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

{"title":"Sequential Inversion for Helicopter Time-Domain Electromagnetics Based on a Regularized Extended Kalman Filtering","authors":"Sirui Zhou;Jun Lin;Chuandong Jiang;Haigen Zhou;Yanzhang Wang","doi":"10.1109/JSTARS.2025.3559504","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559504","url":null,"abstract":"The helicopter time-domain electromagnetic method (HTEM) has been widely applied in challenging geological terrains, particularly in large-scale mineral exploration, underground water resource detection, and the selection of sites for underground engineering, due to its advantage of not requiring personnel to enter the detection area. Currently, the 1-D inversion method with lateral constraints, which is commonly used in HTEM, faces challenges quickly delivering inversion results in the field due to its high computational demands and lengthy processing time. In this article, we propose a sequential inversion method for HTEM based on the regularized extended Kalman filter (REKF). The REKF algorithm is used to predict the current inversion result at a given time by using the inversion result from the previous moment, and predictions are corrected with the observed data at that specific time. We also introduce a vertical roughness regularization term to avoid overfitting issues during the inversion process. Based on the sequential processing strategy of measuring while inverting, the REKF algorithm yields the optimal solution of the inversion objective function in just a few iterations, or even a single iteration, enabling near real-time calculations. In the simulation experiments, the advantages of the REKF method are demonstrated by comparing the inversion results of the REKF method with those of the extended Kalman filter method and the Occam method with lateral constraints. Finally, we perform REKF inversion on HTEM data obtained from a location in Xinjiang, China. The results demonstrate the accuracy and practicality of the REKF inversion method.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10896-10908"},"PeriodicalIF":4.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960538","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896520","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 Progressive Spectral Correction and Spatial Compensation Network for Pansharpening","authors":"Rixian Liu;Hangyuan Lu;Biwei Chi;Yong Yang;Shuying Huang","doi":"10.1109/JSTARS.2025.3559582","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559582","url":null,"abstract":"Pansharpening aims to produce a high resolution multispectral image by fusing a panchromatic image with a low-resolution multispectral image. Current pansharpening methods often overlook the significant modality differences between source images and lack interaction between them, resulting in spatial-spectral distortions. To address these issues, we proposed a novel progressive spectral correction and spatial compensation network for pansharpening. The network comprises a spectral correction branch, a spatial compensation branch, and a spectral-spatial fusion (SSF) branch. In the spectral correction branch, we designed a local spectral reinforcement (LSR) module and a global spectral rectification (GSR) module to keep the spectral fidelity. The LSR module is designed to reinforce the unique local information from different kinds of spectral features, while the GSR module captures long-range dependency and rectifies the spectral features with a cross-attention mechanism. In the spatial compensation branch, we designed a multiscale dilated adaptive feature extraction module guided by spectral and spatial attention to extract useful spatial details, and the details are progressively compensated into the SSF branch to better keep spatial fidelity. The SSF branch is designed to interact with spectral correction branch and spatial compensation branch to mitigate the modal difference and progressively optimize the spectral-spatial information. Comprehensive experiments show that the proposed method outperforms current state-of-the-art pansharpening methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10772-10785"},"PeriodicalIF":4.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960710","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896281","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":"Large Area Crops Mapping by Phenological Horizon Attention Transformer (PHAT) Method Using MODIS Time-Series Imagery","authors":"Quanshan Gao;Taixia Wu;Hongzhao Tang;JingYu Yang;Shudong Wang","doi":"10.1109/JSTARS.2025.3559939","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559939","url":null,"abstract":"Accurate collection of crop planting information at large area is essential for estimating agricultural productivity and ensuring food security. Different crops have varying growth cycles and phenological stages, and changes in factors such as topography, soil type, and moisture conditions can lead to diversity in crops growth status, which complicates uniform monitoring. Multiple crops mapping simultaneously with high precision presents a significant challenge due to the high spatial heterogeneity of crops distribution across vast regions. To address these challenges, this article developed an advanced deep learning crop mapping method, i.e., phenological horizon attention mechanism-transformer model (PHAT) to achieve rapid and accurate multiple crops extraction over large areas. Initially, time-series data were constructed using the normalized differential vegetation index (NDVI) dataset based on moderate resolution imaging spectroradiometer (MODIS) product. Subsequently, in the mixed pixel decomposition phase, orthogonal subspace projection and vertex component analysis were employed to identify crop types and extract endmembers. While the regular changes in the time-series NDVI reflect the phenological evolution trend among multiple crops, but the phenological characteristics difference between the same crop is extremely difficult to find. The PHAT model was therefore trained using the phenological features of endmembers to obtain the spatial distribution of crops, and to resolve the issue of varying time-series curves for the same crop across large areas. This study selected the North China Plain in 2021 as the research area, utilizing Google Earth data and Landsat 8 images to verify the approach's accuracy. Based on the MODIS NDVI data with a coarse spatial resolution of 250 m, our method achieved an OA of 90.1% for the synchronous extraction of soybean, spring peanut-summer sesame, winter wheat-summer maize, paddy rice, and rapeseed-cotton, with a RMSE of approximately 12% in 16.6 million mu of cultivated land.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10995-11013"},"PeriodicalIF":4.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10962301","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896283","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 Multiscale Convolution SAR Image Target Recognition Method Based on Complex-Valued Neural Networks","authors":"Guangyu Hou;Zhihui Xin;Guisheng Liao;Penghui Huang;Yuhao Huang;Rui Zou","doi":"10.1109/JSTARS.2025.3559656","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559656","url":null,"abstract":"Recent advances in deep learning have driven significant success in synthetic aperture radar (SAR) automatic target recognition, particularly through convolutional neural network (CNN) based classification algorithms. However, SAR images possess distinctive physical scattering properties, owing to their unique imaging mechanism. Many deep learning algorithms rely solely on amplitude information, ignoring phase information, which may result in the loss of information in the original complex-valued SAR image and suboptimal performance. To tackle these problems, this article introduces a SAR target recognition approach based on complex-valued operations, designated as complex-valued residual mish activation and convolution block attention module (CBAM) net (CRMC-Net). The CRMC-Net effectively utilizes the amplitude and phase information in complex-valued SAR data. Specifically, first, the elements of CNN, including the input and output layers, the convolution layers, the activation functions, and the pooling layers, are extended to the complex-valued domain. Second, in order to further enhance the representation ability of the model, multiscale information of the target is extracted through different convolution kernel sizes. Finally, the network constructs many complex-valued operation blocks to enhance the robustness of the designed network, including the complex-valued residual block, complex-valued Mish activation function, and complex-valued CBAM. The experimental results obtained from the moving and stationary target capture and recognition dataset and OpenSARShip2.0 dataset demonstrate that the proposed network model outperforms the traditional real-valued models. It can further reduce the classification error and enhance performance.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10657-10673"},"PeriodicalIF":4.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960712","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896518","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 Difference Wavelet Feature Index for Estimating Aerial N Uptake of Winter Wheat from In Situ Hyperspectral Remote Sensing","authors":"Bin-Bin Guo;Wen-Hui Wang;Chao Ma;Jun Zhang;Fei Yin;Wei Feng","doi":"10.1109/JSTARS.2025.3559100","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559100","url":null,"abstract":"The real-time and accurate assessment of crop aerial nitrogen (N) uptake is of significant importance for optimizing N fertilization. To develop a robust method for determining aerial N uptake in winter wheat, a field experiment with different N fertilizer levels was conducted over three successive years at two ecological sites in Henan, China. This research systematically compared the correlation between aerial N uptake and spectral parameters derived from various spectral transform methods: continuum removal (CR), standard normal variate transform method, first derivative reflectance (FDR), and continuous wavelet transforms (CWT). The findings revealed that CWT exhibited the highest efficacy among all the spectral transform methods, followed by FDR, with <italic>R</i>² values of 0.777 for WF(4,770) and 0.764 for FDR₇₄₈. A new index, termed the difference wavelet feature index (DWF), is defined as DWF(4 560 770) = WF(4560) − WF(4770). This simple yet effective index significantly enhances the assessment of aerial N uptake, achieving an <italic>R</i>² of 0.815. Validation with independent data showed that the RMSE for the DIDA, FDR₇₄₈, WF(4770), and DWF(4 560 770) under different cultivation factors were 3.578–4.361 g m^-2, 3.501–4.219 g m^-2, 3.472–4.309 g m^-2, 3.262–4.030 g m^-2, respectively. It was further verified that the newly DWF(4 560 770) index has excellent universality and stability. Therefore, the aforementioned studies indicated that the novel DWF(4 560 770) is more suitable for evaluating aerial N uptake at the heterogeneous field scale and also has significant potential for precise prediction of aerial N uptake using UAV remote sensing.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11213-11224"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10959048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900571","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 Gaussian-PSO XGBoost Model for Alpine Forests Aboveground Biomass Estimation Using Spaceborne PolSAR and LiDAR Data","authors":"Fu-Gen Jiang;Ming-Dian Li;Si-Wei Chen","doi":"10.1109/JSTARS.2025.3559233","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559233","url":null,"abstract":"Accurate estimation of forest aboveground biomass (AGB) is fundamental to forest management and ecosystem monitoring. Natural forest ecosystems are an important guarantee to maintain the global ecological balance and carbon cycle, but the complex climate, dramatic topographic relief, and saturation effects make it difficult to achieve reasonable AGB estimation of alpine forests with commonly used optical data. In this study, spaceborne dual-polarimetric synthetic aperture radar and light detection and ranging data were combined to break through the limitation of optical data, and the information on the vertical structure inside the forests was extracted, to achieve high-precision forest AGB estimation and reveal the distribution pattern of forest AGB. An adaptive Gaussian-particle swarm algorithm XGBoost model (AGP-XGBOOST) was proposed to improve the forest AGB estimation, which adjusted the PSO through the built-in adaptive parameter of the Gaussian function to achieve the hyperparameter optimization for the XGBoost model. The proposed method was validated with the forest survey data, and classic machine-learning models were constructed for comparison. The comparative analysis was carried out using natural forests in the eastern Tibetan Plateau as an example, and the results showed that the proposed AGP-XGBOOST model consistently maintained the best performance across all models, and the AGB estimation errors caused by the combined data source decreased by 30.8%, 24.4%, and 10.1% compared to the independent data sources. In addition, the forest AGB mapping showed that the distribution pattern of forest AGB on the eastern Tibetan Plateau was significantly affected by terrain fluctuations.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10157-10171"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10959714","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883495","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":"HTC-HAD: A Hybrid Transformer-CNN Approach for Hyperspectral Anomaly Detection","authors":"Minghua Zhao;Wen Zheng;Jing Hu","doi":"10.1109/JSTARS.2025.3559079","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559079","url":null,"abstract":"Hyperspectral anomaly detection (HAD) identifies anomalies by analyzing differences between anomalies and background pixels without prior information, presenting a significant challenge. Most existing studies leverage the high correlation in spectral and spatial dimensions, primarily focusing on local spectral and spatial information for background reconstruction while neglecting long-range dependencies. This local perception constrains models from fully capturing intrinsic spatial–spectral connections. To address this, we propose a novel hybrid transformer-CNN network for HAD (HTC-HAD). Specifically, HTC-HAD combines CNNs with transformers, where the CNN focuses on local modeling, and the transformer addresses long-range modeling. This dual approach ensures the accurate reconstruction of backgrounds by capturing both local and long-range dependencies. Meanwhile, to reduce model complexity and redundancy among neighboring bands, we incorporate a simplified and effective band selection strategy as preprocessing. In addition, to prevent anomalies from being reconstructed during background estimation, we employ an adaptive weight loss function to suppress them. Experimental results on several real datasets, both qualitatively and quantitatively, demonstrate that our HTC-HAD achieves satisfying detection performance.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10144-10156"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10959089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883496","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":"DSMF-Net: A One-Stage SAR Ship Detection Network Based on Deformable Strip Convolution and Multiscale Feature Refinement and Fusion","authors":"Xingyu Liu;Jun Pan;Rong Hu;Wenli Huang;Jiawei Lin;Jiarui Hu","doi":"10.1109/JSTARS.2025.3559414","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559414","url":null,"abstract":"Synthetic aperture radar (SAR), an all-weather and day-and-night remote sensing imaging technology, is crucial for ship detection. However, SAR images are hampered by speckle noise and coastal clutter, and ship targets exhibit multiscale and small-scale characteristics. To tackle these challenges, we introduce the DSMF-Net, a SAR ship detection network leveraging deformable strip convolution and multiscale feature refinement and fusion. First, to counter interference from complex backgrounds, such as nearshore ports and speckle noise, the deformable strip convolution (DSConv) is introduced and incorporated into the backbone network for SAR ship feature extraction, named SSFEBackbone. DSConv adaptively adjusts convolution sampling positions based on ship target characteristics, precisely extracting features with directional and strip structures. Second, the dual-stream self-attention feature refinement module is utilized to refine high-level semantic features. Through the mixing spatial and channel attention (MSCA) mechanism, differences and correlations between complex backgrounds and ship entities are further captured, enhancing feature expression. Finally, the adaptive selective feature pyramid network is proposed. By leveraging MSCA attention, high-level semantic and low-level spatial features are flexibly matched, enabling better key information retention during fusion and background clutter suppression, thus improving detection performance for complex backgrounds and multiscale ship targets. Experimental results demonstrate that DSMF-Net performs significantly better in ship detection in SAR images. It outperforms existing state-of-the-art methods on the SAR-Ship-Dataset, high-resolution SAR images dataset, and SAR ship detection dataset, achieving an AP<inline-formula><tex-math>$_{text{50}}$</tex-math></inline-formula> of 96.8%, 93.1%, and 97.4%, respectively.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10694-10710"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960366","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888369","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":"CD-STMamba: Toward Remote Sensing Image Change Detection With Spatio-Temporal Interaction Mamba Model","authors":"Shanwei Liu;Shuaipeng Wang;Wei Zhang;Tao Zhang;Mingming Xu;Muhammad Yasir;Shiqing Wei","doi":"10.1109/JSTARS.2025.3559085","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559085","url":null,"abstract":"Change detection (CD) is a critical Earth observation task. Convolutional neural network (CNN) and Transformer have demonstrated their superior performance in CD tasks. However, the limitations of the limited receptive field of CNN and the high-computational complexity of Transformer remain. Recently, the Mamba architecture, based on state-space models, has demonstrated strong global receptive field capabilities and implements linear time complexity in computational processes. While some researchers have incorporated it into CD tasks, most have neglected the effective application of the Mamba selective scanning algorithm for modeling bitemporal image dependencies, resulting in suboptimal feature learning from bitemporal images. In this article, we propose a CD Mamba model (CD-STMamba), which can efficiently encode and decode bitemporal images interactively from multiple dimensions, thus enabling more accurate CD. Specifically, we propose a spatio-temporal interaction module (STIM), which can interact with bitemporal image features in multiple dimensions and fit with the Mamba architecture, allowing it to fully learn the global contextual information of the bitemporal input image. We also introduce a decoding block called the CD block, which can be fully decoded to learn multiple spatio-temporal relationships based on the characteristics of STIM. This block employs multiple change visual state space blocks internally to decode different spatio-temporal interactions and utilizes the change attention module to capture change features comprehensively for more accurate CD. The proposed CD-STMamba achieved state-of-the-art intersection over union (IoU) on three datasets, Wuhan University Building Change Detection Dataset (91.29% ), Sun Yat-Sen University Change Detection (73.45% ), and Change Detection Dataset (95.56% ).","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10471-10485"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10959091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888317","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":"Satellite Image Inpainting With Edge-Conditional Expectation Attention","authors":"Dazhi Zhou;Yanjun Chen;Yuhong Zhang;Jing Niu","doi":"10.1109/JSTARS.2025.3559203","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559203","url":null,"abstract":"Satellite images often suffer from data loss and corruption due to various factors, including sensor malfunctions and atmospheric interference, leading to incomplete and degraded imagery. In satellite images, long-range dependencies are particularly significant due to irregular and widely distributed geomorphological edges, such as rivers, mountains, and urban structures. Traditional convolutional neural network-based inpainting methods face challenges due to their fixed receptive fields and parameter sharing, limiting their ability to effectively capture long-range dependencies and differentiate between corrupted and uncorrupted areas. To address these limitations, we propose a deep learning approach based on an edge-conditional expectation attention module, which conditions the attention mechanism on edge information to enhance the model's focus on high-frequency edge details. This enables the network to capture critical structures within the image better. In addition, we apply Chebyshev’s inequality within the attention mechanism to constrain the expectation of attention outputs, reducing excessive deviations and stabilizing the reconstruction process. Experimental results demonstrate that our approach outperforms several state-of-the-art methods in restoring missing regions and reconnecting geomorphological features.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10830-10845"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10959709","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888324","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}