. Therefore, we acquired time series SD results from 2019 to 2022 based on small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) and analyzed the spatiotemporal characteristics and mechanism of SD in the Salt Lake to Wuli section. Subsequently, the EnvCA-GRU model for SD prediction was developed, integrating the multihead cross-attention mechanism and gated recurrent unit (GRU) to account for changes in environmental factors. The model was then employed to forecast SD trends over the next two years. Our results showed that the SD was uneven in the Salt Lake to Wuli section of the QTR from 2019 to 2022, there were six typical deformation areas, and the maximum cumulative ground subsidence reached 126.79 mm. The SD velocity of the sunny slope was higher than that of the shady slope, and the closer to the QTR, the greater the ground subsidence. Land surface temperature (LST), normalized difference vegetation index (NDVI), and precipitation are the main factors affecting SD. Our proposed EnvCA-GRU prediction model fusing NDVI, LST, and precipitation showed a root mean square error of 0.153 and an <italic>R</i>² of 0.991, the proposed model was reliable. The maximum cumulative ground subsidence of six typical areas by July 2024 reached 177.52, 268.08, 287.73, 270.99, 190.70, and 211.89 mm, respectively. The results of this study can play a guiding role in the early warning and mitigation of ground subsidence disasters along the QTR.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9297-9320"},"PeriodicalIF":4.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938223","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817986","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":"Iterative PolInSAR Target Decomposition for Scattering Characterization and Building Detection","authors":"Di Zhuang;Lamei Zhang;Bin Zou","doi":"10.1109/JSTARS.2025.3554992","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3554992","url":null,"abstract":"In densely rotated built-up areas, restricted by interactive and complex scatterings, most polarimetric synthetic aperture radar scattering analyses and unsupervised building detection algorithms have failed, especially under conditions of large radar look angles. To handle this problem, an iterative polarimetric interferometric synthetic aperture radar (PolInSAR) target decomposition method for scattering characterization and building detection is proposed in this article, and it consists of three key components. Specifically, by analyzing basic scatterers and electromagnetic wave propagation, the coherent volume scattering is assigned to densely rotated built-up areas. Based on it, a five-component PolInSAR target decomposition method is proposed for unambiguous scattering characterization, where repeat-pass PolInSAR coherence is introduced to aid in unambiguous interpretation by dividing natural areas, nondensely rotated built-up areas, and densely rotated built-up areas. Moreover, to overcome the failure of simple segmentation and deeply explore the scattering differences between densely rotated buildings and forests, an iterative framework integrating self-organizing map (SOM) and PolInSAR target decomposition is finally proposed. SOM uses PolInSAR target decomposition results to refine the segmentation across the three areas, feeding back refined outcomes to the target decomposition module iteratively. This process will ultimately enhance features and improve building detection accuracy. Experiments on three sets of PolInSAR data confirm the validity of the proposed framework, with more reasonable target decomposition results and more accurate building detection results, especially in densely rotated built-up areas.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9211-9229"},"PeriodicalIF":4.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938847","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143818030","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}