IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society最新文献_第10页

SAMSelect: A Spectral Index Search for Marine Debris Visualization Using Segment Anything SAMSelect：光谱索引搜索海洋碎片可视化使用分段任何

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-09 DOI: 10.1109/LGRS.2025.3572407

Joost van Dalen;Yuki M. Asano;Marc Rußwurm

{"title":"SAMSelect: A Spectral Index Search for Marine Debris Visualization Using Segment Anything","authors":"Joost van Dalen;Yuki M. Asano;Marc Rußwurm","doi":"10.1109/LGRS.2025.3572407","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3572407","url":null,"abstract":"This work proposes SAMSelect, an algorithm to obtain a salient three-channel visualization for multispectral images. We develop SAMSelect and show its use for marine scientists visually interpreting floating marine debris in Sentinel-2 imagery. These debris are notoriously difficult to visualize due to their compositional heterogeneity in medium-resolution imagery. Out of these difficulties, a visual interpretation of imagery showing marine debris remains a common practice by domain experts, who select bands and spectral indices on a case-by-case basis informed by common practices and heuristics. SAMSelect selects the band or index combination that achieves the best classification accuracy on a small annotated dataset through the segment anything model (SAM). Its central assumption is that the three-channel visualization achieves the most accurate segmentation results also provide good visual information for photointerpretation. We evaluate SAMSelect in three Sentinel-2 scenes containing generic marine debris in Accra, Ghana, and Durban, South Africa, and deployed plastic targets from the Plastic Litter Project (PLP). This reveals the potential of new previously unused band combinations (e.g., a normalized difference index (NDI) of B8, <inline-formula> <tex-math>${B}2$ </tex-math></inline-formula>), which demonstrate improved performance compared with literature-based indices. We describe the algorithm in this letter and provide an open-source code repository that will be helpful for domain scientists doing visual photo interpretation, especially in the marine field.","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":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281340","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-Based Convolutional Neural Network for Anomaly Detection in Multispectral Images of Semi-Natural Ecosystems 基于注意力的卷积神经网络半自然生态系统多光谱图像异常检测

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-09 DOI: 10.1109/LGRS.2025.3577943

Javier López-Fandiño;Álvaro Ordóñez;Pablo Quesada-Barriuso;Alberto S. Garea;Francisco Argüello;Dora B. Heras

{"title":"Attention-Based Convolutional Neural Network for Anomaly Detection in Multispectral Images of Semi-Natural Ecosystems","authors":"Javier López-Fandiño;Álvaro Ordóñez;Pablo Quesada-Barriuso;Alberto S. Garea;Francisco Argüello;Dora B. Heras","doi":"10.1109/LGRS.2025.3577943","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3577943","url":null,"abstract":"The monitoring of semi-natural ecosystems has become increasingly critical due to the rising impact of ecological disturbances, including natural disasters and unauthorized human-made constructions. Anomaly detection (AD) in multispectral imagery serves as a fundamental tool in this context. Deep-learning (DL)-based techniques are particularly effective at capturing the intricate spectral and spatial patterns of anomalies. This letter proposes a new AD technique called attention-based convolutional neural network (ACNN), designed to enhance AD performance in multispectral images of high spatial resolution for the detection of human-made constructions. The model integrates attention mechanisms to prioritize informative features while suppressing irrelevant background information, thereby improving sensitivity to subtle and rare anomalies. Experimental results on multispectral datasets from semi-natural ecosystems show that the proposed approach outperforms existing DL techniques in terms of detection accuracy. These findings highlight the potential of attention-based models as a robust framework for environmental monitoring and AD in complex remote sensing scenarios.","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":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11028595","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314750","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

Patch Tensor-Based Geometric Structure Representation for Hyperspectral Imagery Classification 基于Patch张量的高光谱图像分类几何结构表示

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-09 DOI: 10.1109/LGRS.2025.3578223

Guangyao Shi;Jingwen Yan;Wenhao Xiang;Feng Chen

{"title":"Patch Tensor-Based Geometric Structure Representation for Hyperspectral Imagery Classification","authors":"Guangyao Shi;Jingwen Yan;Wenhao Xiang;Feng Chen","doi":"10.1109/LGRS.2025.3578223","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3578223","url":null,"abstract":"Hyperspectral remote sensing images (HSIs) have nanometer-level spectral resolution, and the rich spatial and spectral information they contain makes them possible to perform detailed land-cover analysis. In this letter, a patch tensor-based geometric structure representation (PTGSR) method was proposed for HSI classification. At first, based on the high-order structure of tensor samples, a novel representation learning (RL) model based on tensor neighborhood structure is constructed to capture the relationships between different tensor samples. Then, by utilizing the spatial coordinates of central pixels in the tensor samples, the distribution probabilities between different samples are computed to improve the effectiveness of the representation coefficients. Finally, the classification errors of each class across different tensor orders are integrated to enhance the overall classification performance. Experimental results on three HSI datasets demonstrate that the PTGSR algorithm outperforms several classification methods with overall accuracy (OA) improvements of 1.78%–31.43%, 1.55%–8.54%, and 2.14%–15.72% for Indian Pines, Fanglu, and Houston2013, respectively.","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":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662024","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

Enhanced Deep Learning SAR Despeckling Networks Based on SAR Assessing Metrics 基于SAR评估指标的增强型深度学习SAR去噪网络

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-09 DOI: 10.1109/LGRS.2025.3577907

Sergio Vitale;Giampaolo Ferraioli;Vito Pascazio;Luis Gomez Deniz

{"title":"Enhanced Deep Learning SAR Despeckling Networks Based on SAR Assessing Metrics","authors":"Sergio Vitale;Giampaolo Ferraioli;Vito Pascazio;Luis Gomez Deniz","doi":"10.1109/LGRS.2025.3577907","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3577907","url":null,"abstract":"The proposal of deep learning (DL) solutions for synthetic aperture radar (SAR) image despeckling has recently widespread. Such solutions have been mainly designed from a DL perspective by leveraging the training and validation stage on the use of typical norm-based cost functions. For going beyond the DL perspective, in this letter, we propose an SAR-based validation stage by using SAR assessing metrics in the design and hyperparameter selection of neural networks. In the first phase, SAR assessing metrics may be used only as validation metrics to highlight critical issues that cannot be spotted with standard image-processing quality metrics. In a second phase, the same SAR assessing metrics may be used directly for enhancing the DL solution by addressing specific issues that arose during the previous SAR-based validation stage. To this aim, three different DL SAR despeckling solutions and four different SAR assessing metrics have been considered. The outcome of this analysis shows the importance of including SAR knowledge in the training and validation stages of the design of a DL solution for SAR image despeckling.","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":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314749","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

On-Orbit Spectral Calibration and Validation of GF5-02 Advanced Hyperspectral Imager GF5-02先进高光谱成像仪在轨光谱定标与验证

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-06 DOI: 10.1109/LGRS.2025.3577276

Hongzhao Tang;Chenchao Xiao;Wei Chen;Taixia Wu

{"title":"On-Orbit Spectral Calibration and Validation of GF5-02 Advanced Hyperspectral Imager","authors":"Hongzhao Tang;Chenchao Xiao;Wei Chen;Taixia Wu","doi":"10.1109/LGRS.2025.3577276","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3577276","url":null,"abstract":"On September 7, 2021, the GaoFen5-02 (GF5-02) Satellite, the new generation of Chinese hyperspectral remote sensing satellite was successfully launched. GF5-02, the successor to the GF5 satellite, was equipped with six advanced hyperspectral payloads. One of the most important payloads onboard the GF5-02 satellite, the advanced hyperspectral imager (AHSI) has a spatial resolution of 30 m, 330 bands in a spectral range of 380–2500 nm. The spectral resolution of the visible and near infrared (VNIR) and short-wave infrared (SWIR) bands are better than 5 and 10 nm, respectively. To analyze the spectral performance of the GF5-02 AHSI, an on-orbit spectral calibration method that utilizes atmospheric limb observations with an on-board calibration system was proposed in this letter. The on-orbit spectral calibration results were validated by atmospheric absorption features with synchronous measurements of surface reflectance and atmospheric parameters. For the GF5-02 AHSI, the shifts in the central wavelength of the VNIR band is 0.117nm, while the shifts in the full width at half maximum (FWHM) is 0.02 nm. In the SWIR band, these values are 0.25 nm for the central wavelength and 0.04 nm for the FWHM. The results demonstrate that the applied method is effective for on-orbit spectral calibration for GF5-02 AHSI.","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":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281272","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

DOA Estimation of Multibeam Frequency Beam Scanning LWAs Based on Sparse Bayesian Learning 基于稀疏贝叶斯学习的多波束频率扫描lda估计

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-06 DOI: 10.1109/LGRS.2025.3577369

Qingqing Zhu;Biyun Ma;Yide Wang;Jiaojiao Liu;Yuehui Cui

引用次数: 0

Mamba-Wavelet Cross-Modal Fusion Network With Graph Pooling for Hyperspectral and LiDAR Data Joint Classification 基于图池的mamba -小波交叉模态融合网络用于高光谱和激光雷达数据联合分类

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-05 DOI: 10.1109/LGRS.2025.3576778

Daxiang Li;Bingying Li;Ying Liu

{"title":"Mamba-Wavelet Cross-Modal Fusion Network With Graph Pooling for Hyperspectral and LiDAR Data Joint Classification","authors":"Daxiang Li;Bingying Li;Ying Liu","doi":"10.1109/LGRS.2025.3576778","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3576778","url":null,"abstract":"Recently, with the rapid development of deep learning, the collaborative classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) image has become a research hotspot in remote sensing (RS) technology. However, existing methods either only consider complementary learning of spatial-domain information or do not take into account the intrinsic dependencies between pixels and overlook the importance difference of pixels. In this letter, we propose a mamba-wavelet cross-modal fusion network with graph pooling (MW-CMFNet) for HSI and LiDAR joint classification. First, a two-branch feature extraction (TBFE) is used to extract spatial and spectral features. Then, in order to dig deeper into the complementary information of different modalities and fully fuse them under the guidance of frequency-domain information, a mamba-wavelet cross-modal feature fusion (MW-CMFF) module is devised, it aims to utilize mamba’s outstanding long-range modeling ability to learn complementary information in the spatial and frequency domains, Finally, the graph pooling module is designed to sense the intrinsic dependencies of neighboring pixels and explore the importance difference of pixels, rather than assigning the same weight to different pixels. Experiments on the Houston2013 and Trento datasets show that the MW-CMFNet achieves higher classification accuracy compared to other 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":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144264262","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

An Entropy-Driven Clustering and Semantic Association Framework for Cross-Domain Few-Shot Hyperspectral Image Classification 基于熵驱动聚类和语义关联的跨域少镜头高光谱图像分类框架

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-05 DOI: 10.1109/LGRS.2025.3576715

Yan Wang;Fengyi Zhang;Jing Tian;Xuewei Gong;Zhaokui Li

{"title":"An Entropy-Driven Clustering and Semantic Association Framework for Cross-Domain Few-Shot Hyperspectral Image Classification","authors":"Yan Wang;Fengyi Zhang;Jing Tian;Xuewei Gong;Zhaokui Li","doi":"10.1109/LGRS.2025.3576715","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3576715","url":null,"abstract":"Recently, few-shot learning (FSL) has shown promising results in the hyperspectral image (HSI) classification. However, in practical applications, insufficiently labeled training data make it difficult to capture the intraclass variation of novel classes, making it challenging for the model to learn inaccurate feature distributions, which in turn leads to inaccurate decision boundaries. To solve this problem, we propose an entropy-driven clustering and semantic association framework for cross-domain few-shot HSI classification (ECSA-FSL). We design a deep semantic association feature enhancement module (FEA), which first explores the potential semantic relationship between the source and target domains, and then constructs a cross-domain feature enhancement strategy to generate more discriminative features. In addition, we employ an entropy-driven clustering mechanism (EDC) to optimize the feature space distribution of the target domain. Our approach achieves remarkable classification accuracy with a small number of samples, particularly excelling in scenarios with high intraclass variability and limited training data. Experiments on two publicly available HSI datasets confirm that ECSA-FSL significantly outperforms existing FSL methods under similar conditions. The code is available at <uri>https://github.com/Li-ZK/ECSA-FSL-2025</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":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281337","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

Statistical Analysis of Ground-Based Vegetation-Transmission Beidou/GNSS Signal 地面植被传输北斗/GNSS信号的统计分析

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-04 DOI: 10.1109/LGRS.2025.3576641

Jie Li;Dongkai Yang;Yu Jiang;Feng Wang

{"title":"Statistical Analysis of Ground-Based Vegetation-Transmission Beidou/GNSS Signal","authors":"Jie Li;Dongkai Yang;Yu Jiang;Feng Wang","doi":"10.1109/LGRS.2025.3576641","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3576641","url":null,"abstract":"The utilization of global navigation satellite system reflectometry (GNSS-R) signals in remote sensing of land surface parameters has undergone significant advancements over the years. However, a paucity of analysis exists regarding the vegetation-transmitted GNSS signal, which represents an avenue for further research. In this study, an empirical investigation was conducted to ascertain the statistical characteristics of the power associated with GNSS signals emitted from vegetation, and the most appropriate distribution function model was obtained by a combinatorial test. The experimental results indicate that the vegetation-transmitted GNSS signal continues to conform to the characteristics of a Normal [right-hand circular polarization (RHCP)] and Weibull [left-hand circular polarization (LHCP)] distribution; however, significant variations are observed in the distribution parameters and the parameter value ranges. Furthermore, the results suggest a positive correlation between the k-order (<inline-formula> <tex-math>$k=1,2,3,4$ </tex-math></inline-formula>) moment order and the discrepancy in signals obtained by disparate GNSS antennas. Both antenna elevation angle and vegetation type exert an influence on moments of all orders, and the influence of the latter is more pronounced, thereby enabling the differentiation of vegetation types.","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":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291713","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

Sea Surface Temperature Image Completion Method Based on Multiscale Fourier Fusion Neural Operator 基于多尺度傅里叶融合神经算子的海表温度图像补全方法

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-06-04 DOI: 10.1109/LGRS.2025.3576674

Xin Chen;Zijie Zuo;Jie Nie;Xiu Li;Yaning Diao;Xinyue Liang

{"title":"Sea Surface Temperature Image Completion Method Based on Multiscale Fourier Fusion Neural Operator","authors":"Xin Chen;Zijie Zuo;Jie Nie;Xiu Li;Yaning Diao;Xinyue Liang","doi":"10.1109/LGRS.2025.3576674","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3576674","url":null,"abstract":"Sea surface temperature (SST) is a crucial metric in marine science, playing a pivotal role in forecasting and analyzing changes in the marine environment. However, remote sensing technologies often encounter issues where SST images are obscured by clouds, leading to data loss, thereby impacting marine environment prediction efficacy. Although many deep learning methods currently exist for reconstructing SST images, most focus on handling this task within the image domain, making it challenging to adapt to the chaotic nature of ocean systems. In addition, most methods only model at a single scale, which limits their ability to effectively capture the complex multiscale features in SST data. Therefore, this study proposes MSF_FNO, an image completion method based on multiscale Fourier fusion neural operator. MSF_FNO integrates multiscale feature fusion and frequency-domain neural operator technology to effectively overcome the limitations of single-scale feature processing and image-domain reconstruction in existing methods. This approach not only captures SST frequency-domain information and extracts structured features of SST images but also extracts critical features across multiple scales, ensuring global consistency and detailed features in reconstruction results. Experiments on the National Satellite Ocean Application Service (NSOAS) datasets demonstrate that MSF_FNO outperforms state-of-the-art (SOTA) methods in terms of reconstruction quality and robustness.","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":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281333","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