IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society最新文献_第9页

An Uneven Illumination and Radiometric Difference Removing Method for Multicamera Satellite Images 多相机卫星图像的光照不均匀及辐射差消除方法

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-16 DOI: 10.1109/LGRS.2025.3561699

Tao Peng;Ru Chen;Mi Wang

{"title":"An Uneven Illumination and Radiometric Difference Removing Method for Multicamera Satellite Images","authors":"Tao Peng;Ru Chen;Mi Wang","doi":"10.1109/LGRS.2025.3561699","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3561699","url":null,"abstract":"Relative radiometric calibration (RRC) mainly focuses on color consistency and streak levels between multicamera or multiple charge-coupled devices (CCDs), that is to say, full field-of-view (FOV), but RRC may not be conducted completely or that useful due to some factors, such as data quality or quantity in lifetime image statistics and ineffective side-slither RRC. Aimed to this, this letter proposes a novel approach to solve inner uneven illumination of each camera image and relative radiometric difference of multicamera images. The highest layer of unidirectional pyramid (UDP) is decomposed into illumination and reflectance components. Uneven phenomenon in this scale is eliminated in illumination component with column-by-column compensation processing strategy, and different scales of nonuniformity are removed together with UDP reconstruction. Radiometric variation of multicamera images is solved with iterative radiometric adjustment. Some typical data of HISEA-2 Multi-Spectral Scanner 1 (MSS-1) are used to validate the effectiveness of our method both in visual and quantitative terms.","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":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918627","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

Travel Time Computation in Snow and Ice Volumes for Radar Remote Sensing Applications 雷达遥感应用中冰雪体积的走时计算

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-16 DOI: 10.1109/LGRS.2025.3561654

Andreas Benedikter;Christian Huber;Letizia Gambacorta;Marc Rodriguez-Cassola;Gerhard Krieger

引用次数: 0

SRD-NET for Ground Crack Detection in Coal Mines Using UAV Images 基于无人机图像的SRD-NET煤矿地面裂缝检测

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-16 DOI: 10.1109/LGRS.2025.3561463

Hu Haibin;Guo Xinhui;Xiao Jie

{"title":"SRD-NET for Ground Crack Detection in Coal Mines Using UAV Images","authors":"Hu Haibin;Guo Xinhui;Xiao Jie","doi":"10.1109/LGRS.2025.3561463","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3561463","url":null,"abstract":"The large-scale coal exploitation causes numerous surface cracks in mining zones. These cracks endanger area safety, damage the ecological environment, and threaten local people’s lives. Traditional ground survey methods for crack detection are inefficient, costly, and limited, failing to meet monitoring demands. To address this, this study uses drone images and deep learning to identify ground cracks. An enhanced model, SRD-NET, based on U-NET, is proposed. It incorporates SE, DSC, and residual connections to improve crack feature recognition and generalization. Experimental results on a dataset of 400<inline-formula> <tex-math>$512times 512$ </tex-math></inline-formula>-pixel images collected from Huipodi Coal Mine, where 210 were for training, 60 for validation, and 30 for testing, demonstrate the model’s outstanding performance. Compared with U-NET, SRD-NET’s mPrecision is 5.6% higher, mRecall is 10.56% higher, mF1 is 7.16% higher, and mIoU is 7.14% higher. Against DSC-NET, SRD-NET’s mPrecision is 6.93% higher, mRecall is 11.46% higher, mF1 is 8.31% higher, and mIoU is 8.41% higher. When compared with residual network (Res-Net), SRD-NET’s mPrecision is 3.71% higher, mRecall is 9.00% higher, mF1 is 5.18% higher, and mIoU is 4.99% higher. Although SRD-NET’s mPrecision, mRecall, mF1, and mIoU are 0.38%, 0.15%, 1.37%, and 0.45% lower than SR-NET, respectively, SRD-NET’s FPS is 44 and 6 frames/s higher than SR-NET. Overall, SRD-NET improves the segmentation accuracy and has a relatively high processing speed, effectively demonstrating its efficacy in ground crack identification tasks.","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":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281332","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

DUSTNet: An Unsupervised and Noise-Resistant Network for Martian Dust Storm Change Detection DUSTNet：用于火星沙尘暴变化检测的无监督和抗噪声网络

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-16 DOI: 10.1109/LGRS.2025.3561365

Miyu Li;Junjie Li;Yumei Wang;Yu Liu;Haitao Xu

{"title":"DUSTNet: An Unsupervised and Noise-Resistant Network for Martian Dust Storm Change Detection","authors":"Miyu Li;Junjie Li;Yumei Wang;Yu Liu;Haitao Xu","doi":"10.1109/LGRS.2025.3561365","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3561365","url":null,"abstract":"Mars exploration highlights the demand for identifying Martian surface changes, which has sparked research interests in planetary surface changes detection (PSCD). However, the prevailing PSCD algorithms face significant challenges due to the sparse features, low resolution, and high noise levels of captured images data. In this letter, we propose an unsupervised model, the dust unsupervised surface tracking network (DUSTNet), designed to track the surface changes caused by Martian dust storms. Our DUSTNet employs a network architecture with dual input branches to learn the cross-temporal complementary information from pretime and posttime image pairs. A multilevel feature complementary fusion (MFCF) module is utilized to enhance the ability to detect subtle changes. Considering the difficulties in image registration caused by illumination variations, noise, and other factors, we design a noise-resistant module (NRM) that mitigates pseudo-changes and improves the robustness of PSCD. In addition, we construct a dataset of Martian dust storms change detection (CD) based on the images captured by moderate resolution imaging camera (MoRIC) of China’s First Mars Mission TianWen-1 (the dataset is available at <uri>https://github.com/Limiyu1123/SDS</uri>). The detection performance of DUSTNet performs well on multiple Mars surface datasets, including our Martian dust storm test set. Our model achieves improvements of 2.5% in precision, 7.55% in <inline-formula> <tex-math>$F1$ </tex-math></inline-formula>-score, 6.54% in overall accuracy (OA), and 4.57% in Kappa over the state-of-the-art model.","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":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888441","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

SCTNet: A Shallow CNN–Transformer Network With Statistics-Driven Modules for Cloud Detection

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-15 DOI: 10.1109/LGRS.2025.3561004

Weixing Liu;Bin Luo;Jun Liu;Han Nie;Xin Su

{"title":"SCTNet: A Shallow CNN–Transformer Network With Statistics-Driven Modules for Cloud Detection","authors":"Weixing Liu;Bin Luo;Jun Liu;Han Nie;Xin Su","doi":"10.1109/LGRS.2025.3561004","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3561004","url":null,"abstract":"Existing cloud detection methods often rely on deep neural networks, leading to excessive computational overhead. To address this, we propose a shallow convolutional neural network (CNN)–Transformer hybrid architecture that limits the maximum downsampling rate to <inline-formula> <tex-math>$8times $ </tex-math></inline-formula>. This design preserves local details while effectively capturing global context through a lightweight Transformer branch. To enhance adaptability across diverse cloud scenes, we introduce two novel statistics-driven modules: statistics-adaptive convolution (SAC) and statistical mixing augmentation (SMA). SAC dynamically generates convolutional kernels based on input feature statistics, enabling adaptive feature extraction for varying cloud patterns. SMA improves model generalization by interpolating channel-wise statistics across training samples, increasing feature diversity. Experiments on four datasets show that the proposed method achieves state-of-the-art performance with 732 K parameters and 1G multiply-accumulate operations (MACs). Our code will be available at <uri>https://weix-liu.github.io/</uri> for further research.","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":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943920","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

Cloud Removal Using Patch-Based Improved Denoising Diffusion Models and High Gray-Value Attention Mechanism 基于补丁的改进去噪扩散模型和高灰度值关注机制的云去除

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-15 DOI: 10.1109/LGRS.2025.3560799

Yingjie Huang;Famao Ye;Zewen Wang;Shufang Qiu;Leyang Wang

{"title":"Cloud Removal Using Patch-Based Improved Denoising Diffusion Models and High Gray-Value Attention Mechanism","authors":"Yingjie Huang;Famao Ye;Zewen Wang;Shufang Qiu;Leyang Wang","doi":"10.1109/LGRS.2025.3560799","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3560799","url":null,"abstract":"In recent years, diffusion-based methods have outperformed traditional models in many cloud removal tasks due to their strong generative capabilities. However, these methods face the challenges of long inference time and poor recovery effect in cloud regions. To address this issue, this letter proposes a patch-based improved denoising diffusion model with a high gray-value attention for cloud removal in optical remote sensing images. We introduce an overlapping fixed-sized patch method in the improved denoising diffusion model. The patch-based diffusion modeling approach enables size-agnostic image restoration by employing a guided denoising process with smoothed noise estimates across overlapping patches during inference. Additionally, we introduce a high gray-value attention module, specifically designed to focus on thick cloud regions, enhancing attention on areas with relatively high gray values within the image. When compared with other existing cloud removal models on the RICE dataset, our model outperformed them in terms of both peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index. Qualitative results demonstrate that the proposed method effectively removes clouds from images while preserving texture details. Ablation studies further confirm the effectiveness of the high gray-value attention module. Overall, the proposed model delivers superior cloud removal performance compared to existing state of the arts (SOTA) 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":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918629","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

DIP-MoG: Non-i.i.d. Seismic Noise Attenuation Using Mixture of Gaussians Noise Model and Deep Image Prior DIP-MoG: Non-i.i.d。基于高斯噪声模型和深度图像先验的地震噪声抑制

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-15 DOI: 10.1109/LGRS.2025.3560978

Yuqing Wang;Jiangjun Peng;Bangyu Wu;Bo Li

{"title":"DIP-MoG: Non-i.i.d. Seismic Noise Attenuation Using Mixture of Gaussians Noise Model and Deep Image Prior","authors":"Yuqing Wang;Jiangjun Peng;Bangyu Wu;Bo Li","doi":"10.1109/LGRS.2025.3560978","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3560978","url":null,"abstract":"Seismic data denoising is essential for subsequent inversion and interpretation tasks. However, most existing methods rely on loss functions, which assume that seismic noise follows an independent and identically distributed (i.i.d.) Gaussian distribution, which does not align with the characteristics of actual seismic noise. In this letter, we first analyze the principle of the <inline-formula> <tex-math>$L_{2}$ </tex-math></inline-formula>-norm loss function in suppressing i.i.d. Gaussian noise from the maximum a posteriori (MAP) perspective and then introduce the Mixture of Gaussians (MoGs) model to handle non-i.i.d. noise suppression. In addition, we optimize the MoG model using the expectation-maximization (EM) algorithm for improved performance. We propose a novel approach, DIP-MoG, which integrates the deep image prior (DIP) with the MoG model for enhanced denoising. To validate the performance of DIP-MoG, we conduct experiments on two synthetic datasets contaminated with an MoG noise and field noise, as well as a field seismic dataset. The results from both synthetic and field data demonstrate the superior denoising performance of DIP-MoG.","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":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896501","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

1-D Mirrored Aperture Synthesis Based on Artificial Magnetic Conductor 基于人工磁导体的一维镜像孔径合成

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-15 DOI: 10.1109/LGRS.2025.3561129

Rigeng Wu;Chengwang Xiao;Zhenyu Lei;Jian Dong;Yue Zhang

{"title":"1-D Mirrored Aperture Synthesis Based on Artificial Magnetic Conductor","authors":"Rigeng Wu;Chengwang Xiao;Zhenyu Lei;Jian Dong;Yue Zhang","doi":"10.1109/LGRS.2025.3561129","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3561129","url":null,"abstract":"In 1-D mirrored aperture synthesis (MAS), the antenna array arrangement and metal reflector are crucial in determining the rank of the transformation matrix. Accurate cosine visibility is achievable only when the transformation matrix is full rank. However, the anti-phase characteristic of the metal reflector introduces nonzero elements of “−1” into the matrix, leading to rank deficiency (RD). This letter proposes a method of using artificial magnetic conductor (AMC) with in-phase reflection property instead of metal reflector to ensure that the transformation matrix only contains nonzero elements “1.” Based on this property, the rank of both linear and nonlinear arrays is verified. The results indicate that AMC can effectively enhance the rank of the transformation matrix, potentially achieving full rank. Additionally, further verification is performed on the reconstruction of trapezoidal extended source scene using two types of arrays. The results demonstrate that AMC-based 1-D MAS can achieve a low root-mean-square error (RMSE), significantly improving the quality of the reconstructed 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":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875140","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

Underwater Sonar Image Targets Detection Based on Improved RT-DETR 基于改进RT-DETR的水下声纳图像目标检测

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-15 DOI: 10.1109/LGRS.2025.3560769

Ang Li;Raseeda Hamzah;Yousheng Gao

{"title":"Underwater Sonar Image Targets Detection Based on Improved RT-DETR","authors":"Ang Li;Raseeda Hamzah;Yousheng Gao","doi":"10.1109/LGRS.2025.3560769","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3560769","url":null,"abstract":"Underwater sonar imagery is characterized by small target sizes and low resolution, which can result in detection failures or false positives. To counteract these challenges, we introduce the underwater sonar detection transformer (US-DETR), an underwater sonar object detection model derived from the real-time detection transformer (RT-DETR) framework, incorporating attention-based feature fusion. US-DETR includes a novel enhanced feature interaction (EFI) module, which enhances the feature extraction network’s ability to perceive global information of the detected target. In addition, we propose a novel nonlocal attention feature fusion (NAFF) module to heighten the network’s sensitivity to the spatial relationships between feature channels across different scales, thereby enhancing its channel position and global information awareness. Experiments are conducted on a benchmark underwater sonar image dataset. Experimental results show that compared with RT-DETR, US-DETR achieves a 2.2% higher mean average precision (mAP) and a 2.1% higher <inline-formula> <tex-math>$F1$ </tex-math></inline-formula> score compared with RT-DETR. The model also strikes an effective balance between detection speed and accuracy, achieving real-time performance of 126 FPS, which can meet the real-time requirements in industrial production.","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":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892422","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

DFDNet: Deep Feature Decoupling for Oriented Object Detection 面向对象检测的深度特征解耦

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-14 DOI: 10.1109/LGRS.2025.3560388

Yuhan Sun;Shengyang Li

{"title":"DFDNet: Deep Feature Decoupling for Oriented Object Detection","authors":"Yuhan Sun;Shengyang Li","doi":"10.1109/LGRS.2025.3560388","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3560388","url":null,"abstract":"Objects in remote sensing images exhibit diverse orientations. Current oriented object detection (OOD) methods estimate the object angle by designing different loss functions and bounding box representations. However, these approaches do not account for the effects of coupling between rotation-equivariant and -invariant features on the regression of oriented bounding box (OBB) parameters. We manifest the problem in two aspects: 1) the coupling of parameters with different attributes. Current OOD methods overlook the inherent differences among features representing an object’s location, scale, and angle, making it challenging to accurately predict the OBB parameters with different attributes and 2) the coupling of object and background features. Conventional OOD methods apply convolution kernels uniformly across objects and background regions, leading to feature entanglement and degradation in detection performance. To address the above issues, we propose a deep feature decoupling network (DFDNet) to decouple the extracted features. Specifically, we propose parameter regression decoupling (PRD) to separate feature maps based on their attributes, subsequently assigning them to distinct branches for the OBB parameter regression. This approach ensures the decoupling of features related to an object’s location, shape, angle, and category. Additionally, to enhance the ability of OOD networks to differentiate between object and background features, we designed the mask reinforcement module (MRM), which is integrated into the PRD branches. The MRM dynamically adjusts the weights of object features, suppressing background interference and enhancing the distinction between object and background features. Extensive experiments conducted on the DOTA, HRSC2016, and UCAS-AOD datasets validate the effectiveness of DFDNet, demonstrating that it achieves state-of-the-art performance.","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":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879498","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