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":"Lossless Compression Framework Using Lossy Prior for High-Resolution Remote Sensing Images","authors":"Enjia Gu;Yongshan Zhang;Xinxin Wang;Xinwei Jiang","doi":"10.1109/JSTARS.2025.3550721","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3550721","url":null,"abstract":"Lossless compression of remote sensing images is critically important for minimizing storage requirements while preserving the complete integrity of the data. The main challenge in lossless compression lies in striking a good balance between reasonable compression durations and high compression ratios. In this article, we introduce an innovative lossless compression framework that uniquely utilizes lossy compression data as prior knowledge to enhance the compression process. Our framework employs a checkerboard segmentation technique to divides the original remote sensing image into various subimages. The main diagonal subimages are compressed using a traditional lossy method to obtain prior knowledge for facilitating the compression of all subimages. These subimages are then subjected to lossless compression using our newly developed lossy prior probability prediction network (LP3Net) and arithmetic coding in a specific order. The proposed LP3Net is an advanced network architecture, consisting of an image preprocessing module, a channel enhancement module, and a pixel probability transformer module, to learn the discrete probability distribution of each pixel within every subimage, enhancing the accuracy and efficiency of the compression process. Experiments on high-resolution remote sensing image datasets demonstrate the effectiveness and efficiency of the proposed LP3Net and lossless compression framework, achieving a minimum of 4.57% improvement over traditional compression methods and 1.86% improvement over deep learning-based compression methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8590-8601"},"PeriodicalIF":4.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10930824","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761388","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":"Text-Enhanced Multimodal Method for SAR Ship Classification With Geometry and Polarization Information","authors":"Jinyue Chen;Youming Wu;Wei Dai;Wenhui Diao;Yang Li;Xin Gao;Xian Sun","doi":"10.1109/JSTARS.2025.3551239","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3551239","url":null,"abstract":"Synthetic aperture radar (SAR) ship classification is crucial for maritime surveillance. Most existing methods primarily focus on visual or polarimetric features, often constrained by a limited feature set and facing challenges in data diversity and multimodal information integration. This study introduces a text-enhanced multimodal framework for SAR ship classification (TeMSC), an extensible and unified approach that integrates multimodal information related to SAR ships. It consists of text-form geometry information embedding, polarization and visual information embedding, and a multimodal prediction module. By incorporating ship geometry information in text format, TeMSC leverages text representation to enhance feature expressiveness, compensating for the limited discriminative power of traditional visual and polarization features, especially in low-resolution scenarios. TeMSC effectively processes complementary multimodal information through a multimodal prediction module, while avoiding the complexity associated with traditional decision-level feature fusion strategies. In addition, a classification token mechanism is introduced to streamline the classification process. Through a two-stage training strategy, TeMSC captures information across multiple SAR datasets, enhancing its generalization and adaptability. Extensive experiments on the FUSAR-Ship and OpenSARShip datasets demonstrate the superior performance of TeMSC and highlight the benefits of multimodal integration for SAR ship classification. TeMSC provides a foundation for future research on SAR-focused multimodal learning applications.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8659-8671"},"PeriodicalIF":4.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10925632","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761431","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":"Improved Deorientation Processing for Polarimetric SAR Data Using a Phenomenological Approach","authors":"Reza Bordbari;Andrew J. Hooper","doi":"10.1109/JSTARS.2025.3551246","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3551246","url":null,"abstract":"Polarimetric synthetic aperture radar data can be used to retrieve structural and textural information of the surface and is widely used in land cover classification. To extract relevant parameters, the data must first be compensated for the orientation of the polarimetric scattering targets. However, existing methods for deorientation processing are not able achieve this robustly and the data still suffer from orientation-induced scattering mechanism ambiguity. Here, we present a new approach to deorientation that greatly improves on existing methods. Our algorithm innovatively employs phenomenological target decomposition theory and the concept of polarization nulls to extract and deorient orientation-perturbed components of the target. The objective is to manipulate coherency matrix elements of a distributed target to obtain more descriptive target parameters, which are of critical importance in its deorientation. We applied our approach to C-, L-, and P-band datasets containing built-up areas with different orientations. In contrast to existing methods, our deorientation algorithm led to targets of all orientations being identified as having similar scattering characteristics. We also demonstrated that, an improved polarimetric target decomposition performance is achieved when the proposed deorientation processing is incorporated into model-based decompositions.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8685-8695"},"PeriodicalIF":4.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10925637","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761432","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}