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

Semi-Supervised Triple-GAN With Similarity Constraint for Automatic Underground Object Classification Using Ground Penetrating Radar Data 基于相似约束的半监督三重gan探地雷达地下目标自动分类

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-09-12 DOI: 10.1109/LGRS.2025.3609444

Li Liu;Yongcheng Zhou;Hang Xu;Jingxia Li;Jianguo Zhang;Lijun Zhou;Bingjie Wang

{"title":"Semi-Supervised Triple-GAN With Similarity Constraint for Automatic Underground Object Classification Using Ground Penetrating Radar Data","authors":"Li Liu;Yongcheng Zhou;Hang Xu;Jingxia Li;Jianguo Zhang;Lijun Zhou;Bingjie Wang","doi":"10.1109/LGRS.2025.3609444","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3609444","url":null,"abstract":"Automatic underground object classification based on deep learning (DL) has been widely used in ground penetrating radar (GPR) fields. However, its excellent performance heavily depends on sufficient labeled training data. In GPR fields, large amounts of labeled data are difficult to obtain due to time-consuming and experience-dependent manual annotation work. To address the issue of limited labeled data, we propose a novel semi-supervised learning (SSL) method for urban-road underground multiclass object classification. It fully utilizes abundant unlabeled data and limited labeled data to enhance classification performance. We applied a variant of the triple-GAN (TGAN) model and modified it by introducing a similarity constraint, which is associated with GPR data geometric features and can help to produce high-quality generated images. Experimental results of laboratory and field data show that it has higher accuracy than representative baseline methods under limited labeled data.","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":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078645","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

Large-Scale Traveling Ionospheric Disturbances Over North America and Europe During the May 2024 Extreme Geomagnetic Storm 2024年5月极端地磁风暴期间北美和欧洲的大尺度电离层扰动

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-09-11 DOI: 10.1109/LGRS.2025.3608704

Long Tang;Hong Zhang;Yumei Li;Fan Xu;Fang Zou

{"title":"Large-Scale Traveling Ionospheric Disturbances Over North America and Europe During the May 2024 Extreme Geomagnetic Storm","authors":"Long Tang;Hong Zhang;Yumei Li;Fan Xu;Fang Zou","doi":"10.1109/LGRS.2025.3608704","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3608704","url":null,"abstract":"This study investigates the large-scale ionospheric traveling disturbances (LSTIDs) over North America and Europe associated with the intense geomagnetic storm in May 2024, utilizing total electron content (TEC) data derived from ground-based Global Navigation Satellite System (GNSS) stations. The findings reveal that the observed LSTIDs in both regions exhibited an unusually prolonged duration, lasting for over 10 h from 17:00 UT on May 10 to 03:30 UT on May 11, 2024. This extended duration may be attributed to the continuous triggering of LSTIDs by auroral energy input during the geomagnetic storm. Additionally, significant differences in propagation characteristics, including velocities, azimuths, wavelengths, and traveling distances of LSTIDs, were observed between the two regions. These disparities in LSTID parameters are likely due to variations in the magnitude of energy input in the polar regions and local time differences in North America (14:00 LT) and Europe (19:00 LT), which cause diurnal electron-density contrast to influence LSTID propagation.","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":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090175","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

KD-RSCC: A Karras Diffusion Framework for Efficient Remote Sensing Change Captioning KD-RSCC：一种高效遥感变化标注的Karras扩散框架

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-09-10 DOI: 10.1109/LGRS.2025.3608489

Xiaofei Yu;Jie Ma;Liqiang Qiao

{"title":"KD-RSCC: A Karras Diffusion Framework for Efficient Remote Sensing Change Captioning","authors":"Xiaofei Yu;Jie Ma;Liqiang Qiao","doi":"10.1109/LGRS.2025.3608489","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3608489","url":null,"abstract":"Remote sensing image change captioning (RSICC) is a challenging task that involves describing surface changes between bitemporal or multitemporal satellite images using natural language. This task requires both fine-grained visual understanding and expressive language generation. Transformer-based and long short-term memory (LSTM)-based models have shown promising results in this domain. However, they may encounter difficulties in generating flexible and diverse captions, particularly when training data are limited or imbalanced. While diffusion models provide richer textual outputs, they are often constrained by long inference times. To address these issues, we propose a novel diffusion-based framework, KD-RSCC, for efficient and expressive remote sensing change captioning. This framework utilizes the Karras sampling method to significantly reduce the number of steps required during inference, while preserving the quality and diversity of the generated captions. In addition, we introduce a large language model (LLM)-based evaluation strategy <inline-formula> <tex-math>$text {G-Eval}_{text {RSCC}}$ </tex-math></inline-formula> to conduct a more comprehensive assessment of the semantic accuracy, fluency, and linguistic diversity of the generated descriptions. Experimental results demonstrate that KD-RSCC achieves an optimal balance between generation quality and inference speed, enhancing the flexibility and readability of its outputs. The code and supplementary materials are available at <uri>https://github.com/Fay-Y/KD_RSCC</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":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090174","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

RoGLSNet: An Efficient Global–Local Scene Awareness Network With Rotary Position Embedding for Remote Image Segmentation RoGLSNet：基于旋转位置嵌入的高效全局-局部场景感知网络

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-09-09 DOI: 10.1109/LGRS.2025.3607840

Xiaosheng Yu;Weiqi Bai;Jubo Chen;Jiawei Huang;Zhuoqun Fang;Zhaokui Li

{"title":"RoGLSNet: An Efficient Global–Local Scene Awareness Network With Rotary Position Embedding for Remote Image Segmentation","authors":"Xiaosheng Yu;Weiqi Bai;Jubo Chen;Jiawei Huang;Zhuoqun Fang;Zhaokui Li","doi":"10.1109/LGRS.2025.3607840","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3607840","url":null,"abstract":"Accurate segmentation of very high-resolution remote sensing images is vital for downstream tasks. Most semantic segmentation methods fail to fully consider the inherent characteristics of the images, such as intricate backgrounds, significant intraclass variance, and spatial interdependence of geographic object distribution. To address these challenges, we propose an efficient global–local scene awareness network with rotary position embedding (RoGLSNet). Specifically, we introduce the dynamic global filter (DGF) module to adaptively select frequency components, thereby mitigating interference from background noise. For high intraclass variance, the class center aware block (CCAB) performs class-level contextual modeling with spatial information integration. Additionally, the rotary position embedding (RoPE) is incorporated into vanilla attention to indirectly model the positional and distance relationships of geographic target objects. Extensive experimental results on two widely used datasets demonstrate that RoGLSNet outperforms the state-of-the-art (SOTA) segmentation methods. The code is available at <uri>https://github.com/bai101315/RoGLSNet</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":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073326","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

Three-Dimensional Controlled-Source Electromagnetic Modeling Using Octree-Based Spectral Element Method 基于八叉树的三维可控源电磁建模方法

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-09-08 DOI: 10.1109/LGRS.2025.3606934

Jintong Xu;Xiao Xiao;Jingtian Tang

引用次数: 0

Fluid Mobility Attribute Extraction Based on Optimized Second-Order Synchroextracting Wavelet Transform 基于优化二阶同步提取小波变换的流体流动性属性提取

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-09-08 DOI: 10.1109/LGRS.2025.3607097

Yu Wang;Xiao Pan;Kang Shao;Ning Wang;Yuqiang Zhang;Xinyu Zhang;Chaoyang Lei;Xiaotao Wen

引用次数: 0

AFIMNet: An Adaptive Feature Interaction Network for Remote Sensing Scene Classification AFIMNet：用于遥感场景分类的自适应特征交互网络

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-09-08 DOI: 10.1109/LGRS.2025.3607205

Xiao Wang;Yisha Sun;Pan He

{"title":"AFIMNet: An Adaptive Feature Interaction Network for Remote Sensing Scene Classification","authors":"Xiao Wang;Yisha Sun;Pan He","doi":"10.1109/LGRS.2025.3607205","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3607205","url":null,"abstract":"Convolutional neural network (CNN)-based methods have been widely applied in remote sensing scene classification (RSSC) and have achieved remarkable classification results. However, traditional CNN methods have certain limitations in extracting global features and capturing image semantics, especially in complex remote sensing (RS) image scenes. The Transformer can directly capture global features through the self-attention mechanism, but its performance is weaker when handling local details. Currently, methods that directly combine CNN and transformer features lead to feature imbalance and introduce redundant information. To address these issues, we propose AFIMNet, an adaptive feature interaction network for RSSC. First, we use a dual-branch network structure (based on ResNet34 and Swin-S) to extract local and global features from RS scene images. Second, we design an adaptive feature interaction module (AFIM) that effectively enhances the interaction and correlation between local and global features. Third, we use a spatial-channel fusion module (SCFM) to aggregate the interacted features, further strengthening feature representation capabilities. Our proposed method is validated on three public RS datasets, and experimental results show that AFIMNet has a stronger feature representation ability compared to current popular RS image classification methods, significantly improving classification accuracy. The source code will be publicly accessible at <uri>https://github.com/xavi276310/AFIMNet</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":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061905","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

SADFF-Net: Scale-Aware Detection and Feature Fusion for Multiscale Remote Sensing Object Detection 基于尺度感知的多尺度遥感目标检测与特征融合

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-09-05 DOI: 10.1109/LGRS.2025.3606521

Runbo Yang;Huiyan Han;Shanyuan Bai;Yaming Cao

{"title":"SADFF-Net: Scale-Aware Detection and Feature Fusion for Multiscale Remote Sensing Object Detection","authors":"Runbo Yang;Huiyan Han;Shanyuan Bai;Yaming Cao","doi":"10.1109/LGRS.2025.3606521","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3606521","url":null,"abstract":"Multiscale object detection in remote sensing imagery poses significant challenges, including substantial variations in object size, diverse orientations, and interference from complex backgrounds. To address these issues, we propose a scale-aware detection and feature fusion network (SADFF-Net), a novel detection framework that incorporates a Multiscale contextual attention fusion (MCAF) module to enhance information exchange between feature layers and suppress irrelevant feature interference. In addition, SADFF-Net employs an adaptive spatial feature fusion (ASFF) module to improve semantic consistency across feature layers by assigning spatial weights at multiple scales. To enhance adaptability to scale variations, the regression head integrates a deformable convolution. In contrast, the classification head utilizes depth-wise separable convolutions to significantly reduce computational complexity without compromising detection accuracy. Extensive experiments on the DOTAv1 and DIOR_R datasets demonstrate that SADFF-Net outperforms current state-of-the-art methods in Multiscale object detection.","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":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036868","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

Semantic Change Detection of Bitemporal Remote Sensing Images Using Frequency Feature Enhancement 基于频率特征增强的双时相遥感图像语义变化检测

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-09-04 DOI: 10.1109/LGRS.2025.3605910

Renfang Wang;Kun Yang;Feng Wang;Hong Qiu;Yingying Huang;Xiufeng Liu

引用次数: 0

LSAR-Det: A Lightweight YOLOv11-Based Model for Ship Detection in SAR Images 基于yolov11的轻型SAR图像舰船检测模型

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-09-04 DOI: 10.1109/LGRS.2025.3605993

Pengxiong Zhang;Yi Jiang;Xinguo Zhu

{"title":"LSAR-Det: A Lightweight YOLOv11-Based Model for Ship Detection in SAR Images","authors":"Pengxiong Zhang;Yi Jiang;Xinguo Zhu","doi":"10.1109/LGRS.2025.3605993","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3605993","url":null,"abstract":"Due to its superior recognition accuracy, deep learning has been widely adopted in synthetic aperture radar (SAR) ship detection. Nevertheless, significant variations in ship target scales pose challenges for existing detection architectures, frequently leading to missed detections or false positives. Moreover, high-precision detection models are typically structurally complex and computationally intensive, resulting in substantial hardware resource consumption. In this letter, we introduce LSAR-Det, a novel SAR ship detection network designed to address these challenges. We propose a lightweight residual feature extraction (LRFE) module to construct the backbone network, enhancing feature extraction capabilities while reducing the number of parameters and floating-point operations per second (FLOPs). Furthermore, we design a lightweight cross-space convolution (LCSConv) module to replace the traditional convolution in the neck network. In addition, we incorporate a multiscale bidirectional feature pyramid network (M-BiFPN) to facilitate multiscale feature fusion with fewer parameters. Our proposed model contains merely 0.985M parameters and requires only 3.3G FLOPs. Experimental results on the SAR ship detection dataset (SSDD) and high-resolution SAR image dataset (HRSID) datasets demonstrate that LSAR-Det outperforms other models, achieving detection accuracies of 98.2% and 91.8%, respectively, thereby effectively balancing detection performance and model efficiency.","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":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061895","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