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

{"title":"Extracting Dwellings in Refugee Camps Using Multifractal Analysis and Mathematical Morphology Based Descriptors","authors":"Małgorzata Jenerowicz-Sanikowska;Anna Wawrzaszek","doi":"10.1109/JSTARS.2025.3546403","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3546403","url":null,"abstract":"This article presents an automatic procedure for detecting and counting dwellings in refugee/internally displaced persons camps. Very high resolution (VHR) satellite images are used to monitor camps, especially in inaccessible to “in-situ” measures areas. We develop a new algorithm to analyze these images, with the aim of improving detection accuracy and computing performance. The algorithm is based on local multifractal analysis and mathematical morphology, two methods that are becoming increasingly popular in the image analysis community. Our procedure translates the visual characterization of the desired structures into a morphological image processing chain. However, morphological filtering is not performed on the original image <italic>per se</i>, but on the image expressed by the Hölder exponent. Proposed method is applied to a set of VHR satellite images (GeoEye-1, WorldView-2, -3, -4 and JL-1GF02A) of two camps in Africa. Our technique is compared with results obtained by visual interpretation. The correlation coefficient between the two methods is 0.98, with an omission error of 7.98% and a commission error of 4.54%.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8001-8010"},"PeriodicalIF":4.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938975","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706712","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":"PMGMCN: A Parallel Dynamic Multihop Graph and Composite Multiscale Convolution Network for Hyperspectral Sparse Unmixing","authors":"Kewen Qu;Huiyang Wang;Mingming Ding;Xiaojuan Luo;Fangzhou Luo","doi":"10.1109/JSTARS.2025.3549515","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3549515","url":null,"abstract":"In recent years, sparse unmixing (SU) has garnered significant attention in hyperspectral images (HSI) because it does not require endmember estimation, relying instead on prior spectral libraries to represent observed HSI data, which avoids the influence of endmember extraction on unmixing. However, SU methods based on representation models have limited capability in learning nonlinear features, which results in poor abundances estimation performance in complex environments. Recently, inspired by deep learning, SU models based on neural networks have been proposed to more effectively extract and handle nonlinear features. Nevertheless, the convolution strategies employed in existing SU network models lead to insufficient attention to long-range pixel dependencies, consequently resulting in restricted utilization of spatial priors. In view of the abovementioned shortcomings, this article proposes a parallel dynamic multihop graph and composite multiscale convolution network for SU, referred to as PMGMCN. The network combines the advantages of convolutional neural network (CNN) and graph convolutional network (GCN), achieving a complementary and enhanced integration of their characteristics. Specifically, the network captures long-range spatial features through the designed dynamic multihop graph interaction attention module, which is based on GCN, while the composite multiscale convolution spatial–spectral attention module, which is based on CNN, is designed to extract multiscale spatial–spectral information within local regions. In addition, this article introduces an adaptive weighted total variation loss function based on Sobel edge operator and Gaussian function to encourage piecewise smoothness in abundances maps while preserving edge information. Extensive experiments on synthetic and real datasets demonstrate the effectiveness and superiority of the proposed method compared with the state-of-the-art methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8438-8456"},"PeriodicalIF":4.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937499","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726545","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":"2024 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 17","authors":"","doi":"10.1109/JSTARS.2025.3553722","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3553722","url":null,"abstract":"","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"17 ","pages":"20355-20637"},"PeriodicalIF":4.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937376","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688093","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":"Signal Compensation of Moon Mineralogy Mapper (M3) Under Low-Illumination Conditions Using a CycleGAN-Based Network","authors":"Rui Ni;Fei Zhao;Tingyu Meng;Yanlei Du;Pingping Lu;Robert Wang","doi":"10.1109/JSTARS.2025.3549768","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3549768","url":null,"abstract":"Lunar south polar regions have attracted considerable scientific interest due to their potential for preservation of water ice and unique mineralogical compositions. As a key scientific payload for surface composition exploration missions, hyperspectral imager faces significant challenges in the lunar polar regions. The primary issue is the low-illumination conditions in these areas, where terrain-induced shadows drastically reduce the signal-to-noise ratio (SNR) of hyperspectral images (HSIs), resulting in limited availability of reliable spectral available for polar region analysis. Previous studies have largely bypassed low-SNR spectra or filtered them out, as there has been no effective method to recover the spectral information under these harsh conditions. To tackle this problem, an effective method based on CycleGAN network is proposed to compensate hyperspectral data obtained by Moon mineralogy mapper (M3) under low-illumination conditions in lunar south polar regions. The network was trained by constructing paired datasets of low and high SNR M3 spectra from the lunar South Pole. The efficacy of the proposed method is validated using real high SNR M3 spectral observations, with the performance of the compensated results comprehensively assessed across three dimensions: structural indicators, spectral indices, and spatial consistency analysis. The strong correlation between the M3 spectral compensation results with Selenological Engineering Explorer (Kaguya) multiband imager data, as well as other sensors' inversion of plagioclase abundance around the Shackleton Crater, underscores the network's potential for mineral exploration. To the best of authors' knowledge, this study represents one of the first efforts to compensate illumination-limited spectra in lunar HSI. It provides an efficient method for enhancing the SNR of M3 spectra in the lunar polar region, offering a reliable tool and novel insights for future mineralogical and water ice studies.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8504-8522"},"PeriodicalIF":4.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10936990","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740322","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":"IEEE Geoscience and Remote Sensing Society Information for Authors","authors":"","doi":"10.1109/JSTARS.2025.3553720","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3553720","url":null,"abstract":"","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"17 ","pages":"C3-C3"},"PeriodicalIF":4.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667275","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 Novel Convolutional Neural Network for the Extraction of Algal Bloom and Aquatic Vegetation in Typical Eutrophic Shallow Lakes","authors":"Jingming Wang;Chang-Qing Ke;Yu Cai;Jianwan Ji;Zhenqing Wang","doi":"10.1109/JSTARS.2025.3548589","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3548589","url":null,"abstract":"Under the hybrid impact of regional climate change and extensive human activities, lake eutrophication has become an increasingly serious problem, which causes a dramatic reduction in the area of aquatic vegetation (AV) and poses huge challenges to the balance of regional lake ecosystems. As an important freshwater resource, shallow lakes play an important role in balancing water resources, adjusting regional climate, and retaining clean water supply. However, in view of the complexity and variability of shallow lake environment, especially the similarity of spectral characteristics between algal bloom (AB) and AV in shallow lakes, the extraction results of AB and AV using most algorithms are not satisfactory. In response to these problems, this study utilized Landsat images as the dataset to accurately differentiate AB and AV by developing a new extraction network (AAENet) aiming at eutrophic shallow lakes. Next, the AAENet model was compared with three classic semantic segmentation models (UNet, Deeplab v3, and PSPNet) and the vegetation and bloom indices algorithm. Finally, the spatiotemporal distribution and area change in typical shallow lakes were analyzed based on the extraction results of the AAENet model. The results showed that: 1) the AAENet model achieved the highest accuracy in distinguishing AB and AV, with an overall accuracy of 87.85%, an F1 score of 0.85, and a Frequency Weighted Intersection-over-Union of 0.76 in the testing lakes. 2) In terms of improving the performance of the AAENet model, the ConvNeXt encoder played the most significant role. 3) During 2013–2023, the area of AB in Chaohu Lake and Taihu Lake decreased annually by 0.73 km² and 3.29 km², respectively. In particular, the area of AV in Chaohu Lake steadily increased at a rate of 0.27 km²/year, whereas the area of AV in Taihu Lake exhibited an initial decline followed by an increase. This study can provide important technical support for monitoring the dynamics of AB and AV in lakes.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8099-8111"},"PeriodicalIF":4.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10925549","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706704","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":"Soil Moisture Estimates Using -Band Airborne SAR Over Forests Replicating NISAR Observations","authors":"Seung-Bum Kim;Xiao-Lan Xu;Simon Kraatz;Andreas Colliander;Michael H. Cosh;Vicky Kelly;Paul Siqueira","doi":"10.1109/JSTARS.2025.3544095","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3544095","url":null,"abstract":"Airborne SAR observations of soil moisture conditions at 6-m resolution are analyzed over deciduous and evergreen forests in the U.S. Northeast during the 10-day spring and 14-day summer periods in 2022. During the summer, the dynamic range of HH is about 1 dB, associated mostly with soil moisture changes. Larger changes in backscattering are found between the two seasons, reflecting the vegetation effect. In spring, backscattering decreases in time, suggesting the impact of drying trunks and thickening foliage. In summer, σ° correlates highly with in situ soil moisture, consistently between ascending and descending viewing geometry on flat terrain and on slopes only when imaged at similar incidence angles. The consistency benefits NISAR's retrieval by allowing more frequent consistent retrievals of soil moisture. Soil moisture was retrieved using HH to replicate NISAR observations and its accuracy in the eight sites is 0.067 m³/m³ in unbiased RMSE, assessed over a 140-m domain per in situ site. The results are very encouraging as an independent test of the retrieval algorithm under the challenging conditions of surface slope or forest vegetation. Deficiencies in the retrieval algorithm appear to originate from the modeling of vegetation effect and topography. As long as the two causes are temporally static, they introduce a bias error. However, the temporal range of the retrieval is the most useful property for applications and matches well with in situ observations.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"7364-7373"},"PeriodicalIF":4.7,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10925521","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698335","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":"3-D UXSE-Net for Seismic Channel Detection Based on Satellite Image Enhanced Synthetic Datasets","authors":"Xinke Zhang;Yihuai Lou;Naihao Liu;Daosheng Ling;Yunmin Chen","doi":"10.1109/JSTARS.2025.3550578","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3550578","url":null,"abstract":"Channels are essential indicators of sedimentary environments and play a vital role in geological applications, such as hydrocarbon exploration, sediment transport, and the study of ancient river geomorphology. Deep learning (DL) techniques have shown great potential in improving channel detection accuracy and efficiency. However, insufficient labeled training data remains a key challenge for refining DL models. To address this issue, we propose a workflow that automatically generates synthetic datasets by integrating channel features extracted from high-resolution satellite images. We first extract river channel features and grayscale values from satellite images. These extracted features are then used to construct reflectivity models, incorporating structural deformations based on seismic reflector dips. The reflectivity models are subsequently convolved with wavelets to generate synthetic datasets. These synthetic datasets are used to train the proposed 3-D UXSE-Net, which integrates the 3-D UX-Net architecture with the squeeze-and-excitation blocks. The model generates improved feature representations that enhance performance by combining convolutional neural networks for local feature extraction and Transformer-based modules for capturing global context. We validate our approach by applying the model to both synthetic and 3-D field seismic datasets. Our results show that 3-D UXSE-Net outperforms baseline methods, including the coherence-based approach and other DL models, and demonstrates strong generalization to field data even when trained solely on synthetic data. Comparisons of different methods highlight the effectiveness of the synthetic data generation approach for DL model training.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8300-8311"},"PeriodicalIF":4.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10923698","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740374","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":"MSFCN: A Multiscale Feature Correlation Network for Remote Sensing Image Scene Change Detection","authors":"Feng Xie;Zhongping Liao;Jianbo Tan;Zhiguo Hao;Shining Lv;Zegang Lu;Yunfei Zhang","doi":"10.1109/JSTARS.2025.3549471","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3549471","url":null,"abstract":"Scene-level change detection identifies land use changes and determines change types from a high-level semantic perspective, which is significant for monitoring urbanization. The existing advanced methods are generally based on Siamese networks that utilize the feature correlation of bitemporal scenes or introduce change information to enhance the feature representation. However, their extraction of feature correlation is insufficient to improve the model performance further. This article proposed a Siamese-based multiscale feature correlation network (MSFCN) to enhance the correlation extraction process. First, 1-D multiscale local features are obtained by the designed space-channel self-calibration module and multiscale local feature extraction module. Then, these features are inputted into the proposed multiscale feature correlation module to extract feature correlation. Finally, the dual-branch features are fused based on the feature correlation to generate more discriminative 1-D deep features. In addition, cosine embedding loss is used to constrain the scene binary change detection task and construct a multitask loss for model optimization. On the Hanyang and WH-MAVS datasets, MSFCN achieved average scene classification accuracies of 93.33% and 94.86%, scene-level binary change detection accuracies of 95.71% and 98.13%, and scene-level semantic change detection accuracies of 90.00% and 93.95%, respectively, significantly better than the comparison methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8275-8299"},"PeriodicalIF":4.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10923708","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740338","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 Novel Shape Guided Transformer Network for Instance Segmentation in Remote Sensing Images","authors":"Dawen Yu;Shunping Ji","doi":"10.1109/JSTARS.2025.3550460","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3550460","url":null,"abstract":"Instance segmentation performance in remote sensing images (RSIs) is significantly affected by two issues: how to extract accurate boundaries of objects from remote imaging through the dynamic atmosphere, and how to integrate the mutual information of related object instances scattered over a vast spatial region. In this study, we propose a novel shape guided transformer network (SGTN) to accurately extract objects at the instance level. Inspired by the global contextual modeling capacity of the self-attention mechanism, we propose an effective transformer encoder termed LSwin, which incorporates vertical and horizontal 1-D global self-attention mechanisms to obtain better global-perception capacity for RSIs than the popular local-shifted-window based swin transformer. To achieve accurate instance mask segmentation, we introduce a shape guidance module (SGM) to emphasize the object boundary and shape information. The combination of SGM, which emphasizes the local detail information, and LSwin, which focuses on the global context relationships, achieve excellent RSI instance segmentation. Their effectiveness was validated through comprehensive ablation experiments. Especially, LSwin is proven better than the popular ResNet and swin transformer encoders at the same level of efficiency. Compared to other instance segmentation methods, our SGTN achieves the highest average precision scores on two single-class public datasets (WHU dataset and BITCC dataset) and a multiclass public dataset (NWPU VHR-10 dataset).","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8325-8339"},"PeriodicalIF":4.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10923715","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726402","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}