IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society最新文献_第5页

Combining Contrastive Learning and Diffusion Model for Hyperspectral Image Classification 结合对比学习和扩散模型的高光谱图像分类

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-21 DOI: 10.1109/LGRS.2025.3601152

Xiaorun Li;Jinhui Li;Shuhan Chen;Zeyu Cao

{"title":"Combining Contrastive Learning and Diffusion Model for Hyperspectral Image Classification","authors":"Xiaorun Li;Jinhui Li;Shuhan Chen;Zeyu Cao","doi":"10.1109/LGRS.2025.3601152","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3601152","url":null,"abstract":"In recent years, self-supervised learning has made significant strides in hyperspectral image classification (HSIC). However, different approaches come with distinct strengths and limitations. Contrastive learning excels at extracting key information from large volumes of redundant data, but its training objective can inadvertently increase intraclass feature distance. To address this limitation, we leverage diffusion models (DMs) for their proven ability to refine and aggregate features by modeling complex data distributions. Specifically, DMs’ inherent denoising and generative processes are theoretically well-suited to enhance intraclass compactness by learning to reconstruct clean, representative features from perturbed inputs. We propose the new method—ContrastDM. This approach generates synthetic features, improving and enriching feature representation, and partially addressing the issue of sample sparsity. Classification experiments on three publicly available datasets demonstrate that ContrastDM significantly outperforms state-of-the-art methods.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

S3LBI: Spectral–Spatial Segmentation-Based Local Bicubic Interpolation for Single Hyperspectral Image Super-Resolution 基于光谱-空间分割的单幅高光谱图像超分辨率局部双三次插值

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-21 DOI: 10.1109/LGRS.2025.3601230

Yubo Ma;Wei He;Siyu Cai;Qingke Zou

引用次数: 0

Multimodal-Guided Transformer Architecture for Remote Sensing Salient Object Detection 遥感显著目标检测的多模态引导变压器结构

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-21 DOI: 10.1109/LGRS.2025.3601083

Bei Cheng;Zao Liu;Huxiao Tang;Qingwang Wang;Wenhao Chen;Tao Chen;Tao Shen

{"title":"Multimodal-Guided Transformer Architecture for Remote Sensing Salient Object Detection","authors":"Bei Cheng;Zao Liu;Huxiao Tang;Qingwang Wang;Wenhao Chen;Tao Chen;Tao Shen","doi":"10.1109/LGRS.2025.3601083","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3601083","url":null,"abstract":"The latest remote sensing image saliency detectors primarily rely on RGB information alone. However, spatial and geometric information embedded in depth images is robust to variations in lighting and color. Integrating depth information with RGB images can enhance the spatial structure of objects. In light of this, we innovatively propose a remote sensing image saliency detection model that fuses RGB and depth information, named the multimodal-guided transformer architecture (MGTA). Specifically, we first introduce the strongly correlated complementary fusion (SCCF) module to explore cross-modal consistency and similarity, maintaining consistency across different modalities while uncovering multidimensional common information. In addition, the global–local context information interaction (GLCII) module is designed to extract global semantic information and local detail information, effectively utilizing contextual information while reducing the number of parameters. Finally, a cascaded feature-guided decoder (CFGD) is employed to gradually fuse hierarchical decoding features, effectively integrating multilevel data and accurately locating target positions. Extensive experiments demonstrate that our proposed model outperforms 14 state-of-the-art methods. The code and results of our method are available at <uri>https://github.com/Zackisliuzao/MGTANet</uri>","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Removal of the Feedback Loop in CCSDS 123.0-B-2 During Hardware Implementation CCSDS 123.0-B-2硬件实现过程中反馈回路的去除

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-21 DOI: 10.1109/LGRS.2025.3601194

Liang Jia;Qi Wang;Lei Zhang;Chengpeng Song;Peng Zhang

引用次数: 0

Channel Characterization Based on 3-D TransUnet-CBAM With Multiloss Function 基于多损耗函数的三维TransUnet-CBAM通道表征

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-21 DOI: 10.1109/LGRS.2025.3601200

Binpeng Yan;Jiaqi Zhao;Mutian Li;Rui Pan

{"title":"Channel Characterization Based on 3-D TransUnet-CBAM With Multiloss Function","authors":"Binpeng Yan;Jiaqi Zhao;Mutian Li;Rui Pan","doi":"10.1109/LGRS.2025.3601200","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3601200","url":null,"abstract":"The channel system is intimately linked to the formation of oil and gas reservoirs. In petroliferous basins, channel deposits frequently serve as both storage spaces and fluid conduits. Consequently, the accurate identification of channels in 3-D seismic data is, therefore, critical for reservoir prediction. Traditional seismic attribute-based methods can outline channel boundaries, but noise and stratigraphic complexity introduce discontinuities that reduce accuracy and require extensive manual correction. Deep learning-based methods outperform conventional methods in terms of efficiency and precision. However, the similar seismic signatures of channels and continuous karst caves in seismic profiles can still mislead the existing models. To address this challenge, we proposed an improved variant of the 3-D TransUnet model for 3-D seismic data recognition. The model incorporates channel and spatial attention mechanisms into the skip connections of the TransUnet architecture, effectively enhancing its feature representation capability and recognition accuracy. In addition, a multiloss function is introduced to improve the delineation and continuity of the channel while increasing the model’s robustness against nonchannel interference features. Experiments on synthetic and field seismic data confirm superior boundary delineation, continuity, and noise resistance compared with baseline methods.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Polynomial Fitting Emitter Localization Method Based on Multisubaperture Phase Stitching 基于多子孔径相位拼接的多项式拟合发射器定位方法

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-21 DOI: 10.1109/LGRS.2025.3601138

Jiayu Sun;Hao Huan;Ran Tao;Yue Wang

引用次数: 0

Enhanced Slum Mapping Through U-Net CNN and Multimodal Remote Sensing Data: A Case Study of Makassar City 利用U-Net CNN和多模态遥感数据增强贫民窟制图：以望加锡市为例

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-21 DOI: 10.1109/LGRS.2025.3601167

Yohanes Fridolin Hestrio;Eduard Thomas Prakoso;Kiki Winda Veronica;Ika Siwi Supriyani;Destri Yanti Hutapea;Siti Desty Wahyuningsih;Nico Cendiana;Steward Augusto;Krisna Malik Sukarno;Olivia Maftukhaturrizqoh;Rubini Jusuf;Orbita Roswintiarti;Wisnu Jatmiko

{"title":"Enhanced Slum Mapping Through U-Net CNN and Multimodal Remote Sensing Data: A Case Study of Makassar City","authors":"Yohanes Fridolin Hestrio;Eduard Thomas Prakoso;Kiki Winda Veronica;Ika Siwi Supriyani;Destri Yanti Hutapea;Siti Desty Wahyuningsih;Nico Cendiana;Steward Augusto;Krisna Malik Sukarno;Olivia Maftukhaturrizqoh;Rubini Jusuf;Orbita Roswintiarti;Wisnu Jatmiko","doi":"10.1109/LGRS.2025.3601167","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3601167","url":null,"abstract":"Urban slums present critical challenges for sustainable development, particularly in rapidly urbanizing cities like Makassar, Indonesia. This study develops an automated slum mapping approach that integrates high-resolution SPOT-6/7 satellite imagery (1.5-m spatial resolution) with multimodal geospatial data using a U-Net convolutional neural network. Our methodology combines spectral and textural features from satellite imagery with nighttime light emissions, infrastructure proximity analysis, land use classifications, and socioeconomic indicators. The integrated approach achieves an overall accuracy of 97.1%–98.3% across both the datasets. However, slum-specific classification remains challenging with producer’s accuracy of 55.8%–59.1% and user’s accuracy of 22.9%–35.7%, yielding F1-scores of 0.33–0.43 for slum detection. Despite these limitations, the approach demonstrates significant enhancements over traditional census-based methods through automated processing, improved spatial resolution (1.5 m versus administrative units), and increased temporal frequency (annual versus decadal updates). The framework provides actionable insights for urban planning and social assistance targeting while establishing a foundation for automated slum monitoring system iterative improvement.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SpADANet: A Spatially Aware Domain Adaptation Network for Hurricane Damage Assessment SpADANet：用于飓风损害评估的空间感知域自适应网络

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-21 DOI: 10.1109/LGRS.2025.3601507

Pratyush V. Talreja;Surya S. Durbha

{"title":"SpADANet: A Spatially Aware Domain Adaptation Network for Hurricane Damage Assessment","authors":"Pratyush V. Talreja;Surya S. Durbha","doi":"10.1109/LGRS.2025.3601507","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3601507","url":null,"abstract":"Hurricanes cause significant damage to communities, necessitating rapid and accurate damage assessment to support timely disaster response. However, image-based deep learning models for hurricane-induced damage assessment face substantial challenges due to domain shifts across different hurricane events, and the restricted availability of labeled data for each disaster further complicates this task. In this study, we propose a novel domain-adaptive deep learning framework that mitigates the domain gap while requiring minimal labeled samples from the target domain. Our approach integrates a self-supervised learning (SSL) pretext task to enhance feature robustness and leverages a novel bilateral local Moran’s I (BLMI) module to improve spatial feature aggregation for damage localization. We evaluate our method using aerial datasets from Hurricanes Harvey, Matthew, and Michael. The experimental results demonstrate that our model achieves more than 5% improvement in damage classification accuracy over baseline methods. These findings highlight the potential of our approach for scalable and efficient hurricane damage assessment in real-world disaster scenarios.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CSA-RSIC: Cross-Modal Semantic Alignment for Remote Sensing Image Captioning 遥感图像标注的跨模态语义对齐

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-21 DOI: 10.1109/LGRS.2025.3601114

Kangda Cheng;Jinlong Liu;Rui Mao;Zhilu Wu;Erik Cambria

{"title":"CSA-RSIC: Cross-Modal Semantic Alignment for Remote Sensing Image Captioning","authors":"Kangda Cheng;Jinlong Liu;Rui Mao;Zhilu Wu;Erik Cambria","doi":"10.1109/LGRS.2025.3601114","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3601114","url":null,"abstract":"Remote sensing image captioning (RSIC) is an important task in environmental monitoring and disaster assessment. However, existing methods are constrained by redundant feature interference, insufficient multiscale feature integration, and cross-modal semantic gaps, leading to limited performance in scenarios requiring fine-grained descriptions and semantic integrity, such as disaster assessment and emergency response. In this letter, we propose a cross-modal semantic alignment model for RSIC (CSA-RSIC), addressing these challenges with three innovations. First, we designed an adaptive feature selection module (AFSM) that generates channel weights through dual pooling. The AFSM dynamically weights the most informative features at each scale to improve caption accuracy. Second, we propose a cross-scale feature aggregation module (CFAM) that constructs a hierarchical feature pyramid by aligning multiscale resolutions and performs attention-guided fusion with enhanced weighting via AFSM, ensuring the effective integration of fine-grained and global semantic information. Finally, a novel loss function that combines contrastive learning and consistency loss is proposed to enhance the semantic alignment between visual and textual features. Experiments on three datasets show the advancement of CSA-RSIC over strong baselines, indicating its effectiveness in enhancing both semantic completeness and accuracy.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sea-Undistort: A Dataset for Through-Water Image Restoration in High-Resolution Airborne Bathymetric Mapping sea - undistortion：高分辨率航空测深制图中通过水图像恢复的数据集

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-21 DOI: 10.1109/LGRS.2025.3601239

Maximilian Kromer;Panagiotis Agrafiotis;Begüm Demir

{"title":"Sea-Undistort: A Dataset for Through-Water Image Restoration in High-Resolution Airborne Bathymetric Mapping","authors":"Maximilian Kromer;Panagiotis Agrafiotis;Begüm Demir","doi":"10.1109/LGRS.2025.3601239","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3601239","url":null,"abstract":"Accurate image-based bathymetric mapping in shallow waters remains challenging due to the complex optical distortions, such as wave-induced patterns, scattering, and sunglint, introduced by the dynamic water surface, the water column properties, and solar illumination. In this work, we introduce Sea-Undistort, a comprehensive synthetic dataset of 1200 paired <inline-formula> <tex-math>$512times 512$ </tex-math></inline-formula> through-water scenes rendered in Blender. Each pair comprises a distortion-free and a distorted view, featuring realistic water effects, such as sun glint, waves, and scattering over diverse seabeds. Accompanied by per-image metadata, such as camera parameters, sun position, and average depth, Sea-Undistort enables supervised training that is otherwise infeasible in real environments. We use Sea-Undistort to benchmark two state-of-the-art image restoration methods alongside an enhanced lightweight diffusion-based framework with an early fusion sun-glint mask. When applied to real aerial data, the enhanced diffusion model delivers more complete digital surface models (DSMs) of the seabed, especially in deeper areas, reduces bathymetric errors, suppresses glint and scattering, and crisply restores fine seabed details. Dataset, weights, and code are publicly available at <uri>https://www.magicbathy.eu/Sea-Undistort.html</uri>.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11132387","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0