IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society最新文献_第7页

Integrated Physically Interpretable Model for SAR Target Recognition: Unified Fusion of Electromagnetic and Deep Features

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-05 DOI: 10.1109/LGRS.2025.3548166

Leiyao Liao;Zishuo Hong;Ziwei Liu;Gengxin Zhang

{"title":"Integrated Physically Interpretable Model for SAR Target Recognition: Unified Fusion of Electromagnetic and Deep Features","authors":"Leiyao Liao;Zishuo Hong;Ziwei Liu;Gengxin Zhang","doi":"10.1109/LGRS.2025.3548166","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3548166","url":null,"abstract":"Synthetic aperture radar (SAR) target recognition has been a prominent research topic in the field of remote sensing image processing. Traditional methods can extract physical features for SAR target recognition, but they are often time-consuming. Deep learning-based methods can learn representative features, but they often operate as black boxes and lack interpretability. This letter introduces an integrated physically interpretable model (IPIM) for SAR target recognition, which unifies electromagnetic and deep learning features. Our method is an integrative model built with an end-to-end mechanism, achieving promising performance and high time efficiency. Specifically, our method employs a deep-unfolding network to learn physical features by incorporating the generative process of complex SAR images. Additionally, a feature fusion module is designed to combine physical features, which reflect local target characteristics, with deep features that capture global information. Experimental results on a measured SAR image dataset demonstrate that our method effectively learns physical features and achieves high performance in target recognition.","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":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716482","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

Fine-Grained Object Detection of Satellite Video in the Frequency Domain

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-05 DOI: 10.1109/LGRS.2025.3548104

Yuhan Sun;Shengyang Li

{"title":"Fine-Grained Object Detection of Satellite Video in the Frequency Domain","authors":"Yuhan Sun;Shengyang Li","doi":"10.1109/LGRS.2025.3548104","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3548104","url":null,"abstract":"Satellite video objects often have small scales and the occlusion of their distinguishable regions. Existing fine-grained object detection methods estimate object locations and categories by enhancing object features and increasing feature differences between categories. However, they fail to account for the impact of limited feature information on fine-grained detection, specifically reflected in two aspects: 1) limited pixels lead to limited features. The small pixel coverage of satellite video objects results in a low upper bound on the available feature information, hindering significant improvements in fine-grained detection accuracy and 2) limited differences exacerbate limitations. Occlusion of distinguishable regions in small-scale objects exacerbates the indistinctness of features between different fine-grained categories. It prevents the network from accurately learning unique features for certain object classes, thereby degrades detector performance. To address these challenges, we propose a frequency auxiliary network (FANet), which integrates frequency domain feature learning into fine-grained object detection networks. Specifically, we propose the spectral augmented module (SAM) to extract multispectral features from various frequency components of satellite video frames to complement spatial-domain features, enabling the network to leverage hidden semantic information from the frequency domain. In addition, to better distinguish small-scale objects from the background and emphasize fine-grained category features, we design the frequency domain attention (FDA) mechanism. FDA assigns dynamic weights to spatial and frequency domain features, suppressing background information and enhancing feature differences between fine-grained categories. Extensive experiments on the SAT-MTB dataset demonstrate that FANet achieves superior performance compared to 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":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735417","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

Multilateral Cascading Network for Semantic Segmentation of Large-Scale Outdoor Point Clouds

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-04 DOI: 10.1109/LGRS.2025.3547913

Haoran Gong;Haodong Wang;Di Wang

{"title":"Multilateral Cascading Network for Semantic Segmentation of Large-Scale Outdoor Point Clouds","authors":"Haoran Gong;Haodong Wang;Di Wang","doi":"10.1109/LGRS.2025.3547913","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3547913","url":null,"abstract":"Semantic segmentation of large-scale outdoor point clouds is of significant importance in environment perception and scene understanding. However, this task continues to present a significant research challenge, due to the inherent complexity of outdoor objects and their diverse distributions in real-world environments. In this study, we propose the multilateral cascading network (MCNet) designed to address this challenge. The model comprises two key components: a multilateral cascading attention enhancement (MCAE) module, which facilitates the learning of complex local features through multilateral cascading operations; and a point cross-stage partial (P-CSP) module, which fuses global and local features, thereby optimizing the integration of valuable feature information across multiple scales. Our proposed method demonstrates superior performance relative to state-of-the-art approaches across two widely recognized benchmark datasets: Toronto3D and SensatUrban. Especially on the city-scale SensatUrban dataset, our results surpassed the current best result by 2.1% in overall mean intersection over union (mIoU) and yielded an improvement of 15.9% on average for small-sample object categories comprising less than 2% of the total samples, in comparison to the baseline method. Our code is available at <uri>https://github.com/ranhaogong/MCNet</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":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667443","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

Estimation of Top-of-Atmosphere Net Radiation From AVHRR Data

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-04 DOI: 10.1109/LGRS.2025.3547834

Chuan Zhan;Yong Chen;Zuohua Miao;Wenjing Li;Xiangyang Zeng;Jun Li

{"title":"Estimation of Top-of-Atmosphere Net Radiation From AVHRR Data","authors":"Chuan Zhan;Yong Chen;Zuohua Miao;Wenjing Li;Xiangyang Zeng;Jun Li","doi":"10.1109/LGRS.2025.3547834","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3547834","url":null,"abstract":"The top-of-atmosphere (TOA) net radiation (NR), a key component of the Earth’s energy budget, directly indicates the imbalance between incoming solar radiation from the space and outgoing shortwave/longwave radiation from the Earth’s climate system. However, the spatial resolutions of the existing TOA NR products are too coarse to provide enough details when analyzing the energy budget at regional scales. This letter presents a direct machine learning method to estimate TOA NR by directly linking advanced very high-resolution radiometer (AVHRR) TOA radiances with TOA NR determined by Clouds and the Earth’s Radiant Energy System (CERES) and other information, such as the solar/viewing geometry, land surface temperature (LST), and cloud top temperature determined by Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). Models are built using a gradient boosting regression tree. Independent test results show that the root mean square error (RMSE) of the model for estimating instantaneous values is 25.16 W/m2. Daily results are converted from the instantaneous results using climatology conversion ratios derived from CERES daily and hourly data. Intercomparisons of the daily results with CERES TOA NR data show that the RMSEs of the estimated AVHRR NR are less than 30 W/m2. The developed algorithm may contribute to generating relatively high-resolution (5-km) AVHRR TOA NR dataset, which will be beneficial in analyzing the regional energy budget.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675973","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 2D-TwISTA for 2-D Sparse ISAR Imaging

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-04 DOI: 10.1109/LGRS.2025.3547408

Quan Huang;Lei Zhang;Shaopeng Wei;Jia Duan

引用次数: 0

Reconstruction-Based 2DPCANet for Unsupervised SAR Image Change Detection

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-04 DOI: 10.1109/LGRS.2025.3547844

Jie Wu;Qimeng Zhang;Rongrong Li;Luis Gomez;Alejandro C. Frery

引用次数: 0

A Pixel Expansion-Based Improvement in Dense Nesting Structures for Infrared Small Target Detection

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-04 DOI: 10.1109/LGRS.2025.3547899

Zhichao Zhao;Hao Wang;Haiyan Li;Jundon Yang;Pengfei Yu

{"title":"A Pixel Expansion-Based Improvement in Dense Nesting Structures for Infrared Small Target Detection","authors":"Zhichao Zhao;Hao Wang;Haiyan Li;Jundon Yang;Pengfei Yu","doi":"10.1109/LGRS.2025.3547899","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3547899","url":null,"abstract":"In infrared detection, the identification of weak targets is often hindered by low pixel count and resolution, leading to a scarcity of semantic details for small targets and significant blurring of boundary information. To solve these problems, we propose a novel approach for the enhanced cross-stage feature matching network (ECFNet) to learn features beyond a single-scale data source by introducing pixel expansion branching in this letter. First, we introduce the feature attention enhancement module (FAEM), which uses rapid edge feature extraction to effectively enhance the boundary information of weak targets after pixel expansion, thereby improving the network’s fine-grained detection capability. Moreover, inspired by the Monte Carlo attention mechanism used in medical image processing, we introduce the stage randomness enhancement module (SREM) to direct the network’s focus toward small target regions rather than background noise during the learning process, allowing the network to adapt to various random situations independent of a fixed structure. Furthermore, we design a cross-feature matching module (CFMM), which effectively aggregates shallow profile information and deeper semantics at the center of the network, facilitating efficient information transfer and precise feature assignment, thereby narrowing the feature gap between the encoding and decoding stages. Our network achieves an intersection-over-union (IoU) ratio of 78.51% on the publicly available NUAA-SIRST dataset. The experimental results of NUDT-SIRDT and infrared small target detection (IRSTD)-1k can be found in <uri>https://github.com/bobo66597/ECF</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":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667442","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

LSTMAR-Net: A Deep Learning Model for Improving Time Resolution of AIRS Satellite Arctic Tropospheric Ozone Data

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-03 DOI: 10.1109/LGRS.2025.3547015

Kai Yu;Le Cao;Yuhan Luo;Kaili Wu;Qidi Li;Fuqi Si

{"title":"LSTMAR-Net: A Deep Learning Model for Improving Time Resolution of AIRS Satellite Arctic Tropospheric Ozone Data","authors":"Kai Yu;Le Cao;Yuhan Luo;Kaili Wu;Qidi Li;Fuqi Si","doi":"10.1109/LGRS.2025.3547015","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3547015","url":null,"abstract":"Arctic tropospheric ozone is a critical chemical component of the Earth’s atmosphere with diverse impacts on regional ecological environments and climate systems. Stratosphere-troposphere exchange (STE) is an important factor in the increase in Arctic tropospheric ozone. In this study, a long short-term memory with attention and residual network (LSTMAR-Net) model was developed to improve the time resolution of atmospheric infrared sounder (AIRS) satellite Level 3 (<inline-formula> <tex-math>$L3$ </tex-math></inline-formula>) product tropospheric ozone profile data from multiple stations in the Arctic. The model performed well on the test set. The Pearson correlation coefficients for the upper troposphere at the Eureka station are mostly above 0.70, with root mean squared error (RMSE) and mean absolute error (MAE) below 0.027 ppmv. Similarly, in the upper troposphere at the Alert station, the Pearson correlation coefficients are mostly above 0.60, with RMSE and MAE values below 0.028 ppmv. The model successfully simulated ozone changes in the STE that were not detectable by AIRS data. The model also simulated the horizontal transport processes of ozone and successfully explained the evolution of ozone concentration changes.","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":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908811","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611900","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

A Prompter Guided Distribution Matching for Zero-Shot SAR ATR

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-03 DOI: 10.1109/LGRS.2025.3547006

Chunhui Qu;Yujia Gan;Yaoqing Li;Long Tian;Ting Zhang;Mengqi Shen;Bo Chen

引用次数: 0

Satellite Attitude Estimation Based on Whole-to-Part Decoupling 基于整体对部分解耦的卫星姿态估计

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-03 DOI: 10.1109/LGRS.2025.3545438

Zipeng Zhang;Wenzheng Wang;Chenwei Deng;Yuqi Han;Zhuokai Li

{"title":"Satellite Attitude Estimation Based on Whole-to-Part Decoupling","authors":"Zipeng Zhang;Wenzheng Wang;Chenwei Deng;Yuqi Han;Zhuokai Li","doi":"10.1109/LGRS.2025.3545438","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3545438","url":null,"abstract":"Satellite attitude estimation is a key technology in on-orbit servicing missions. However, occlusions on the satellite’s surface in space introduce local omissions in satellite images, resulting in incomplete extraction of global satellite features. This causes significant biases in the mapping from features to attitude. Besides, there is a coupling between the parameters in rotation representations such as Euler angles and quaternions, making it difficult to improve the accuracy of each attitude parameter simultaneously. Existing methods focus on improving feature extraction performance and lack an evaluation of the relationship between satellite structure and attitude. To address these issues, we proposed a satellite attitude estimation method based on whole-to-part decoupling to separate features and reconstruct rotation representation, balancing attitude estimation biases. Specifically, we constructed a multibranch mapping with feature decoupling to select robust local features that contribute to attitude estimation, mitigating the impact of occlusion on feature extraction. Meanwhile, we designed an explicit rotation representation to decouple attitude parameters and match the relevant mappings for each parameter, reducing the estimation biases. The experimental results on public datasets demonstrate that the proposed method 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":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667205","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