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

{"title":"A Triple Dynamic Optimization-Based Multiscale Object Detection Network","authors":"Xin Cheng;Haisu Zhang;Sheng Zhang;Leiyang Chen;Yibo Liu;Ning Xu","doi":"10.1109/JSTARS.2025.3610623","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3610623","url":null,"abstract":"The object detection in high-resolution remote sensing images is affected by the uniqueness of imaging perspectives and the complexity of detection targets. Existing models face core challenges such as feature degradation of small objects, extreme scale variations, and intricate, dynamic backgrounds. To address these issues, this article proposes a triple dynamic optimization framework for remote sensing target detection (TDO-YOLO). First, construct the deformable reparameterized feature module, which utilizes multibranch feature fusion and structural reparameterization techniques to achieve refined capture of tiny and multiscale targets. Second, we design an adaptive receptive field fusion (ARFF) module that overcomes fixed receptive field limitations in traditional convolutions through parallel multiscale kernel extraction and geometry-aware feature reorganization, significantly improving multiscale object detection accuracy. Third, we propose a convolutional dynamic position-encoding residual attention mechanism, leveraging long-range dependency modeling and background suppression to effectively mitigate complex background interference and reduce false detection rates. Finally, we introduce a multiscale-aware IoU loss function specifically designed for remote sensing images, incorporating multiscale perception mechanisms and dynamic weighting to enhance detection precision. Experimental results on the RSOD and DIOR datasets demonstrate that TDO-YOLO achieves mAP<inline-formula><tex-math>$_{50}$</tex-math></inline-formula> scores of 96.6% and 68.7%, representing improvements of 3.87% and 3.15%, respectively, over the baseline model YOLOv11. Compared to current state-of-the-art networks, TDO-YOLO exhibits superior detection accuracy.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"25110-25123"},"PeriodicalIF":5.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11165177","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255872","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":"EESAGAN: Edge-Enhanced and Structure-Aware GAN for Remote Sensing Image Super-Resolution","authors":"Yin Zhang;Songping Wei;Yu Sun;Jiaqi Shen;Zhen Yang;Junhua Yan","doi":"10.1109/JSTARS.2025.3610709","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3610709","url":null,"abstract":"Remote sensing (RS) images often suffer from spatial resolution degradation and edge blurring due to sensor limitations and complex degradation factors such as atmospheric turbulence and motion. These issues negatively impact subsequent tasks like classification and detection. Most existing super-resolution (SR) methods rely on synthetic low-resolution (LR) data generated by bicubic interpolation, which fails to reflect real degradation processes, resulting in limited performance in practical applications. Furthermore, many methods insufficiently exploit high-frequency details and lack the ability to effectively model both global context and local structures. To address these challenges, this article proposes an edge-enhanced SR framework for RS images. A more realistic degradation model is constructed by estimating blur kernels from real LR images. The edge-enhanced and structure-aware generative adversarial network is designed, incorporating the edge priorenhancement module, the edge-aware local attention module, and the dynamic feature space attention module. The edge-guided loss is also introduced to enhance image sharpness and perceptual consistency. Experimental results demonstrate that the proposed method achieves superior performance in PSNR, SSIM, and ERGAS compared to existing SR approaches.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"24947-24962"},"PeriodicalIF":5.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11165376","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255821","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":"Fusing Ice Surface Temperature With the AI4Arctic Dataset for Improved Deep Learning-Based Sea Ice Mapping","authors":"Lily de Loë;David A. Clausi;K. Andrea Scott","doi":"10.1109/JSTARS.2025.3610260","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3610260","url":null,"abstract":"Arctic sea ice mapping is vital for supporting marine navigation, climate monitoring, and efforts by northern communities to adapt to variable ice conditions. Automated mapping approaches can leverage freely accessible satellite data to supplement navigational ice charts, improve operational forecasting, and produce high-resolution sea ice parameter estimates. The AI4Arctic dataset enables deep learning-based mapping using synthetic aperture radar (SAR), passive microwave (PM), and reanalysis data. However, SAR and PM can struggle to resolve ice features due to ambiguous textures, atmospheric effects, and sensor limitations. To provide complementary data, an 84-scene Visible Infrared Imager Radiometer Suite (VIIRS) dataset is co-registered with AI4Arctic to evaluate whether ice surface temperature (IST) measurements can improve estimation of sea ice concentration (SIC), stage of development, and floe size. Input- and feature-level fusion methods, based on the U-Net architecture, are explored. Models are evaluated using the SIC <italic>R</i>² coefficient and SOD/FLOE F1-score, as well as predicted sea ice maps. In addition, an alternative SIC accuracy score is introduced to assist with evaluating marginal ice predictions. Incorporating IST improves performance across all models compared to the AI4Arctic baseline; this includes single-encoder, dual-encoder, and multidecoder U-Nets. Results highlight significant improvements in the prediction of open water under conditions with low-incidence angle, high atmospheric moisture, and wind roughening. Overall, the best performing dual-encoder model, DUE-Net-V, improves predictions by 2.18–5.01% across all metrics, relative to the baseline. These results support integrating IST in deep learning workflows and highlight the potential for next-generation thermal-infrared sensors to improve automated sea ice mapping.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"24766-24782"},"PeriodicalIF":5.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11165056","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210131","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":"Domain-Invariant Progressive Enhancement for Cross-Scene Classification of Hyperspectral Images","authors":"Cuiping Shi;Zhipeng Zhong;Weiwei Sun;Liguo Wang","doi":"10.1109/JSTARS.2025.3610094","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3610094","url":null,"abstract":"Cross-scene classification is a significant direction in hyperspectral image (HSI) processing, and the differences in distribution of the source and target domains render this task extremely formidable. Domain adaptive methods require access to target data when aligning distributions between domains, while domain generalization methods allow models trained on the source domain to be immediately utilized in unknown domains, with a wider range of applicability. Currently, most domain generalization methods mainly focus on generating simulated samples related to the source domain and using some strategies, such as adversarial learning, to obtain domain-invariant features. Nevertheless, these methods fail to clearly distinguish domain-invariant features from domain-specific features, which makes the model prone to fitting specific features and makes it difficult to learn invariant features effectively. Inspired by disentangled representation learning, we propose a novel domain-invariant progressive enhancement network for cross-scene classification of HSI. First, a domain invariant progressive enhancement module is proposed, which is based on a frequency domain disentanglement layer to gradually separate domain invariant features from shallow to deep layers. Then, a re-entanglement module is carefully designed for deep fusion of original features and domain invariant features, thereby improving generalization ability. Finally, a frequency-aware feature aggregation module is constructed for more accurately extracting features that are conducive to classification. Some experiments carried out on three public datasets indicate that the proposed method outperforms some state-of-the-art approaches with a large margin.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"24701-24715"},"PeriodicalIF":5.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11164787","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210069","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":"Observation Experiments and Analysis of Summer Urban Heat Island in Cold Region Based on Low-Altitude Dual-Source Remote Sensing: A Case Study of Zhengzhou University Main Campus","authors":"Xu Yuan;Yalun Zhang;Sihan Xue;Jialiang Han;Xiao Liu;Zhi Lv","doi":"10.1109/JSTARS.2025.3610219","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3610219","url":null,"abstract":"Rapid urbanization worldwide has exacerbated the urban heat island (UHI) effect. Although studies on UHI effect have already become a hotspot, high-precision UHI investigations in cold regions utilizing remote sensing technologies are still underexplored. To enrich this type of research, we conducted observation experiments and analysis of UHI in cold region during summer based on low-altitude dual-source remote sensing. In this study, a low-altitude observation experiment scheme was designed based on a self-developed low-altitude dual-source remote sensing platform (LADSRSP). According to this scheme, the observation experiments were conducted at the main campus of Zhengzhou University, to synchronously collect dual-source remote sensing data, retrieve land surface temperature and classify underlying surface. The surface urban heat island intensity (SUHII) and the heat field intensity index were calculated to quantitatively characterize the UHI characteristics based on different urban underlying surfaces. The results indicate that the fusion analysis of high-precision thermal infrared and hyperspectral data has successfully constructed the relationship between underlying surface and its temperature characteristics, and has realized the calculation of SUHII with high-precision resolution based on the cool reference of vegetation. Our founding has revealed the characteristics and pattern of UHI distribution in cold region during summer, and has verified the applicability of our LADSRSP and the observation experiments in complex urban thermal environments. This study could enrich the research on UHI effect during summer in cold regions and provide reference for high-precision urban thermal environment observation.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"24751-24765"},"PeriodicalIF":5.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11164497","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210075","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":"Downstream Task-Aware Cloud Removal for Very-High-Resolution Remote Sensing Images: An Information Loss Perspective","authors":"Ziyao Wang;Xianping Ma;Man-On Pun","doi":"10.1109/JSTARS.2025.3610641","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3610641","url":null,"abstract":"Cloud removal (CR) methods have been widely studied and discussed to address the issue of cloud occlusion in Earth observation tasks. Existing CR methods heavily rely on image similarity metrics, such as peak signal-to-noise ratio and structural similarity index measure to evaluate the quality of CR results. However, due to factors including rapid changes in landforms and viewpoint differences between cloudy and reference images, image similarity metrics could be ineffective, even misleading. To address these challenges, this study investigates CR by evaluating whether CR algorithms effectively produce information beneficial for downstream tasks. We introduce CUHKCR-EXT, the first very-high-resolution CR dataset explicitly designed for post-CR downstream task performance assessment. Furthermore, we propose DFCFormer, a dynamic filter-based transformer that generates adaptive kernels conditioned on cloud characteristics, enabling more precise recovery across diverse cloud types within a unified framework. In addition, we design a feature alignment loss that enforces consistency between cloud-removed and reference features at the semantic level, which guides the model to retain landform-relevant information crucial for downstream analysis. Using scene classification as a representative downstream task, we conduct extensive experiments and evaluate performance using both image similarity and information loss metrics. The results demonstrate that the proposed method achieves strong performance across all evaluated metrics. More importantly, the improvements lie not only in image similarity but also in the preservation of task-relevant semantics, which enhances the effective quality of output images for downstream applications rather than merely their visual fidelity.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"24531-24545"},"PeriodicalIF":5.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11165130","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210066","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":"RRCANet: Recurrent Reusable-Convolution Attention Network for Infrared Small Target Detection","authors":"Yongxian Liu;Boyang Li;Ting Liu;Zaiping Lin;Wei An","doi":"10.1109/JSTARS.2025.3610301","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3610301","url":null,"abstract":"Infrared small target detection is a challenging task due to its unique characteristics (e.g., small, dim, shapeless, and changeable). Recently published CNN-based methods have achieved promising performance with heavy feature extraction and fusion modules. To achieve efficient and effective detection, we propose a recurrent reusable-convolution attention network (RRCA-Net) for infrared small target detection. Specifically, RRCA-Net incorporates reusable-convolution block (RuCB) in a recurrent manner without introducing extra parameters. With the help of the repetitive iteration in RuCB, the high-level information of small targets in the deep layers can be well maintained and further refined. Then, a dual interactive attention aggregation module is proposed to promote the mutual enhancement and fusion of refined information. In this way, RRCA-Net can both achieve high-level feature refinement and enhance the correlation of contextual information between adjacent layers. Moreover, to achieve steady convergence, we design a target characteristic inspired loss function (DpT-k loss) by integrating physical and mathematical constraints. Experimental results on three benchmark datasets (e.g., NUAA-SIRST, IRSTD-1k, DenseSIRST) demonstrate that our RRCA-Net can achieve comparable performance to the state-of-the-art methods while maintaining a small number of parameters, and act as a plug and play module to introduce consistent performance improvement for several popular IRSTD methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"24632-24646"},"PeriodicalIF":5.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11164892","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210145","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":"SAM2-CD: Remote Sensing Image Change Detection With SAM2","authors":"Yuan Qin;Chaoting Wang;Yuanyuan Fan;Chanling Pan","doi":"10.1109/JSTARS.2025.3610156","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3610156","url":null,"abstract":"Change detection in high-resolution remote sensing imagery remains challenging due to the difficulty in distinguishing task-relevant semantic changes from irrelevant variations and capturing subtle local differences. While segment anything model 2 (SAM2) exhibits strong generalization in natural image segmentation, its direct application to remote sensing change detection is hindered by single-image segmentation bias and contextual granularity mismatches. To address these limitations, we propose SAM2-CD, a lightweight architecture that adapts SAM2 for bitemporal change detection through two novel modules: An activation selection gate) that dynamically suppresses task-irrelevant variations by learning channel-wise activation maps from cross-temporal features, and A global–local contextual attention module that hierarchically integrates adaptive pooling and spatial attention to amplify both scene-level semantics and pixel-level details. By leveraging SAM2’s multiscale pyramid encoder and our optimized multiscale feature fusion module, SAM2-CD achieves state-of-the-art performance across three benchmarks (LEVIR-CD, WHU-CD, and LEVIR+-CD), with IoU scores of 85.51%, 88.97%, and 69.31%, respectively. Notably, cross-dataset experiments demonstrate superior generalization, outperforming baselines by 35.29% in F1 under zero-shot settings, demonstrating superior accuracy and robustness in complex scenarios.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"24575-24587"},"PeriodicalIF":5.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11164661","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210022","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":"Unbiased Detection of Warming Trends Through Advanced Integration of Satellite and Reanalysis Air Temperatures","authors":"Che Wang;Min He;Ning Lu;Jun Qin","doi":"10.1109/JSTARS.2025.3610319","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3610319","url":null,"abstract":"Accurate detection of warming trends is crucial for both mitigation and adaptation strategies. While satellite observations provide high spatial resolution temperature data, cloud contamination creates gaps that require filling methods–a process that can bias warming trend calculations. Traditional gap-filling approaches, though accurate for absolute temperatures, consistently underestimate warming trends. This study presents a new data assimilation method that integrates moderate-resolution imaging spectroradiometer derived air temperatures with ERA5-Land reanalysis data to achieve unbiased warming trend detection while maintaining high spatial resolution. We validate our method against weather station data on the Tibetan Plateau (TP) and compare it with four mainstream gap-filling approaches: 1) temporal, 2) spatial, 3) spatio-temporal, and 4) multisource fusion-based methods. Our results show that while all methods, including ours, perform similarly in terms of absolute temperature accuracy (with root-mean-square error around 1.66 °C and R around 0.99), a critical difference emerges in warming trend estimation. Traditional gap-filling methods show negative biases in warming trends, but our assimilation-based approach almost completely eliminates these biases when validated against both station-level data and elevation-binned averages. The integrated air temperature for the TP reveals significant warming patterns, particularly in glacier regions, although the overall warming rate (0.022 °C/yr) is lower than that indicated by station data alone (0.026 °C/yr). This difference likely reflects the ability of our method to capture warming trends across the diverse terrain of the plateau, not just at station locations. This improvement in trend estimation, combined with the method’s ability to maintain high spatial resolution, represents a significant advance in the use of satellite-derived data for climate change analysis.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"24924-24935"},"PeriodicalIF":5.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11164928","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255862","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":"An Initial Frequency-Chirp Rate Spectrum Reconstruction Framework and Its Application in SA-ISAR Imaging of Maneuvering Targets","authors":"Jiaxing Yang;Yong Wang","doi":"10.1109/JSTARS.2025.3609724","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3609724","url":null,"abstract":"Sparse initial frequency-chirp rate (IFCR) spectrum reconstruction of a sparse aperture linear frequency modulation (SA-LFM) signal is crucial in SA inverse synthetic aperture radar (SA-ISAR) imaging for maneuvering targets. To avoid both the reconstruction error introduced by the matched phase-based spectrum and the optimal <italic>l</i>₁ regularization coefficient selection, this article proposes a framework to reconstruct the IFCR spectrum of the SA-LFM signal. It first constructs the initial IFCR spectrum by the proposed weighted fast iterative shrinkage thresholding (WFISTA) algorithm with a small <italic>l</i>₁ regularization coefficient, followed by the band exclude (BE) technique to eliminate the influence of the strong correlation between atoms. Assuming the signal atoms’ normalized amplitude is greater than both that of nonsignal atom and a preset threshold, the nonsignal atoms are iteratively removed until convergence. The IFCR spectrum served as the atom removal criterion, which was generated by WFISTA, ridge regression under varying coefficients, and the proposed improved local optimization (ILO) technique. The ILO additionally eliminates the BE-generated adjacent atom errors and corrects the spectrum amplitude via least square estimation. Furthermore, based on the strongest azimuth signal’s amplitude estimated via the Dechirp algorithm in each range bin, we design a range bin adaptive <italic>l</i>₁ regularization coefficient determination principle for the proposed framework and subsequently form the ISAR image. Finally, the experimental results validate the effectiveness of the proposed framework with its application in SA-ISAR imaging.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"24716-24750"},"PeriodicalIF":5.3,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11163639","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210129","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}