IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society最新文献_第8页

Spatial–Stratigraphic Information and Dynamic Range Attention Assist Well-Logging Lithological Interpretation 空间地层信息和动态范围的关注有助于测井岩性解释

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-18 DOI: 10.1109/LGRS.2025.3562350

Keran Li;Jinmin Song;Shugen Liu;Zhiwu Li;Di Yang;Wei Chen;Xin Jin;Chunqiao Yan;Shan Ren

{"title":"Spatial–Stratigraphic Information and Dynamic Range Attention Assist Well-Logging Lithological Interpretation","authors":"Keran Li;Jinmin Song;Shugen Liu;Zhiwu Li;Di Yang;Wei Chen;Xin Jin;Chunqiao Yan;Shan Ren","doi":"10.1109/LGRS.2025.3562350","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3562350","url":null,"abstract":"Time-series models, particularly CNN-bidirectional long short-term memory (BiLSTM) architectures, have shown advances in the lithological interpretation of well-logging data. However, CNN and attention mechanisms face challenges in training efficiency and predicting precision. To improve this deficiency, a dynamic and lightweight attention mechanism and a strategy that combines geological/spatial information have been proposed. This study introduces two novel enhancements: the spatial and stratigraphic information processing (shortened as Spatial and Strat) method and the dynamic range attention (DRA) mechanism. Spatial-stratigraphic context (SSP) integrates geological context by encoding depositional sequences as time series. DRA is a lightweight attention module that adaptively adjusts local attention ranges based on global context. Experiments on a collected dataset from the eastern Sichuan Basin (13 wells and 14 587 labeled samples) demonstrate that the proposed DRA-BiLSTM model with SSP achieves excellent performance, achieving accuracies of 0.99 on the training set, 0.97 on the validation set, and 0.92 on the testing set, with low error rates of 0.08 for Top-5 and 0.02 for Top-1. Ablation studies confirm the critical roles of SSP in capturing geological patterns and DRA in balancing computational efficiency by paying more attention to the vertical sedimentary process. These innovations significantly advance automated lithological interpretation, offering a robust framework for geophysical applications.","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":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072852","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

CDxLSTM: Boosting Remote Sensing Change Detection With Extended Long Short-Term Memory CDxLSTM：扩展长短期记忆的遥感变化检测

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-18 DOI: 10.1109/LGRS.2025.3562480

Zhenkai Wu;Xiaowen Ma;Rongrong Lian;Kai Zheng;Wei Zhang

{"title":"CDxLSTM: Boosting Remote Sensing Change Detection With Extended Long Short-Term Memory","authors":"Zhenkai Wu;Xiaowen Ma;Rongrong Lian;Kai Zheng;Wei Zhang","doi":"10.1109/LGRS.2025.3562480","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3562480","url":null,"abstract":"In complex scenes and varied conditions, effectively integrating spatial-temporal context is crucial for accurately identifying changes. However, current remote sensing change detection (RS-CD) methods lack a balanced consideration of performance and efficiency. CNNs lack global context, transformers are computationally expensive, and Mambas face compute unified device architecture (CUDA) dependence and local correlation loss. In this letter, we propose CDxLSTM, with a core component that is a powerful xLSTM-based feature enhancer (FE) layer, integrating the advantages of linear computational complexity, global context perception, and strong interpretability. Specifically, we introduce a scale-specific FE layer, incorporating a cross-temporal global perceptron (CTGP) customized for semantic-accurate deep features, and a cross-temporal spatial refiner (CTSR) customized for detail-rich shallow features. In addition, we propose a cross-scale interactive fusion (CSIF) module to progressively interact global change representations with spatial responses. Extensive experimental results demonstrate that CDxLSTM achieves state-of-the-art performance across three benchmark datasets, offering a compelling balance between efficiency and accuracy. Code is available at <uri>https://github.com/xwmaxwma/rschange</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":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918796","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

Optimizing Relative Radiometric Normalization: Minimizing Residual Distortions in Multispectral Bitemporal Images Using Trust-Region Reflective and Laplacian Pyramid Fusion 优化相对辐射归一化：利用信任区域反射和拉普拉斯金字塔融合最小化多光谱双时间图像的残留畸变

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-18 DOI: 10.1109/LGRS.2025.3562276

Armin Moghimi;Turgay Celik;Ali Mohammadzadeh;Saied Pirasteh;Jonathan Li

{"title":"Optimizing Relative Radiometric Normalization: Minimizing Residual Distortions in Multispectral Bitemporal Images Using Trust-Region Reflective and Laplacian Pyramid Fusion","authors":"Armin Moghimi;Turgay Celik;Ali Mohammadzadeh;Saied Pirasteh;Jonathan Li","doi":"10.1109/LGRS.2025.3562276","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3562276","url":null,"abstract":"Accurate relative radiometric normalization (RRN) is important for reliable multitemporal remote sensing image analysis. Traditional methods often depend on coregistered image pairs, limiting their applicability with unregistered data. Keypoint-based RRN (KRRN) relaxes this constraint but remains affected by residual radiometric errors due to normalization inaccuracies and nonlinear effects. This letter introduces a refinement strategy that leverages the trust-region reflective (TRR) algorithm to optimize normalization parameters, coupled with Laplacian pyramid (LP) fusion for seamless image integration. Evaluation on four multispectral image pairs from different sensors (e.g., Landsat 8 and Sentinel-2, IRS and Landsat 5, Landsat 7 and SPOT-5, and UK-DMC2 and Landsat 5) and one pair from the same sensor (Sentinel-2) showed that our method reduces residual radiometric discrepancies, achieving up to 29% lower RMSE than some well-known models. The source code and datasets are available on GitHub: <uri>https://github.com/ArminMoghimi/Tensor-based-keypoint-detection</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":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896434","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 Novel Truncated Capped Norm Regularization Method for Hyperspectral Image Denoising 一种新的截断帽范数正则化方法用于高光谱图像去噪

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-18 DOI: 10.1109/LGRS.2025.3562203

Xuegang Luo;Junrui Lv

引用次数: 0

Robust DOA Estimation Using Complex-Valued Residual Attention Networks With Low-Rank and Sparse Prior 基于低秩稀疏先验复值残差注意网络的鲁棒DOA估计

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-17 DOI: 10.1109/LGRS.2025.3562069

Zeqi Yang;Shuai Ma;Yiheng Liu;Hua Zhang;Xiaode Lyu

引用次数: 0

Seismic Statistical Prediction for Fracture Azimuth Based on Fourier Series 基于傅立叶级数的裂缝方位地震统计预测

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-17 DOI: 10.1109/LGRS.2025.3561743

Zhan Wang;Xingyao Yin;Zhengqian Ma;Yaming Yang;Wei Xiang

{"title":"Seismic Statistical Prediction for Fracture Azimuth Based on Fourier Series","authors":"Zhan Wang;Xingyao Yin;Zhengqian Ma;Yaming Yang;Wei Xiang","doi":"10.1109/LGRS.2025.3561743","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3561743","url":null,"abstract":"The azimuth of fractures has long been a subject of interest for geophysicists, and it holds paramount importance in the exploration and development of oil and gas resources. However, traditional fracture azimuth prediction methods heavily rely on seismic data quality and well-logging data, often encountering severe noise interference and 90° ambiguity. This makes fracture azimuth prediction challenging in areas with complex geological structures. A method for seismic statistical prediction of fracture azimuth based on the Fourier series has been proposed to address these issues. First, the Rüger approximation is rewritten into Fourier series form, combining parameters with high linear correlation to mitigate the ill-conditioning of the coefficient matrix. Second, construct a complex representation of fracture azimuth and initially adjust the sign based on the characteristic that the azimuthal period of the fourth-order Fourier coefficient is <inline-formula> <tex-math>$pi $ </tex-math></inline-formula>/2. Third, considering that the fourth-order Fourier coefficients are susceptible to noise, a directional statistical method is introduced to enhance the stability of fracture azimuth prediction. Then, by analyzing the relationship between second- and fourth-order Fourier coefficients under saturated fluid and gas-filled conditions, the Welch t-test, suitable for data with nonhomogeneous variance, is introduced to eliminate the influence of fluid type on fracture azimuth prediction. Numerical experiments and field data demonstrate that the proposed method overcomes the 90° ambiguity inherent in conventional fracture azimuth prediction, proving its stability and effectiveness in areas with severe structural variations.","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":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143938016","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 Segmentation of Multimodal Optical and SAR Images With Multiscale Attention Network 基于多尺度注意网络的多模态光学和SAR图像语义分割

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-17 DOI: 10.1109/LGRS.2025.3561747

Dongdong Xu;Jin Qian;Hao Feng;Zheng Li;Yongcheng Wang

{"title":"Semantic Segmentation of Multimodal Optical and SAR Images With Multiscale Attention Network","authors":"Dongdong Xu;Jin Qian;Hao Feng;Zheng Li;Yongcheng Wang","doi":"10.1109/LGRS.2025.3561747","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3561747","url":null,"abstract":"The joint semantic segmentation of multimodal remote sensing (RS) images can make up for the problem of insufficient features of single-modal images and effectively improve classification accuracy. Some deep learning methods have achieved good performance, but they face problems such as complex network structure, large number of parameters, and deployment difficulty. In this letter, more attention is paid to front-end and branch-level feature transformation to obtain multiscale semantic information. The multiscale dilated extraction module (MDEM) is constructed to mine the specific features of different modalities. The multimodal complementary attention module (MCAM) is designed for further acquiring prominent complementary content. The concatenated features are transmitted and reused by the dense convolution to complete the encoding. Ultimately, a general and concise end-to-end model is proposed. Comparative experiments are carried out on three heterogeneous datasets, and the model put forward performs well in qualitative analysis, quantitative comparison, and visual effect. Meanwhile, the dexterity and practicability of the model are more prominent, which can provide support for lightweight design and hardware deployment.","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":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918628","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

IRSTD-YOLO: An Improved YOLO Framework for Infrared Small Target Detection IRSTD-YOLO：红外小目标检测的改进YOLO框架

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-17 DOI: 10.1109/LGRS.2025.3562096

Yuan Tang;Tingfa Xu;Haolin Qin;Jianan Li

{"title":"IRSTD-YOLO: An Improved YOLO Framework for Infrared Small Target Detection","authors":"Yuan Tang;Tingfa Xu;Haolin Qin;Jianan Li","doi":"10.1109/LGRS.2025.3562096","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3562096","url":null,"abstract":"Detecting small targets in infrared images, especially in low-contrast and complex backgrounds, remains challenging. To tackle this, we propose infrared small target detection YOLO (IRSTD-YOLO), a novel detection network. The edge and feature extraction (EFE) module enhances feature representation by integrating a SobelConv branch and a 2DConv branch. The SobelConv branch applies Sobel operators to extract gradient information, enhancing edge contrast and making small targets more distinguishable from the background. Unlike standard convolutions, which process all features uniformly, this edge-aware operation emphasizes structural information crucial for detecting small infrared targets. The 2DConv branch captures spatial context, complementing the edge features to create a more comprehensive representation. To further refine detection, we introduce the infrared small target enhancement (IRSTE) module, addressing the limitations of conventional feature pyramid networks. Instead of merely adding a shallow detection head, IRSTE processes and enhances shallow-layer features, which are rich in small target information, and fuses them with deeper features. By leveraging a multibranch strategy that integrates local, global, and large-scale contexts, IRSTE enhances small target representation and detection robustness, particularly in low-contrast environments where traditional networks often fail. Experimental results show that IRSTD-YOLO achieves an mAP@0.5:0.95 of 36.7% on the InfraredUAV dataset and 51.6% on the AntiUAV310 dataset, outperforming YOLOv11-s by 4.4% and 4.2%, respectively. Code is released at <uri>https://github.com/vectorbullet/IRSTD-YOLO</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":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073096","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

Learning Instructive Frequency Spectral and Curvature Features for Cloud Detection

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-17 DOI: 10.1109/LGRS.2025.3561935

Wanjuan Hu;Guanyi Li;Guoguo Zhang;Liang Chang;Dan Zeng

{"title":"Learning Instructive Frequency Spectral and Curvature Features for Cloud Detection","authors":"Wanjuan Hu;Guanyi Li;Guoguo Zhang;Liang Chang;Dan Zeng","doi":"10.1109/LGRS.2025.3561935","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3561935","url":null,"abstract":"Current cloud detection methods often treat all spectral bands equally, which limits their ability to capture instructive clues necessary for accurate detection. As a result, distinguishing clouds from snow in coexisting environments remains challenging. Moreover, most approaches struggle to adaptively model the boundaries of clouds, which is crucial for detecting thin clouds with ambiguous edges. To address these challenges, we propose a novel approach for cloud detection called FSCFNet, which captures guiding visual features from frequency and curvature computations. FSCFNet comprises two key modules: the frequency spectral feature enhancement module (FSFEM) and the curvature-based edge-awareness module (CEAM). The FSFEM leverages the distinct characteristics of spectral bands to extract instructive visual cues, enabling the network to learn robust discriminative features for ice, snow, and clouds. In contrast, the CEAM adaptively identifies texture-rich regions using curvature, enhancing the ability to delineate thin cloud boundaries. Comprehensive quantitative and qualitative experiments on the Landsat 8 and MODIS datasets demonstrate that FSCFNet consistently outperforms state-of-the-art methods. Our code is publicly available at <uri>https://github.com/wanjuanhu/FSCFNet/tree/main</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":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943848","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

Semi-Supervised Graph Constraint Dual Classifier Network With Unknown Class Feature Learning for Hyperspectral Image Open-Set Classification 基于未知类特征学习的半监督图约束对偶分类器网络用于高光谱图像开集分类

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.2025.3561306

Na Li;Xiaopeng Song;Yongxu Liu;Wenxiang Zhu;Chuang Li;Weitao Zhang;Yinghui Quan

{"title":"Semi-Supervised Graph Constraint Dual Classifier Network With Unknown Class Feature Learning for Hyperspectral Image Open-Set Classification","authors":"Na Li;Xiaopeng Song;Yongxu Liu;Wenxiang Zhu;Chuang Li;Weitao Zhang;Yinghui Quan","doi":"10.1109/LGRS.2025.3561306","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3561306","url":null,"abstract":"In view of the practical value of open datasets of hyperspectral images (HSIs), HSI open-set classification (OSC) has attracted more and more attention. Existing HSI OSC methods are usually based on learning labeled samples to identify unknown classes. However, due to the complex high-dimensional characteristics of HSIs and the limited number of labeled samples, the recognition of unknown classes based only on limited labeled samples often has low and unstable accuracy. To address this problem, we propose a semi-supervised graph constraint dual classifier network (SSGCDCN) that can achieve efficient and stable OSC by learning unknown class features and relationships among samples. First, a dual classifier consisting of a multiclassifier and multiple binary classifiers is constructed, which has the ability to discover the unknown class samples by assigning and enabling pseudo-labels to participate in model training to achieve unknown class feature learning. Then, to improve the classification accuracy of both known and unknown classes, a homogeneous graph constraint is imposed on SSGCDCN to learn the relationship information among samples (including labeled and unlabeled samples). This constraint can bring the features of similar samples closer while pushing apart features of dissimilar samples. Experiments evaluated on three datasets demonstrate that the proposed method can obtain superior OSC performance than other state-of-the-art classification 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":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073034","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