IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society最新文献_第9页

Modified PUMA/EPUMA Based on Forward and Backward Linear Prediction for DOA Estimation

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-20 DOI: 10.1109/LGRS.2025.3544068

Biyun Ma;Fu Zhu;Yide Wang;Qingqing Zhu;Jiaojiao Liu

引用次数: 0

Airplane State Discrimination From Single-Temporal High-Resolution Remote Sensing Images

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-19 DOI: 10.1109/LGRS.2025.3543674

Zizhen Li;Shichao Jin;Guangjun He;Xueliang Zhang;Pengming Feng;Han Fu;Ying Liang

{"title":"Airplane State Discrimination From Single-Temporal High-Resolution Remote Sensing Images","authors":"Zizhen Li;Shichao Jin;Guangjun He;Xueliang Zhang;Pengming Feng;Han Fu;Ying Liang","doi":"10.1109/LGRS.2025.3543674","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3543674","url":null,"abstract":"The absence of temporal information in single-temporal satellite remote sensing images presents a substantial challenge for target state discrimination. In this letter, a pioneering Remote Sensing Airplane State Discrimination Network (RSASDNet) is introduced, by leveraging the relationship between targets and their backgrounds in single-temporal high-resolution remote sensing images. To facilitate the study, we take airplane state discrimination as an example, and a Remote Sensing Airport Panoptic Segmentation with Airplane States Dataset (RSAPS-ASD) is constructed. RSASDNet incorporates two key innovations: 1) a scene knowledge graph generation module that constructs scene knowledge representation by capturing spatial relationships between airplane instances and their surrounding environment (e.g., taxiways and hangars); and 2) a novel graph-image hybrid convolution discrimination module that synergistically integrates structural knowledge and spatial semantic information through dedicated dual-branch learning. The effectiveness of the proposed method is validated using RSAPS-ASD, with experimental results demonstrating that RSASDNet achieves an impressive accuracy of 73.95% in airplane state discrimination.","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":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570685","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 Phase Synchronization Scheme for Spaceborne Multistatic SAR Based on OFDM-Chirp Signal

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-19 DOI: 10.1109/LGRS.2025.3543584

Qiang Lin;Weidong Yu;Shiqiang Li;Sisi Dong

{"title":"A Phase Synchronization Scheme for Spaceborne Multistatic SAR Based on OFDM-Chirp Signal","authors":"Qiang Lin;Weidong Yu;Shiqiang Li;Sisi Dong","doi":"10.1109/LGRS.2025.3543584","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3543584","url":null,"abstract":"The phase synchronization is a necessary condition for the normal operation of spaceborne multistatic synthetic aperture radar (SAR). The research on bistatic SAR phase synchronization is relatively mature, and some phase synchronization schemes have been verified in practical systems. Because of the limitations that existing synchronization schemes extended to the case of multistatic SAR, this letter proposes a phase synchronization scheme for spaceborne multistatic SAR. As the scheme suggests, we used orthogonal frequency-division multiplexing (OFDM)-Chirp signal to be the synchronization signal and exploited the orthogonality of subcarriers to separate the mixed synchronization signal. The decomposed signals are independently compressed to extract phase error. This scheme can simultaneously achieve phase synchronization for multiple radar platforms on the basis of satisfying the high accuracy of phase synchronization, greatly improves the efficiency of phase synchronization. Furthermore, the performance prediction and phase synchronization simulation for this synchronization scheme are presented, which verifies the feasibility of the proposed scheme.","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":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611894","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-Based Profiling of Buried Elliptical-Cylindrical Objects

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-18 DOI: 10.1109/LGRS.2025.3543290

Zahra Dastfal;Maryam Hajebi;Mansoureh Sharifzadeh;Ahmad Hoorfar

{"title":"Learning-Based Profiling of Buried Elliptical-Cylindrical Objects","authors":"Zahra Dastfal;Maryam Hajebi;Mansoureh Sharifzadeh;Ahmad Hoorfar","doi":"10.1109/LGRS.2025.3543290","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3543290","url":null,"abstract":"This letter presents a novel AI-based approach for subsurface profiling of buried dielectric objects. Using elliptical modeling, the method frames the inverse problem as a regression task, characterizing the target’s geometry with seven parameters: center coordinates, radii, tilt angle, sector angle, and permittivity. The sector angle enables the reconstruction of diverse shapes, enhancing flexibility and accuracy. The method exclusively utilizes amplitude-only scattered field data as a direct input to the convolutional neural network (CNN), eliminating the need for complex-valued data and qualitative preprocessing, thus bypassing their inherent limitations. Numerical results demonstrate the algorithm’s efficacy in reconstructing diverse profile shapes across a wide range of permittivity values, with a relative error of 16% in predicting the output of the network. The impact of factors, such as noise levels, measurement points, multi-frequency measurements, and out-of-range parameters, is also analyzed. Furthermore, a comparative analysis with the state-of-the-art global optimization techniques underscores the superior performance of the proposed method, highlighting its potential for significant advancements in the field. This algorithm also presents itself as an appealing candidate for use as an initializer for pixel-wise methods, replacing traditional qualitative approaches.","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":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553112","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

Lightweight Mamba Model Based on Spiral Scanning Mechanism for Hyperspectral Image Classification

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-18 DOI: 10.1109/LGRS.2025.3543315

Yu Bai;Haoqi Wu;Lili Zhang;Hanlin Guo

{"title":"Lightweight Mamba Model Based on Spiral Scanning Mechanism for Hyperspectral Image Classification","authors":"Yu Bai;Haoqi Wu;Lili Zhang;Hanlin Guo","doi":"10.1109/LGRS.2025.3543315","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3543315","url":null,"abstract":"Hyperspectral image classification (HSIC) has advanced significantly in recent years, driven by the development of advanced algorithms in remote sensing. However, the high-dimensional nature of hyperspectral data and the limited availability of labeled samples remain significant challenges, hindering the effectiveness of many existing methods. To address these limitations, we propose SpiralMamba, a novel classification framework inspired by the recent Mamba model, renowned for its efficient global feature extraction with linear complexity. To minimize the loss of spatial information when converting images into sequences for Mamba processing, we propose the innovative spiral scan embedding (SSE) module. In addition, the introduction of the Gaussian mask weighting (GMW) module enhances the feature weights around the central pixel, thereby improving the classifiability of the extracted features. We introduce the lightweight Mamba module (LWM), which reduces model parameters and computational requirements, making it particularly well-suited for HSIC with limited samples. Experimental results on three real datasets demonstrate that the SpiralMamba model outperforms existing methods in various performance metrics.","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":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563914","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

TS-BiT: Two-Stage Binary Transformer for ORSI Salient Object Detection TS-BiT：用于 ORSI 突出物体检测的两级二进制变换器

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-18 DOI: 10.1109/LGRS.2025.3542369

Jinfeng Zhang;Tianpeng Liu;Jiehua Zhang;Li Liu

{"title":"TS-BiT: Two-Stage Binary Transformer for ORSI Salient Object Detection","authors":"Jinfeng Zhang;Tianpeng Liu;Jiehua Zhang;Li Liu","doi":"10.1109/LGRS.2025.3542369","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3542369","url":null,"abstract":"Vision transformers (ViTs) have demonstrated superior performance in various remote sensing tasks, such as optical remote sensing image salient object detection (ORSI-SOD). However, the high resolution of remote sensing images and the substantial computational costs pose significant challenges for deploying existing methods on resource-constrained devices. Model binarization significantly reduces computational costs and storage requirements by constraining weights and activations to 1-bit representations, which has been widely explored in convolutional neural networks (CNNs). However, directly applying binary methods to ViTs poses challenges since quantization errors hinder the ability to capture the similarity between tokens, resulting in significant performance degradation in detecting salient objects in complex ORSI scenarios. To address this issue, we propose two-stage binary transformer (TS-BiT) for the ORSI-SOD task to preserve information on salient objects under 1-bit representation. Specifically, we design a two-stage central-aware softmax binarization (TCSB) strategy to reduce quantization errors arising from substantial discrepancies in the long-tail distribution of multihead attention. Furthermore, we develop a scalable hyperbolic tangent function to approximate the gradients of the Sign function within each binarization group, substantially mitigating quantization errors during the binarization of softmax attention. Extensive experiments demonstrate that our method outperforms existing binary ViT approaches on ORSSD, EORSSD, and ORSI-4199 datasets.","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":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521470","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

Evaluation of GEDI for Estimating the Vertical Distribution of PAI in Temperate Forests: A Case Study of the Conterminous United States

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-17 DOI: 10.1109/LGRS.2025.3542874

Duo Jia;Cangjiao Wang;Yanchen Bo

{"title":"Evaluation of GEDI for Estimating the Vertical Distribution of PAI in Temperate Forests: A Case Study of the Conterminous United States","authors":"Duo Jia;Cangjiao Wang;Yanchen Bo","doi":"10.1109/LGRS.2025.3542874","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3542874","url":null,"abstract":"The vertical leaf area index (LAI) is crucial for assessing photosynthetic and carbon sequestration dynamics and atmospheric interaction within terrestrial ecosystems. The global ecosystem dynamics investigation (GEDI), the first full-waveform lidar for monitoring global forest structure, has generated a vertical plant area index (VPAI) product at 5 m intervals. This study conducts a comprehensive assessment of the accuracy of GEDI’s vertical plant area index (PAI) across temperate forests in the conterminous United States and analyzes the impact of sensor parameters on the accuracy of the VPAI to provide insights for the optimal application of GEDI’s capabilities. The results show that GEDI can offer reliable layered PAI for heights exceeding 10 m. Substantial inaccuracies across various forest types were observed in layers of 5–10 m, with the worst accuracy observed in mixed forests. The impact of GEDI sensor parameters varies across different layers of PAI with GEDI’s Power beam being more accurate than its Coverage beam in layered PAI below 20 m; night observations are more accurate but also less available than day observations. A significant negative correlation between the signal-to-noise ratio (SNR) and errors of layered PAI exists below 15 m. Prioritizing the use of the Power beam and weighting the GEDI footprint based on the SNR for layers within 15 m, are recommended ways to improve the accuracy of the subsequent layered PAI mapping.","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":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570818","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

HCA-Net: An Instance Segmentation Network for High-Consequence Areas Identification From Remote Sensing Images

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-17 DOI: 10.1109/LGRS.2025.3542586

Xiaojun Dai;Weiyi Huang;Ming Xi;Yaqi Zhang;Deying Ma;Daguo Wang

{"title":"HCA-Net: An Instance Segmentation Network for High-Consequence Areas Identification From Remote Sensing Images","authors":"Xiaojun Dai;Weiyi Huang;Ming Xi;Yaqi Zhang;Deying Ma;Daguo Wang","doi":"10.1109/LGRS.2025.3542586","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3542586","url":null,"abstract":"The high-consequence area (HCA) is crucial for the safety management and operation of oil and gas pipelines. However, traditional models that rely on manual field investigations are costly, inefficient, and risky. Deep learning (DL)-based instance segmentation (IS) has the potential to enable automatic HCA identification. Unfortunately, the existing studies lack methods specifically designed to identify HCAs from remote sensing (RS) images. This letter proposes an IS network (HCA-Net) with spatial relation enhancement and mask decoupling refinement for HCA recognition is proposed. The proposed method first develops a spatial relation enhancement module (SREM) that queries the similarity of features at different spatial locations to represent spatial relations, further enhancing these features to promote completeness. Moreover, a unique decoupled mask refinement head (DMRH) is designed to refine the mask by decoupling boundary features from body features and optimally integrating them into the final features. Experiments on the constructed gas pipeline aerial dataset (GPAD) show that our method outperforms eight state-of-the-art (SOTA) methods. Compared to the baseline model mask R-CNN, HCA-Net improves the mAP of masks and the mIoU of HCA by 3.9% and 6.9%, 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":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496602","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

Few-Shot Hyperspectral Image Classification With Deep Fuzzy Metric Learning

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-17 DOI: 10.1109/LGRS.2025.3542571

Haojin Tang;Chao Zhang;Dong Tang;Xin Lin;Xiaofei Yang;Weixin Xie

{"title":"Few-Shot Hyperspectral Image Classification With Deep Fuzzy Metric Learning","authors":"Haojin Tang;Chao Zhang;Dong Tang;Xin Lin;Xiaofei Yang;Weixin Xie","doi":"10.1109/LGRS.2025.3542571","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3542571","url":null,"abstract":"Deep metric learning (DML) has shown promising results in few-shot hyperspectral image (HSI) classification. The core idea of DML is to learn a generalized metric space, in which pixels from unseen classes can be effectively classified with only a few labeled samples. However, the existing DML methods mainly adopt traditional Euclidean distance to achieve the feature metric, which ignores the category uncertainty of spatial-spectral features in mixed and edge pixels. To address this issue, we fully exploit fuzzy logic theory and propose a deep fuzzy metric learning (DFML) method for few-shot HSI classification. First, we design a novel hybrid CNN-transformer spatial-spectral feature extraction network to fully capture the spatial-spectral features of HSI pixels. Then, a fuzzy set representation method based on Gaussian membership function for spatial-spectral features is proposed, which describes the inherent fuzziness of the spatial-spectral features. Finally, to perform the fuzzy similarity measure between the fuzzy sets of query samples and prototypes, we construct a spatial-spectral fuzzy metric space, in which HSI pixels with category uncertainty in their features can be better classified under the condition of small-scale labeled samples. Extensive experimental results on three public HSI datasets demonstrate that the proposed DFML method outperforms the state-of-the-art few-shot HSI 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":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535511","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 the Transferred Brightness Temperature Variation Versus Feeder Antenna Position in a Fundamental Calibration Link

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-17 DOI: 10.1109/LGRS.2025.3542797

Ming Jin;Jiashuo Zhang;Lifei Jiang;Jieying He;Jiakai He;Jing Xu;Yunan Han;Ming Bai

{"title":"On the Transferred Brightness Temperature Variation Versus Feeder Antenna Position in a Fundamental Calibration Link","authors":"Ming Jin;Jiashuo Zhang;Lifei Jiang;Jieying He;Jiakai He;Jing Xu;Yunan Han;Ming Bai","doi":"10.1109/LGRS.2025.3542797","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3542797","url":null,"abstract":"In this work, the brightness temperature (TB) transfer from the microwave calibration target (MCT) to the feeder antenna in the near-field region is investigated, which is the fundamental physical process in microwave radiometer calibration. As in this scenario, the MCT and antenna cannot be separately considered as points like in far fields, it is interesting and important to study the TB characteristics of the target in cases of feeder antennas at different positions and with different aperture sizes. Recently, as reciprocity in the near field has been established, this fundamental issue can be now investigated. The study starts at a high frequency of 89 GHz when the free-space lambda is much smaller than the unit period of MCT; then, the distributions of the local TB contribution rate are calculated to understand the possible TB transfer variation. It is found that the key factor for the TB variation is the illumination area upon the array-type MCT, and as the footprint is sufficiently large to cover several pyramid units, the TB can be stable versus relative position.","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":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553455","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