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

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

Toward Point Cloud Density Consistency in Mobile Laser Scanning: A Mathematical Modeling and Correction Method 移动激光扫描中点云密度一致性的数学建模与校正方法

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.2025.3580577

Kai Tan;Shu Zhang;Shuai Liu

{"title":"Toward Point Cloud Density Consistency in Mobile Laser Scanning: A Mathematical Modeling and Correction Method","authors":"Kai Tan;Shu Zhang;Shuai Liu","doi":"10.1109/LGRS.2025.3580577","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3580577","url":null,"abstract":"The mobile laser scanning (MLS) systems enable rapid acquisition of high-definition 3-D point clouds for urban digitization, topographic mapping, and infrastructure inspection. Despite the critical role of point cloud density in quantifying data fidelity and object discriminability, its inherent spatiotemporal variability—arising from the nonlinear interplay of scanning geometry, platform dynamics, and surface topology—has remained inadequately addressed in current metrological frameworks. This study establishes a rigorous mathematical model that quantifies MLS density variations through the interdependent variables: density search radius, scanning distance, angular resolution, platform velocity, pulse repetition frequency, and three angles defining the spatial orientation of the local infinitesimal plane at the target point. Building upon this formulation, we propose the first MLS point cloud density correction method to mitigate heterogeneity caused by varying influencing factors and to derive a new corrected density value for each point that serves as an indicator of target geometry attribute. Experiments conducted across different platforms and environments demonstrate that the proposed method effectively eliminates inhomogeneity in density. The correction procedure achieves an average 61% decrease in the density coefficient of variation (cv) over homogeneous surfaces. The proposed method exhibits strong performance regarding feasibility and generality, offering significant application value in enhancing MLS data interpretation and understanding spatial distribution patterns of point clouds under various circumstances.","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":"144472491","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

Attention U-Mamba: A Simple and Efficient Method for Landslide Segmentation U-Mamba：一种简单有效的滑坡分割方法

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-17 DOI: 10.1109/LGRS.2025.3580565

Yushuang Fu;Hao Zhong;Chengyong Fang

引用次数: 0

Unsupervised Seismic Erratic Noise Suppression Using Implicit Neural Representation 基于隐式神经网络表征的无监督地震不稳定噪声抑制

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-17 DOI: 10.1109/LGRS.2025.3580648

Qianzong Bao;Weiwei Xu;Wei Shi;Ji Li;Xiaokai Wang;Wenchao Chen

{"title":"Unsupervised Seismic Erratic Noise Suppression Using Implicit Neural Representation","authors":"Qianzong Bao;Weiwei Xu;Wei Shi;Ji Li;Xiaokai Wang;Wenchao Chen","doi":"10.1109/LGRS.2025.3580648","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3580648","url":null,"abstract":"Seismic erratic noise, characterized by large isolated events following non-Gaussian distributions, significantly degrades seismic data quality by masking useful signals. Methods based on conventional priors remain essential but face inherent challenges as they struggle to balance noise attenuation and signal preservation. Supervised deep learning approaches are constrained by the scarcity of high-quality labeled training pairs while existing unsupervised techniques often suffer from suboptimal accuracy and high-computational cost. To address these limitations, we propose an unsupervised deep learning framework based on implicit neural representation (INR) for erratic noise suppression in seismic data. The proposed method employs Fourier feature mapping to encode the spatial coordinates of noisy seismic data, which are then processed by a lightweight multilayer perceptron (MLP). The MLP is optimized using a robust Huber loss function to learn a continuous representation of the underlying seismic wavefield, effectively attenuating erratic noise while preserving valuable signal components. The Fourier feature mapping enhances the MLP’s ability to capture high-frequency signal details, while the Huber loss adaptively weights residuals based on amplitude, enabling precise noise suppression. Experimental results on synthetic and field datasets demonstrate its superior performance in suppressing noise while preserving signal fidelity.","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":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472557","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

A Lightweight Forest Fire Detection Method Based on UAV Dual-Modal Images 一种基于无人机双峰图像的轻型森林火灾探测方法

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-17 DOI: 10.1109/LGRS.2025.3580564

Lingxia Mu;Yichi Yang;Youmin Zhang;Xianghong Xue;Nan Feng

{"title":"A Lightweight Forest Fire Detection Method Based on UAV Dual-Modal Images","authors":"Lingxia Mu;Yichi Yang;Youmin Zhang;Xianghong Xue;Nan Feng","doi":"10.1109/LGRS.2025.3580564","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3580564","url":null,"abstract":"This letter presents a lightweight method for detecting forest fires using dual-modal remote sensing images captured by an uncrewed aerial vehicle (UAV). The aim is to achieve efficient fire monitoring on a computationally resource-constrained UAV platform. The proposed detection network is based on the improved YOLOv8, which uses RGB image and thermal image as network input at the same time. A lightweight dual-modal feature fusion module named dual-modal fusion module (DFM) is designed to effectively combine RGB and thermal features. The existing C2f module in YOLOv8 was replaced by the lightweight module C2f-F, along with the addition of the parameter-free attention module SimAM. This improvement improves the detection performance of the model while minimizing the model parameters. The evaluation experimental results on the FLAME 2 dataset show that the accuracy of the proposed dual-modal forest fire detection method reaches 98.4%, and the model size is only 2.9 MB, which achieves a good balance between accuracy and number of parameters compared with other mainstream methods. In addition, on the iCrest 2-s edge computing device, the detection speed reaches 20.67 frames per second (FPS), further confirming that this lightweight approach satisfies the real-time detection requirements for forest fires.","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":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472560","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

Mitigating the Ground-Based Radar Interference Efficiently Based on an Autoregressive Model With Optimal Order 基于最优阶自回归模型有效抑制地基雷达干扰

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-17 DOI: 10.1109/LGRS.2025.3580550

Xiaojie Bao;Guohua Wei;Jinlong Ren;Jiahao Bai

{"title":"Mitigating the Ground-Based Radar Interference Efficiently Based on an Autoregressive Model With Optimal Order","authors":"Xiaojie Bao;Guohua Wei;Jinlong Ren;Jiahao Bai","doi":"10.1109/LGRS.2025.3580550","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3580550","url":null,"abstract":"In this letter, we address the interference mitigation problem caused by ground-based space surveillance radars on spaceborne debris monitoring radars. Suppression-based methods often involve high computational complexity, which limits their applicability in scenarios requiring rapid response, such as space debris detection. In contrast, autoregressive (AR) model-based reconstruction offers faster processing but suffers from performance degradation when the model order is selected solely by information criteria. Moreover, AR-based reconstruction in a single direction (either forward or backward) accumulates estimation errors over time, resulting in increasing deviations from the true signal. To overcome these challenges, we propose an interference mitigation method based on an AR model with optimal order, which includes the following key steps: interference detection, bidirectional signal reconstruction with candidate orders, weighted fusion of forward and backward reconstruction results to enhance reconstruction accuracy, and optimal order selection based on the signal-to-interference-plus-noise ratio (SINR) after coherent accumulation. Experimental results demonstrate superior interference mitigation and computational efficiency of the proposed approach.","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":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472562","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

SAR Image Simulation of Steering Kelvin Wake on Sea Surface With Breaking Waves 海面破碎浪上转向开尔文尾流的SAR图像模拟

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-17 DOI: 10.1109/LGRS.2025.3580563

Meng-Qing Wang;Peng-Ju Yang;Rui Wu

引用次数: 0

Determination of the Optimal Channel Configuration for Land Surface Temperature Retrieval Using Split Window Algorithm 利用分割窗算法确定地表温度检索的最优通道配置

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-13 DOI: 10.1109/LGRS.2025.3579608

Youying Guo;Xiaopo Zheng;Zhongliang Zhou;Dahui Li;Zhenyu Wang

{"title":"Determination of the Optimal Channel Configuration for Land Surface Temperature Retrieval Using Split Window Algorithm","authors":"Youying Guo;Xiaopo Zheng;Zhongliang Zhou;Dahui Li;Zhenyu Wang","doi":"10.1109/LGRS.2025.3579608","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3579608","url":null,"abstract":"Currently, various algorithms have been developed to retrieve regional and global land surface temperature (LST) from satellite thermal infrared (TIR) observations, among which the split window (SW) algorithm is the most widely used one. However, the LST retrieval accuracy would be affected by the channel centers and channel widths owing to the vast atmospheric conditions and land surface types around the world. The theoretical channel configuration leading to the best performance of the SW algorithm is still not well investigated currently. In this study, the LST retrieval accuracies of the SW algorithm using different channel configurations were studied iteratively through the whole TIR atmospheric window. Consequently, the two channels centered at 10.3 and <inline-formula> <tex-math>$11.5~mu $ </tex-math></inline-formula>m with the widths of 0.3 and <inline-formula> <tex-math>$0.4~mu $ </tex-math></inline-formula>m were found to be the optimal channel configuration for applying the SW algorithm. Based on the global atmospheric profiles provided in the ERA5 and SeeBor V5.0 database and the emissivity spectra provided in the ECOSTRESS library, the performance of the SW algorithm using the determined channel configuration was delicately evaluated. Results show that the LST retrieval root mean square error (RMSE) of the determined channel configuration was 1.09 K, better than that of the MODIS (1.28 K), Landsat-9 (1.24 K), and Sentinel-3 A (1.24 K) instruments regarding the global atmospheric profiles provided in the ERA5 database. Similar results were obtained corresponding to SeeBor V5.0 atmospheric profiles with the LST retrieval RMSE of 1.28 K (determined channel configuration), 1.49 K (MODIS), 1.96 K (Landsat-9), and 1.94 K (Sentinel-3 A).","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":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314900","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

Continual Self-Supervised Learning With Masked Autoencoders in Remote Sensing 遥感中基于掩模自编码器的持续自监督学习

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-13 DOI: 10.1109/LGRS.2025.3579585

Lars Möllenbrok;Behnood Rasti;Begüm Demir

{"title":"Continual Self-Supervised Learning With Masked Autoencoders in Remote Sensing","authors":"Lars Möllenbrok;Behnood Rasti;Begüm Demir","doi":"10.1109/LGRS.2025.3579585","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3579585","url":null,"abstract":"The development of continual learning (CL) methods, which aim to learn new tasks in a sequential manner from the training data acquired continuously, has gained great attention in remote sensing (RS). The existing CL methods in RS, while learning new tasks, enhance robustness toward catastrophic forgetting. This is achieved using a large number of labeled training samples, which is costly and not always feasible to gather in RS. To address this problem, we propose a novel continual self-supervised learning (SSL) method in the context of masked autoencoders (MAEs) (denoted as CoSMAE). The proposed CoSMAE consists of two components: 1) data mixup and 2)model mixup knowledge distillation. Data mixup is associated with retaining information on previous data distributions by interpolating images from the current task with those from the previous tasks. Model mixup knowledge distillation is associated with distilling knowledge from past models and the current model simultaneously by interpolating their model weights to form a teacher for knowledge distillation. The two components complement each other to regularize the MAE at the data and model levels to facilitate better generalization across tasks and reduce the risk of catastrophic forgetting. Experimental results show that CoSMAE achieves significant improvements of up to 4.94% over state-of-the-art CL methods applied to MAE. Our code is publicly available at: <uri>https://git.tu-berlin.de/rsim/CoSMAE</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":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472563","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

Compensating Spatial Degradation in HSI Classification: A Polarity Transformer With LBP-Guided Feature Learning 补偿HSI分类中的空间退化：一种具有lbp引导特征学习的极性变压器

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-13 DOI: 10.1109/LGRS.2025.3579603

Min Zhu;Jingxing Zhong;Zhihua Shen;Suquan Wu

{"title":"Compensating Spatial Degradation in HSI Classification: A Polarity Transformer With LBP-Guided Feature Learning","authors":"Min Zhu;Jingxing Zhong;Zhihua Shen;Suquan Wu","doi":"10.1109/LGRS.2025.3579603","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3579603","url":null,"abstract":"Hyperspectral images’ (HSIs) classification is inherently challenged by the degraded spatial information in complex scenarios, where existing feature extraction methods fail to realize the consistent compensation from spectral to spatial information. To address this, we propose a novel framework that integrates local binary patterns (LBPs) for texture-guided spatial enhancement and a polarity transformer to model complementary feature representations. The key innovation lies in decomposing the input features into polarity mask through the LBP texture guidance. The positive polarity emphasizes dominant spectral–spatial correlations, while the negative suppresses noise and redundant responses, jointly enabling adaptive compensation of lost spatial details. Simultaneously, multiscale LBP operators are embedded prior to the transformer to explicitly encode rotation-invariant texture features. The original HSI data and LBP-enhanced features are processed by polarity transformers and fused through a cross-polarity interaction module, ensuring the complementary advantages of global spectral context and local texture details. Experimental results on widely used HSI datasets demonstrate that our method achieves superior classification performance compared with state-of-the-art approaches, particularly in low-resolution conditions.","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":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366998","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