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

{"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}

{"title":"DOA Estimation of Multibeam Frequency Beam Scanning LWAs Based on Sparse Bayesian Learning","authors":"Qingqing Zhu;Biyun Ma;Yide Wang;Jiaojiao Liu;Yuehui Cui","doi":"10.1109/LGRS.2025.3577369","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3577369","url":null,"abstract":"Multibeam frequency beam scanning leaky wave antennas (FBS-LWAs) enable a compact system with reduced bandwidth but introduce parasitic interference that impairs the direction-of-arrival (DOA) estimation. To solve this issue, this letter proposes a novel DOA estimation method tailored for multibeam FBS-LWAs. Theoretical analysis reveals that the true DOAs align with multiple peaks in the radiation pattern. Based on this insight, a cropped hypercomplete set is constructed via grid refinement in the frequency-space domain. The sparse Bayesian learning (SBL) algorithm, enhanced by singular value decomposition (SVD-SBL), is then employed to suppress parasitic peaks and accurately recover the true DOAs. The simulation results demonstrate the proposed method’s effectiveness androbustness.","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":"144314720","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}

{"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}

{"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}

{"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}

{"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}

{"title":"Enhanced YOLOv11-Based River Aerial Image Detection Research","authors":"Lei Zhang;Ao Zheng;Xiaoyan Sun;Zhipeng Sun","doi":"10.1109/LGRS.2025.3576640","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3576640","url":null,"abstract":"The unmanned aerial vehicle (UAV) encounters challenges in detecting similar small targets during target detection tasks. Consequently, the current target detection algorithms struggle to accurately identify river debris, overgrazing, and suspected sand mining activities. To address the issues of low precision and high complexity associated with small target detection in the existing models, this article introduces an enhanced version of YOLOv11, referred to as PAB-YOLOv11. First, the C3K2-PPA module is employed to replace the C3K2 module within the backbone network. Additionally, a multibranch fusion approach is utilized to enhance the model’s feature extraction capabilities for small targets across various scales. The attention for fine-grained classification (AFGC) attention mechanism is integrated between the neck network and the detection head to improve the recognition of similar objects. This is achieved by emphasizing local fine features and dynamically adjusting the distribution of attention. The experimental results demonstrate that, on the dataset obtained from the Sanggan River basin, the mAP@0.5 of PAB-YOLOv11 reaches 64.9%, reflecting an improvement of 2.1% over the original YOLOv11 model. Compared to the three mainstream models, YOLOv5s, YOLOv8s, and YOLOv11n, PAB-YOLOv11 achieves improvements of 3.1%, 3.2%, and 2.6% in mAP@0.5, respectively. When compared to more advanced models, such as RT-DETR and DINO, PAB-YOLOv11 also shows enhancements in mAP@0.5 of 5.1% and 2.8%, respectively. These findings indicate that the PAB-YOLOv11 model proposed in this study is an effective method for river channel inspection.","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":"144272810","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}

{"title":"SMGNet: A Semantic Map-Guided Multitask Neural Network for Remote Sensing Image Semantic Change Detection","authors":"Jiang Long;Sicong Liu;Mengmeng Li","doi":"10.1109/LGRS.2025.3576673","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3576673","url":null,"abstract":"Semantic change detection (SCD) aims to identify potential Earth surface changes, including their location and class, from multitemporal remote sensing images. However, the underdetection and pseudochange issues in existing SCD methods severally limit their effectiveness in diverse ground scenarios. To address these issues, a semantic map-guided network, namely, SMGNet, is proposed based on a multitask architecture designed to identify potential land-cover changes from bitemporal high-resolution remote sensing images. A robust feature extractor is first developed to extract multiscale contextual information while retaining fine-grained spatial details, thus enhancing the semantic representation of complex objects with irregular shapes and large sizes. To address the issue of underdetection, we integrate historical semantic information derived from pretemporal land-cover maps into the model using a semantic map encoder module. A semantic fusion module based on Bayesian theory is developed to highlight salient changed information, thus reducing pseudochanges caused by the same ground objects with spectra variations. Experimental results obtained in a public SCD dataset demonstrate the effectiveness of the proposed method in identifying various semantic changes. Results indicate that the proposed SMGNet achieved the highest detection accuracy, exceeding nine existing methods by 14.81%–41.28% and 8.45%–40.31% in terms of separated kappa (SeK) and <inline-formula> <tex-math>$F1$ </tex-math></inline-formula>-score (<inline-formula> <tex-math>$F1_{text {scd}}$ </tex-math></inline-formula>) metrics on the high-resolution SCD (HRSCD) dataset, respectively. The proposed method effectively alleviated pseudochanges induced by spectra and temporal differences, and accurately detecting these changed objects with irregular shapes and large sizes. The detected results exhibited high interclass compactness and well-defined boundaries. Code and data are available at <uri>https://github.com/long123524/SMGNet</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":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281335","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}

{"title":"Scattering Characteristics Guided Network for ISAR Space Target Component Segmentation","authors":"Fengjun Zhong;Fei Gao;Tianjin Liu;Jun Wang;Jinping Sun;Huiyu Zhou","doi":"10.1109/LGRS.2025.3576662","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3576662","url":null,"abstract":"Affected by the large dynamic range of gray values, strong scattering point edge effect, noise, and clutter, inverse synthetic aperture radar (ISAR) images have problems such as boundary blurring and target discontinuity, which bring great challenges to ISAR space target component segmentation. In this letter, a novel ISAR space target component segmentation method, called scattering characteristics guided network (SCGN), is proposed. First, a cross-scale self-attention module (CSSAM) is proposed, which establishes global relationships in different dimensions during cross-scale feature fusion, refining the detailed features of the target while suppressing high sidelobe scattering points and noise. Second, a novel component scattering center extractor (CSCE) is proposed to combine scattering center distribution with the network via explicit supervision. Finally, a novel scattering-characteristic-assisted segmentation head (SCASH) is proposed, which introduces the scattering characteristics of each component into the mask segmentation process and models the semantic interdependencies over long distances through a spatial attention mechanism to achieve fine-grained component segmentation. Experimental results on the ISAR simulation dataset and realistic ISAR images show that SCGN outperforms existing 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":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472529","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}

{"title":"Quantum-Enhanced Water Quality Monitoring: Exploiting $Phi$ Sat-2 Data With Quanvolution","authors":"Francesco Mauro;Francesca Razzano;Pietro Di Stasio;Alessandro Sebastianelli;Gabriele Meoni;Gilda Schirinzi;Paolo Gamba;Silvia Liberata Ullo","doi":"10.1109/LGRS.2025.3576677","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3576677","url":null,"abstract":"Coastal water quality monitoring is crucial for environmental sustainability and public health. This work introduces a very cutting-edge methodology, using <inline-formula> <tex-math>$Phi $ </tex-math></inline-formula>Sat-2 multispectral data and quanvolutional neural networks (QNNs) to explore quantum-enhanced machine learning (ML) for water contaminant assessment. By integrating quantum preprocessing into a classical regression model, it is possible to achieve a significant reduction in model parameters while maintaining high predictive accuracy. In addition, this work introduces an innovative dataset that integrates simulated <inline-formula> <tex-math>$Phi $ </tex-math></inline-formula>Sat-2 spectral data with Copernicus Marine Service biogeochemical products, ensuring a strong alignment between satellite observations and reference turbidity measurements. Our results show that quantum models use up to 98% fewer parameters than their classical counterparts, while achieving a 6.9% improvement in the Pearson correlation coefficient between the <inline-formula> <tex-math>$Phi $ </tex-math></inline-formula>Sat-2 preprocessed bands and the ground-truth turbidity values, compared with the case without quantum preprocessing. In addition, the root mean square error (RMSE) improves by 7.3% over the classical baseline. These findings highlight the potential of quantum-assisted remote sensing (RS) to enable more efficient and scalable analysis of large-scale water contaminant data, paving the way for advanced big data approaches in water quality monitoring.","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":"144281338","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}