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

CPL-PL: Contrapositive Learning-Based Pseudo-Labeling for Semi-Supervised Scene Classification in Remote Sensing Images 基于对置学习的遥感图像半监督场景分类伪标记

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-26 DOI: 10.1109/LGRS.2025.3583475

G. Swetha;Rajeshreddy Datla;Sobhan Babu;C. Krishna Mohan

{"title":"CPL-PL: Contrapositive Learning-Based Pseudo-Labeling for Semi-Supervised Scene Classification in Remote Sensing Images","authors":"G. Swetha;Rajeshreddy Datla;Sobhan Babu;C. Krishna Mohan","doi":"10.1109/LGRS.2025.3583475","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3583475","url":null,"abstract":"Scene classification in remote sensing (RS) images is a challenging task due to the limited availability of labeled data and the high intraclass variability in complex landscapes. Semi-supervised learning (SSL) has emerged as an effective approach to leverage the limited labeled data in utilizing a large amount of unlabeled data for improved classification. Pseudo-labeling (PL), a widely used SSL technique, determines suitable labels to unlabeled data based on high-confidence model predictions. However, traditional PL methods suffer from confirmation bias, where incorrect labels reinforce errors, degrading model performance. To address this, we propose contrapositive learning-based PL (CPL-PL), a novel method designed specifically for RS scene classification. CPL-PL introduces a contrapositive loss (CPLoss) that enforces feature consistency for similar scenes while ensuring representation separation for dissimilar ones, leading to more reliable pseudo-label assignments. Our approach mitigates pseudo-label noise, enhances feature discrimination, and improves classification robustness. Experimental results on benchmark RS datasets demonstrate that CPL-PL significantly outperforms conventional PL strategies, especially in low-label regimes. The proposed method provides a promising direction for advancing semi-supervised scene classification in RS images.","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":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563387","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

An Attention Architecture With Twice Attention Convolution and Simplified Transformer for Hyperspectral Image Classification 基于二次注意卷积和简化变换的高光谱图像分类注意结构

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-26 DOI: 10.1109/LGRS.2025.3583576

Xuejiao Liao;Fangyuan Lei;Xun Liu;Li Guo;Alex Hay-Man Ng;Jinchang Ren

{"title":"An Attention Architecture With Twice Attention Convolution and Simplified Transformer for Hyperspectral Image Classification","authors":"Xuejiao Liao;Fangyuan Lei;Xun Liu;Li Guo;Alex Hay-Man Ng;Jinchang Ren","doi":"10.1109/LGRS.2025.3583576","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3583576","url":null,"abstract":"Convolutional neural network (CNN) and transformer-based hybrid models have been successfully applied to hyperspectral image (HSI) classification, enhancing the local feature extraction capability of single transformer-based models. However, these transformers in the hybrid models suffer from structural redundancy in components such as positional encoding (PE) and multilayer perceptron (MLP). To address the issue, we propose a novel attention architecture termed twice attention convolution module and simplified transformer (TAST) for HSI classification. The proposed TAST primarily consists of a twice attention convolution module (TACM) and a simplified transformer (ST). TACM is designed to improve the ability to extract local features. In addition, we introduce the ST by removing the PE and MLP components from the original transformer, which captures long-range dependencies while simplifying the structure of the original transformer. Experimental results on four public datasets demonstrate that the proposed TAST model outperforms both state-of-the-art CNN and transformer models in terms of classification performance, with improvements in terms of overall accuracy (OA) around 3.87%–34.95% (Indian Pines), 0.35%–23.43% (Salinas), 0.37%–6.05% (WHU-Hi-LongKou), and 0.65%–10.79% (WHU-Hi-HongHu).","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":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557785","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

InverDiff: Seismic Impedance Inversion Using a Deep Diffusion Model 用深扩散模型反演地震阻抗

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-25 DOI: 10.1109/LGRS.2025.3582929

Xiaofang Liao;Junxing Cao

引用次数: 0

An Adaptive Error Correction Method for InSAR Data Processing Guided by Deformation Prior Information 基于形变先验信息的InSAR数据处理自适应误差校正方法

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-25 DOI: 10.1109/LGRS.2025.3582950

Yilin Wang;Guangcai Feng;Haipeng Guo;Yunlong Wang;Zhiqiang Xiong;Hongbo Jiang

{"title":"An Adaptive Error Correction Method for InSAR Data Processing Guided by Deformation Prior Information","authors":"Yilin Wang;Guangcai Feng;Haipeng Guo;Yunlong Wang;Zhiqiang Xiong;Hongbo Jiang","doi":"10.1109/LGRS.2025.3582950","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3582950","url":null,"abstract":"Interferometric synthetic aperture radar (InSAR) is a crucial technology for monitoring large-scale surface deformation. Recent advancements have increasingly emphasized the automation of InSAR data processing. However, terrain complexity, environmental variability, and diverse deformation patterns in wide-area monitoring introduce multiple error sources. Conventional correction models based on singular assumptions struggle to achieve adaptive processing, often resulting in low processing efficiency and distortion of some deformation results. To address these challenges, this study proposes an adaptive error correction method guided by deformation prior information, enhancing automated workflows for wide-area InSAR processing. By integrating prior deformation and terrain information to create mask files, this method adaptively distinguishes deformation signals from error components, enabling precise error correction. A case study conducted in the North China Plain (NCP) demonstrates the method’s adaptive error-correction capabilities. Experimental results indicate that the proposed method achieves robust error separation while maintaining solution accuracy across deformation scales from regional subsidence to localized deformations. This method provides novel algorithmic support for automated InSAR data processing in wide-area applications, significantly improving processing efficiency and result reliability.","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":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557807","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

RFWNet: A Lightweight Remote Sensing Object Detector Integrating Multiscale Receptive Fields and Foreground Focus Mechanism RFWNet：一种集成多尺度感受场和前景聚焦机制的轻型遥感目标探测器

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-23 DOI: 10.1109/LGRS.2025.3582337

Yujie Lei;Wenjie Sun;Sen Jia;Qingquan Li;Jie Zhang

{"title":"RFWNet: A Lightweight Remote Sensing Object Detector Integrating Multiscale Receptive Fields and Foreground Focus Mechanism","authors":"Yujie Lei;Wenjie Sun;Sen Jia;Qingquan Li;Jie Zhang","doi":"10.1109/LGRS.2025.3582337","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3582337","url":null,"abstract":"Challenges in remote sensing object detection (RSOD), such as high interclass similarity, imbalanced foreground–background distribution, and the small size of objects in remote sensing images, significantly hinder detection accuracy. Moreover, the tradeoff between model accuracy and computational complexity poses additional constraints on the application of RSOD algorithms. To address these issues, this study proposes an efficient and lightweight RSOD algorithm integrating multiscale receptive fields and foreground focus mechanism, named robust foreground weighted network (RFWNet). Specifically, we proposed a lightweight backbone network receptive field adaptive selection network (RFASNet), leveraging the rich context information of remote sensing images to enhance class separability. Additionally, we developed a foreground–background separation module (FBSM) consisting of a background redundant information filtering module (BRIFM) and a foreground information enhancement module (FIEM) to emphasize critical regions within images while filtering redundant background information. Finally, we designed a loss function, the weighted CIoU-Wasserstein loss (<inline-formula> <tex-math>$L_{text {WCW}}$ </tex-math></inline-formula>), which weights the IoU-based loss by using the normalized Wasserstein distance to mitigate model sensitivity to small object position deviations. The comprehensive experimental results demonstrate that RFWNet achieved 95.3% and 73.2% mean average precision (mAP) with 6.0 M parameters on the DOTA V1.0 and NWPU VHR-10 datasets, respectively, with an inference speed of 52 FPS.","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":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536366","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

Quantifying Heterogeneity in SAR Imagery With the Rényi Entropy 利用rsamnyi熵量化SAR图像的异质性

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-20 DOI: 10.1109/LGRS.2025.3581855

Janeth Alpala;Abraão D. C. Nascimento;Alejandro C. Frery

引用次数: 0

FDSANet: Seismic Data Reconstruction Based on a Frequency-Domain Self-Attention Network 基于频域自关注网络的地震数据重构

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-19 DOI: 10.1109/LGRS.2025.3581375

Yuting Mu;Changpeng Wang;Xin Geng;Chunxia Zhang;Jiangshe Zhang

{"title":"FDSANet: Seismic Data Reconstruction Based on a Frequency-Domain Self-Attention Network","authors":"Yuting Mu;Changpeng Wang;Xin Geng;Chunxia Zhang;Jiangshe Zhang","doi":"10.1109/LGRS.2025.3581375","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3581375","url":null,"abstract":"The seismic data reconstruction is a crucial step in seismic data processing. Most existing methods reconstruct seismic data in the spatial domain, often ignoring some important frequency components in the frequency domain, such as high-frequency texture features. Therefore, we propose a frequency-domain self-attention network (FDSANet) to effectively reconstruct seismic data with a high missing rate. The wavelet transform is employed in this model to better restore weak signals and provide more information at different resolutions. The fast Fourier transform in the frequency-domain self-attention module (FDSAM) enhances the global frequency awareness, especially for high-frequency energy. Different frequency components are elementwise multiplied by dynamic weights, effectively suppressing energy leakage and aliasing. Moreover, the nearest neighbor similarity loss on adjacent shot gathers is incorporated into the loss function to learn information from neighboring shot gathers, further enhancing the reconstruction performance of our model. Experiments on both synthetic and field datasets demonstrate that FDSANet achieves significant improvement over several 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":0.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481914","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

VPGCD-Net: A Visual Prompt-Driven Network for Polar Glacier Change Detection in Remote Sensing Imagery VPGCD-Net：极地冰川变化遥感影像的视觉驱动网络

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-19 DOI: 10.1109/LGRS.2025.3581221

Jianming Cui;Zhishen Shi;Xiaohan Chen;Jianzhi Yu;Binge Cui

{"title":"VPGCD-Net: A Visual Prompt-Driven Network for Polar Glacier Change Detection in Remote Sensing Imagery","authors":"Jianming Cui;Zhishen Shi;Xiaohan Chen;Jianzhi Yu;Binge Cui","doi":"10.1109/LGRS.2025.3581221","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3581221","url":null,"abstract":"Monitoring glacier changes is essential for understanding global climate dynamics and assessing their environmental impacts. However, accurate detection remains challenging due to seasonal variations, illumination differences, and heterogeneous textures in remote sensing imagery. To address these issues, we propose VPGCD-Net, a transformer-based dual-branch network that achieves robust glacier change detection through visual prompt engineering. The visual prompting branch integrates threshold segmentation and difference calculation, leveraging a visual prompt transformer (VPT) to encode regions of significant change and generate high-level semantic prompts. Meanwhile, the change detection branch adopts ResNet18 as the backbone to extract dual-temporal features, followed by a transformer module for modeling global spatiotemporal dependencies and a feature-wise linear modulation (FiLM) module for adaptive feature modulation to emphasize real change regions. Complementing the method, we introduce the first polar-glacier-focused dataset specifically designed for deep-learning-based glacier change detection in remote sensing. Experimental results demonstrate that VPGCD-Net outperforms existing state-of-the-art methods, achieving superior accuracy even under complex conditions such as shadow interference. The dataset is publicly available at <uri>https://huggingface.co/datasets/cuibinge/Glacier-Dataset</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":0.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144514463","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

Virtual Channel-Based Split-Window Algorithm for Landsat-8 Land Surface Temperature Retrieval 基于虚拟信道的Landsat-8地表温度反演分窗算法

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-18 DOI: 10.1109/LGRS.2025.3580673

Junli Zhao;Wei Zhao;Bo-Hui Tang;Yanqing Yang;Jiujiang Wu

{"title":"Virtual Channel-Based Split-Window Algorithm for Landsat-8 Land Surface Temperature Retrieval","authors":"Junli Zhao;Wei Zhao;Bo-Hui Tang;Yanqing Yang;Jiujiang Wu","doi":"10.1109/LGRS.2025.3580673","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3580673","url":null,"abstract":"As a key driving factor of land-atmosphere system, land surface temperature (LST) is widely applied in geoscience studies across various fields. Among numerous LST retrieval methods, the split-window (SW) algorithm has been widely used because of its advantage of free of atmospheric profile data. However, some satellites provide only one single available thermal infrared (TIR) channel, which limits the direct application of the SW algorithm. To overcome this shortcoming, this study takes Landsat-8 as an example, whose TIR Channel-11 is affected by degraded calibration accuracy caused by stray light and develops a method to construct a virtual channel using MODIS TIR data, enabling the application of the SW algorithm to Landsat-8 data for LST retrieval. During the construction, the angular normalization is adopted to the MODIS TIR data in advance. The validation results derived from the simulated dataset show that the RMSE of LST retrieval based on the virtual channel using the SW method is less than 1.2 K. Further validation with ground-based measurements from the FPK station results in an RMSE of 2.44 K, demonstrating better accuracy than the result from single channel (SC) algorithm. Moreover, the angular normalization applied to MODIS data leads to an improvement of 0.36 K in LST retrieval accuracy. The results demonstrate the advantages of LST retrieval from Landsat-8 data with virtual channel and extend the applicability of the SW algorithm.","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":0.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502852","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

Multiscale Channel Attention and Cross-Layer Fusion Network for Infrared Small Target Detection 红外小目标检测的多尺度通道关注和跨层融合网络

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-18 DOI: 10.1109/LGRS.2025.3580709

Shengli Zhou;Tong Liu;Xiaolu Guo;Meibo Lv

{"title":"Multiscale Channel Attention and Cross-Layer Fusion Network for Infrared Small Target Detection","authors":"Shengli Zhou;Tong Liu;Xiaolu Guo;Meibo Lv","doi":"10.1109/LGRS.2025.3580709","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3580709","url":null,"abstract":"Infrared small target detection (IRSTD) faces challenges due to limited global perception and feature ambiguity in complex scenarios. To address these issues, we propose a novel multiscale channel attention and cross-layer fusion network (MACFNet). The framework integrates three key innovations: 1)the feature convolution attention transformer (FCAT) addresses limited global perception by combining local features and global contexts to enhance target representation; 2) the efficient channel and spatial attention (ECSA) module resolves feature ambiguity by optimizing discriminative feature weighting; and 3) an enhanced M-UNet architecture incorporates channelwise cross fusion transformer (CCT) modules to enable effective cross-scale semantic alignment. Extensive experiments on the SIRST and NUDT-SIRST datasets demonstrate the state-of-the-art performance, achieving significantly higher IoU of 0.8396 and 0.9346, respectively, surpassing the existing model-driven and data-driven methods while maintaining a real-time capable inference speed of 29.7167 FPS.","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":0.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536374","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