IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society最新文献_第5页

A Deep-Learning-Based Method for Attenuation Compensation in Ground-Penetrating Radar

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-24 DOI: 10.1109/LGRS.2025.3554397

Weikun Liu;Fengyuan Sun;Hang Zhao

{"title":"A Deep-Learning-Based Method for Attenuation Compensation in Ground-Penetrating Radar","authors":"Weikun Liu;Fengyuan Sun;Hang Zhao","doi":"10.1109/LGRS.2025.3554397","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3554397","url":null,"abstract":"Ground-penetrating radar (GPR) is an essential tool for nondestructive subsurface exploration. However, electromagnetic wave propagation in underground environments is severely attenuated, leading to the loss of important geological information and limiting the resolution of underground imaging. To address this challenge, we propose an attention-enhanced U-Net (AEU-Net) model for GPR signal attenuation compensation. This model builds upon the 1-D U-Net architecture and integrates a feature fusion attention block (FFAB) to effectively capture both local and global features, thereby enhancing its capability to process complex datasets. In addition, to overcome dataset acquisition challenges, we use GprMax software to simulate realistic geological structures based on the relationship between conductivity and electromagnetic wave attenuation, thereby generating the training dataset. Experimental results with synthetic and field data demonstrate that the proposed method significantly improves noise robustness, restores fine subsurface details, and effectively compensates for GPR signal attenuation, thereby showing its potential for high-resolution underground imaging.","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":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143824655","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

Spectral Partitioning of Synthetic Aperture Sonar Imagery for Improved ATR

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-24 DOI: 10.1109/LGRS.2025.3554335

David P. Williams;Daniel C. Brown

引用次数: 0

SVRNN: A Spatiotemporal Prediction Model for Sea Surface Temperature Prediction in the Taiwan Strait

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-24 DOI: 10.1109/LGRS.2025.3554296

Haiqiang Chen;Yongxiang Chen;Zhenchang Zhang

{"title":"SVRNN: A Spatiotemporal Prediction Model for Sea Surface Temperature Prediction in the Taiwan Strait","authors":"Haiqiang Chen;Yongxiang Chen;Zhenchang Zhang","doi":"10.1109/LGRS.2025.3554296","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3554296","url":null,"abstract":"Accurate prediction of sea surface temperature (SST) plays a critical role in climate research and marine ecosystem management. Traditional models predict trends by analyzing and fitting data, but they struggle with capturing long-range dependencies and complex spatiotemporal patterns. The transformer’s attention mechanism effectively addresses long-range dependencies, but its high computational complexity poses challenges. To overcome these limitations, this study proposes a novel spatiotemporal sequence prediction model: the spatiotemporal vision mamba recurrent neural network (SVRNN). The model innovatively integrates a bidirectional state-space processing mechanism and decoupled memory modules. The bidirectional mechanism maintains a global receptive field with linear computational complexity, while the decoupled memory modules explicitly separate spatiotemporal dependencies, enhancing the model’s ability to capture complex spatiotemporal patterns. During the experiment on hourly SST prediction in the Taiwan Strait, where the SST of the next 12 h was predicted using data from the previous 12 h, the SVRNN model demonstrated superior performance, achieving a root mean square error (RMSE) of <inline-formula> <tex-math>$0.159~^{circ }$ </tex-math></inline-formula>C, a mean absolute error (MAE) of <inline-formula> <tex-math>$0.105~^{circ }$ </tex-math></inline-formula>C, and a mean absolute percentage error (MAPE) of 0.496%. Furthermore, our seasonal error analysis reveals that the model exhibits robust performance in different seasons, providing more reliable technical support for SST prediction in Taiwan Strait.","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":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783287","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

Learned Spectral and Spatial Transforms for Multispectral Remote Sensing Data Compression

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-24 DOI: 10.1109/LGRS.2025.3554269

Sebastià Mijares;Joan Bartrina-Rapesta;Miguel Hernández-Cabronero;Joan Serra-Sagristà

{"title":"Learned Spectral and Spatial Transforms for Multispectral Remote Sensing Data Compression","authors":"Sebastià Mijares;Joan Bartrina-Rapesta;Miguel Hernández-Cabronero;Joan Serra-Sagristà","doi":"10.1109/LGRS.2025.3554269","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3554269","url":null,"abstract":"As more and more multispectral and hyperspectral platforms are deployed for Earth observation (EO), limited downlink capacity increases the pressure for more efficient data compression algorithms. Machine learning (ML) has been successfully applied to produce highly competitive compression models though this performance has typically been at the cost of high computational complexity, a crucial limitation for on-board remote sensing data compression. To address these issues, a reduced-complexity multispectral and hyperspectral data compression architecture is proposed. Using separate spectral and spatial transforms, the complexity of the proposed models is scalable on the number of bands, regardless of the compression ratios. This proposal outperforms state-of-the-art ML compression models as well as established lossy compression methods such as JPEG 2000 prepended with a spectral Karhunen-Loève transform (KLT) on a variety of remote sensing data sources. The performance improvement is achieved with a lower complexity than said ML models. To reproduce our results, training and test data are publicly available at <uri>https://gici.uab.cat/GiciWebPage/datasets.php</uri> and source code at <uri>https://github.com/smijares/mbhs2025</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":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938112","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual-View Structural Similarity Subspace Clustering for Hyperspectral Band Selection

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-24 DOI: 10.1109/LGRS.2025.3554356

Dongkai Yan;Xudong Sun;Jiahua Zhang;Xiaodi Shang

{"title":"Dual-View Structural Similarity Subspace Clustering for Hyperspectral Band Selection","authors":"Dongkai Yan;Xudong Sun;Jiahua Zhang;Xiaodi Shang","doi":"10.1109/LGRS.2025.3554356","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3554356","url":null,"abstract":"Band selection (BS) is a vital technique for improving efficiency of hyperspectral image (HSI) processing. This letter proposes a dual-view structural similarity subspace clustering model (DVS3C) for BS. Traditional low-rank subspace clustering (LRSC) methods rely solely on single-view data (e.g., original HSI), potentially leading to the loss of critical information (e.g., spatial structures) and insufficient exploitation of the multi-dimensional features of HSI for optimal BS. To do so, DVS3C constructs a spatial view alongside the spectral view, leveraging global spectral-spatial information through subspace clustering to achieve complementary advantages between views. Besides, to overcome LRSC’s limitations in capturing band local structure, DVS3C introduces a structural similarity matrix to deeply exploit intraview neighborhood relationships of bands, further reducing band redundancy. Ultimately, an adaptive dual-view fusion strategy that iteratively optimizes a consensus matrix while dynamically adjusting the contribution of each view is designed to ensure view consistency. Experimental results on four public datasets demonstrate its remarkable stability and superiority. The source code is available at <uri>https://github.com/ydk0912/DVS3C</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":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786388","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

Hyperspectral Band Selection via Structural Correlation and Information Measures

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-24 DOI: 10.1109/LGRS.2025.3554265

Zijian Li;Mi Wang;Shaoju Wang

{"title":"Hyperspectral Band Selection via Structural Correlation and Information Measures","authors":"Zijian Li;Mi Wang;Shaoju Wang","doi":"10.1109/LGRS.2025.3554265","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3554265","url":null,"abstract":"Band selection is an effective dimensionality reduction technique for hyperspectral image (HSI). In recent years, many unsupervised band selection methods have been proposed, but most of them measure the similarity between bands only from the spectral dimension, ignoring their high spatial correlation. In addition, these methods provide a less than ideal quantitative description of spectral information and neglect the redundancy within the selected bands. To address these issues, we propose a hyperspectral band selection via structural correlation and information measures (SCIMs), claiming the following contributions: 1) through introducing the structural similarity (SSIM) index to assess the correlation between bands, both spectral and spatial information are considered; 2) the HSI cube is partitioned into several groups by considering both intragroup and intergroup similarity measured by SSIM; and 3) in order to obtain a high-quality band subset, a representative band is selected in each group from the point of view of information as well as redundancy. The experimental results on three HSI datasets show that the proposed method has significant advantages compared with competitors.","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":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800903","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

The Coupling GSV and MARMIT-2 Models to Characterize Reflectance Properties of Dry and Wet Soils

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-24 DOI: 10.1109/LGRS.2025.3554266

Anxin Ding;Yi Yao;Haoran Song;Jun Geng;Ping Zhao;Peng Peng;Hailan Jiang;Kaijian Xu;Ziti Jiao

{"title":"The Coupling GSV and MARMIT-2 Models to Characterize Reflectance Properties of Dry and Wet Soils","authors":"Anxin Ding;Yi Yao;Haoran Song;Jun Geng;Ping Zhao;Peng Peng;Hailan Jiang;Kaijian Xu;Ziti Jiao","doi":"10.1109/LGRS.2025.3554266","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3554266","url":null,"abstract":"Soil models are widely used to characterize the reflectance properties of dry and wet soils. By considering detailed physical processes, the improved multilayer radiative transfer model of soil reflectance (MARMIT-2) model significantly improves the accuracy of simulating wet soil properties. However, the MARMIT-2 model relies on measured dry soil reflectance as an input, which limits its applicability in practical scenarios, especially when detailed information about specific soils is unavailable. To address this issue, this study first evaluated the ability of the general spectral vector (GSV) model of dry soil to represent the reflectance properties of dry soil. Then, we coupled these dry soil vectors with the MARMIT-2 model to propose the GSV + MARMIT-2 model. Finally, we assessed the accuracy of all three models using a wet soil database. The main conclusions of this study include: 1) the dry soil spectral vectors from the GSV model demonstrated high accuracy in describing the reflectance properties of dry soil, achieving an <inline-formula> <tex-math>$R^{2}$ </tex-math></inline-formula> of 0.988 and a root mean square error (RMSE) of 0.016. 2) All three soil models exhibited high fitting accuracy for the wet soil database (<inline-formula> <tex-math>$R^{{2}} = sim 0.992$ </tex-math></inline-formula> and RMSE <inline-formula> <tex-math>$= sim 0.012$ </tex-math></inline-formula>). Compared to the GSV and MARMIT-2 models, the GSV + MARMIT-2 model showed slightly improved accuracy under different soil moisture content (SMC) conditions. This study developed a more versatile and flexible soil model framework as it directly integrates the dry soil spectral vectors from the GSV model into the MARMIT-2 model. This coupling significantly expanded the applicability and improved the stability of the MARMIT-2 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":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792924","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

Interferometric Phase Noise Reduction Based on Adaptive Edge Detection and Temporal Area Filtering for GNSS-Based InBSAR 基于自适应边缘检测和时域滤波的干涉相位噪声降低，用于基于 GNSS 的 InBSAR

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-24 DOI: 10.1109/LGRS.2025.3554183

Yuanhao Li;Zhixiang Xu;Feifeng Liu;Zhanze Wang;Jingtian Zhou;Xiaojing Wu

{"title":"Interferometric Phase Noise Reduction Based on Adaptive Edge Detection and Temporal Area Filtering for GNSS-Based InBSAR","authors":"Yuanhao Li;Zhixiang Xu;Feifeng Liu;Zhanze Wang;Jingtian Zhou;Xiaojing Wu","doi":"10.1109/LGRS.2025.3554183","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3554183","url":null,"abstract":"Global navigation satellite system-based bistatic synthetic aperture radar interferometry (GNSS-based InBSAR) can improve the monitoring interval to one day due to the using of navigation satellites. Meanwhile, the low signal-to-noise ratio (SNR), poor image resolution, and the random focus position offsets cause large interferometric phase noise. In this letter, an interferometric phase noise reduction algorithm is proposed for GNSS-based InBSAR based on adaptive edge detection and temporal area filtering. An improved edge detection algorithm is adopted to solve the overlapping of resolution cells and phase interference caused by poor resolution. Then, to compensate the random focus position, an area filtering algorithm is proposed to find the temporal supporting area of persistent scatterers (PSs). Finally, the principal phase is extracted to reduce the interferometric phase error. The raw data are used to indicate the effectiveness of the proposed algorithm, and the best monitoring accuracy can reach millimeter level.","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":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821594","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

Detection of Small Targets in Sea Clutter Using Dual-Polarization Correlation Features and One-Class Classifier 使用双极化相关特征和单类分类器检测海杂波中的小目标

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-24 DOI: 10.1109/LGRS.2025.3554337

Lichao Liu;Qiang Guo;Shuai Huang;Mykola Kaliuzhnyi

{"title":"Detection of Small Targets in Sea Clutter Using Dual-Polarization Correlation Features and One-Class Classifier","authors":"Lichao Liu;Qiang Guo;Shuai Huang;Mykola Kaliuzhnyi","doi":"10.1109/LGRS.2025.3554337","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3554337","url":null,"abstract":"Due to the nonstationary, time-varying, and target-like characteristics of sea clutter, detection of small targets embedded within it has been a long-standing and formidable challenge in the field of remote sensing. Traditional adaptive detectors based on sea clutter modeling often struggle to achieve satisfactory detection performance. To address this issue, this letter proposed a novel small target detector embedded in sea clutter that leverages dual-polarization correlation features and one-class classifier. Initially, three distinct features are extracted from the significant differences between the target echoes and sea clutter in the dual-polarization correlation domain. Each individual feature possesses a certain level of discriminative power. Furthermore, under the framework of anomaly detection, a one-class classifier for small sea surface target detection is constructed based on the fast convex hull learning (FCHL) algorithm to make the final decisions. Experimental results based on the IPIX datasets demonstrate that the proposed detector outperforms several existing detectors.","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":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143824656","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

Swin Transformer Embedding MSMDFFNet for Road Extraction From Remote Sensing Images

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-19 DOI: 10.1109/LGRS.2025.3552763

Yuchuan Wang;Ling Tong;Jiaxing Yang;Shangtao Qin

{"title":"Swin Transformer Embedding MSMDFFNet for Road Extraction From Remote Sensing Images","authors":"Yuchuan Wang;Ling Tong;Jiaxing Yang;Shangtao Qin","doi":"10.1109/LGRS.2025.3552763","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3552763","url":null,"abstract":"Contextual road features and multiscale spatial semantic information play a vital role in road extraction from remote sensing (RS) images. However, accurately modeling these essential features with current convolutional neural network (CNN)-based road extraction algorithms remains challenging, leading to fragmented roads in occluded areas. Inspired by the self-attention mechanism of transformers in natural language processing (NLP), we propose an innovative MSMDFFNet in conjunction with Swin Transformer (SwinMSMDFFNet) for road extraction from RS images. First, the Swin Transformer is embedded as an auxiliary encoder into the MSMDFFNet to incorporate necessary self-attention mechanisms. Meanwhile, a multigranularity sampling (MGS) module is introduced to enhance the computation of self-attention at multiple granularities by the Swin Transformer. This module specifically transforms the feature maps produced by the main encoder into suitable inputs for the auxiliary encoder. Furthermore, to enhance the connections between adjacent local windows in the auxiliary encoder, a cross-directional fusion (CDF) module is designed for feeding the features of the auxiliary encoder back into the main encoder. Extensive experiments conducted on the DeepGlobe and LSRV datasets demonstrate that our proposed SwinMSMDFFNet has significant advantages in extracting road structure, particularly in areas with long-distance occlusions. It surpasses existing methods in pixel-level metrics such as F1 score, intersection over union (IoU), and connectivity metric average path length similarity (APLS). The code will be made publicly available at: <uri>https://github.com/wycloveinfall/SwinMSMDFFNet</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":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143824576","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