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

Deep Learning-Based Multisensor Approach for Precision Agricultural Crop Classification Based on Nitrogen Levels 基于深度学习的多传感器氮素水平精准农作物分类方法

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-31 DOI: 10.1109/LGRS.2025.3556122

J. Reji;Rama Rao Nidamanuri

{"title":"Deep Learning-Based Multisensor Approach for Precision Agricultural Crop Classification Based on Nitrogen Levels","authors":"J. Reji;Rama Rao Nidamanuri","doi":"10.1109/LGRS.2025.3556122","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3556122","url":null,"abstract":"Accurate classification of crops at the patch level based on nutrient status, particularly nitrogen (N) levels, is essential for advancing precision agriculture (PA). While recent advancements in remote sensing, scalable computing, and visualization technologies have enabled high-resolution plant monitoring, the spectral similarity among crops remains a challenge for precise classification using remote sensing data. This study introduces a multisensor fusion approach, integrating terrestrial LiDAR point cloud data and WorldView-III multispectral imagery within a deep learning (DL) framework to classify cabbage, eggplant, and tomato across different N levels. By combining structural and spectral information, this method effectively captures N-induced growth variations, leading to improved crop discrimination. Our results demonstrate that applying a deep convolutional neural network (DCNN) model to the fused dataset enhances classification accuracy by 13%–16% compared to using multispectral data alone. The incorporation of LiDAR data plays a key role in capturing canopy structure, significantly improving classification performance. Additionally, our DL approach outperforms traditional machine-learning methods, including the random forest (RF) classifier, reinforcing the advantages of DL for N-sensitive crop classification. By leveraging multisensor integration and DL, this study presents a robust and scalable approach for enhancing crop classification accuracy, with significant potential for advancing PA and site-specific nutrient management.","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":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908349","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

Effect of 2-D Turntable Pointing Performance of a Moon-Based Sensor on Geolocation Accuracy 月基传感器的二维转盘指向性能对地理定位精度的影响

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-31 DOI: 10.1109/LGRS.2025.3556099

Yin Jin;Huadong Guo;Mengxiong Zhou;Hanlin Ye;Guang Liu

{"title":"Effect of 2-D Turntable Pointing Performance of a Moon-Based Sensor on Geolocation Accuracy","authors":"Yin Jin;Huadong Guo;Mengxiong Zhou;Hanlin Ye;Guang Liu","doi":"10.1109/LGRS.2025.3556099","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3556099","url":null,"abstract":"A Moon-based sensor offers a unique view for continuous Earth observation. The 2-D turntable’s pointing performance is a critical factor influencing geolocation accuracy. The vast distance between the Earth and the Moon amplifies minor pointing errors of the turntable into significant geolocation inaccuracy. By establishing a geometric model, an analytic expression of Earth’s trajectory from the Moon-based view is derived. Three critical issues are discussed: 1) the Earth’s 18.6-year trajectory forms a <inline-formula> <tex-math>$16^{circ } times 14^{circ }$ </tex-math></inline-formula> envelope, which determines the observation range for the sensor. The rotation angle and position of the envelope vary at different lunar locations, while its size and shape remain consistent; 2) geolocation errors caused by temporal interval vary periodically with a half-sidereal month cycle and can be compensated by calculating Earth’s velocity, while errors due to the step angle show irregular oscillations. Without calibration, both parameters can introduce geolocation errors on the scale of hundreds of kilometers. Reducing both parameters can significantly improve geolocation accuracy; and 3) even with optimization of both parameters, the geolocation accuracy cannot be reduced to within a single pixel. To achieve geolocation accuracy within design requirements, it is necessary to not only optimize these two factors but also adopt additional measures to improve precision. All these insights will inform the parameter optimization and design of the Moon-based sensor for future applications.","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":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143824654","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

MOSR: An Open-Set Recognition Network Based on Masked Autoencoder for Ship Detection 基于掩模自编码器的船舶检测开集识别网络

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-28 DOI: 10.1109/LGRS.2025.3555477

Pinjie Li;Jing Wu;Qianchuan Zhao;Xiaoyan Liu;Liguo Liu;Ziyuan Yang;Tao Zhang

{"title":"MOSR: An Open-Set Recognition Network Based on Masked Autoencoder for Ship Detection","authors":"Pinjie Li;Jing Wu;Qianchuan Zhao;Xiaoyan Liu;Liguo Liu;Ziyuan Yang;Tao Zhang","doi":"10.1109/LGRS.2025.3555477","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3555477","url":null,"abstract":"In remote sensing image classification, open-set recognition (OSR) poses a significant challenge, aiming to accurately classify known categories while effectively rejecting unknown class samples or identifying potential novel categories. Although existing methods have made strides in recognizing known classes, they exhibit notable limitations in handling unknown class samples. This letter introduces an OSR model for ship detection, termed masked autoencoder (MAE)-based OSR (MOSR), which leverages the robust representation learning capabilities of the MAE. MOSR not only sustains high accuracy in the recognition of known classes but also markedly enhances the performance in the identification of unknown class samples. Comprehensive experiments on the custom RSHIP-137 remote sensing dataset validate the efficacy and superiority of the MOSR model. Compared with the state-of-the-art (SOTA) adversarial reciprocal point learning (ARPL) method, MOSR shows substantial improvements in both known class recognition accuracy and the area under the receiver operating characteristic curve (AUROC) for unknown class recognition for ship detection. This study presents a novel solution for OSR in remote sensing ship detection and offers valuable insights for future research.","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":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888322","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

Unlocking the Potential of Multisource Satellites for Harmonized Algal Bloom Detection in Plateau Lakes 释放多源卫星在高原湖泊协调藻华探测中的潜力

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-28 DOI: 10.1109/LGRS.2025.3554486

Chen Yang;Zhenyu Tan;Yimin Li;Hongtao Duan

{"title":"Unlocking the Potential of Multisource Satellites for Harmonized Algal Bloom Detection in Plateau Lakes","authors":"Chen Yang;Zhenyu Tan;Yimin Li;Hongtao Duan","doi":"10.1109/LGRS.2025.3554486","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3554486","url":null,"abstract":"Algal blooms pose a considerable threat to both human health and the natural environment, their presence even extending to lakes situated across plateau regions. The geolocation and volatile climate conditions render it quite a challenge for algal bloom detection with single optical satellite across plateau lakes. To address this limitation, this study aims to achieve algal bloom detection through five satellites with high spatial resolution based on machine learning (ML) across nine lakes in Yunnan Province, China. Noteworthy findings from the study include: 1) achieving high accuracy on algal bloom detection over 0.82 based on random forest (RF) across multiple lakes and multisensors; 2) evaluating quantitatively and qualitatively algal bloom outbreaks in five out of nine plateau lakes in 2019; and 3) establishing a severity ranking of algal bloom occurrences, with Lake Dianchi exhibiting the highest severity, followed by Lake Xingyun, Lake Chenghai, Lake Erhai, and Lake Qilu. In general, this work demonstrated the effectiveness in multisource satellites observation with rational precision. These results laid the foundation for implementing a practical technical framework that enables precise algal bloom detection and facilitates comparative analyses among different lakes.","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":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800902","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 Physically Based Neural Network for Fast Infrared Atmospheric Transmittance Simulation 基于物理的快速红外大气透过率模拟神经网络

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-28 DOI: 10.1109/LGRS.2025.3555238

Mingkun Liu;Xiangtao Wang;Zhicheng Sheng;Yaojiao Wang;Kaiwen Wang;Jianming Wang;Zhong Zhang;Lei Guan

{"title":"A Physically Based Neural Network for Fast Infrared Atmospheric Transmittance Simulation","authors":"Mingkun Liu;Xiangtao Wang;Zhicheng Sheng;Yaojiao Wang;Kaiwen Wang;Jianming Wang;Zhong Zhang;Lei Guan","doi":"10.1109/LGRS.2025.3555238","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3555238","url":null,"abstract":"The atmospheric radiation transfer model (RTM) is the foundation and core of the physical retrieval of atmospheric and surface parameters in remote sensing as well as the assimilation of satellite observation data. Infrared RTM is widely used in the retrieval of Earth’s surface temperature, cloud detection, and water vapor remote sensing. This letter is committed to exploring the fast and accurate simulation of atmospheric radiation transfer over the clear-sky ocean using deep learning algorithms, with the key issue being the fast calculation of atmospheric transmittance. We have constructed a neural infrared transmittance model (NITM) for atmospheric radiation transfer simulation from thermal infrared channels and applied it to the visible infrared imaging radiometer suite (VIIRS) M15 and M16 channels. In addition, to improve the model’s performance, we conducted a sensitivity analysis on the inputs and selected predictors with high sensitivity to transmittance. The comparison results with the line-by-line radiative transfer model (LBLRTM) indicate that the algorithm achieves fast and precise simulation of atmospheric radiation transfer, with the simulated brightness temperature (BT) accuracy better than 0.1 K.","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":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850829","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

High-Resolution Remote Sensing Change Detection With Edge-Guided Feature Enhancement 利用边缘引导特征增强技术进行高分辨率遥感变化检测

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-28 DOI: 10.1109/LGRS.2025.3555584

Changyuan You;Nan Wang;Dehui Zhu;Rong Liu;Wei Li

{"title":"High-Resolution Remote Sensing Change Detection With Edge-Guided Feature Enhancement","authors":"Changyuan You;Nan Wang;Dehui Zhu;Rong Liu;Wei Li","doi":"10.1109/LGRS.2025.3555584","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3555584","url":null,"abstract":"High-resolution (HR) remote sensing image change detection aims to identify surface changes; however, complex scenes and irregular object edges pose significant challenges to achieving accurate results. Existing methods leverage upsampling, downsampling, or dilated convolution to capture multiscale spatial features and fuse fine-scale details into coarse-scale features using concatenation, addition, or skip connections to enhance edge information. However, these direct fusion operations can cause fine edge details to be overshadowed by dominant regional features. To address this, we propose an edge-guided change detection (EGCD) network that improves edge preservation and detection accuracy. In the encoding stage, a region-edge feature extraction module (REM) is introduced to extract regional and edge features in parallel using a two-branch structure for each temporal image. The edge and regional features from the two temporal images are then fused independently via a separation feature fusion (SFF) module, preventing fine edge details from being dominated by regional features. In the decoding stage, a edge enhancement upsampling (EEU) module uses edge features to guide the reconstruction of regional features, ensuring precise boundary delineation. Experiments on public datasets validate the effectiveness and robustness of the proposed network.","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":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835386","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 Weak Target Detection Based on Weighted Difference Visibility Graph 基于加权差分可见性图的海面弱目标检测

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-28 DOI: 10.1109/LGRS.2025.3555560

Yifei Fan;Xinbao Wang;Shichao Chen;Zixun Guo;Jia Su;Mingliang Tao;Ling Wang

{"title":"Sea-Surface Weak Target Detection Based on Weighted Difference Visibility Graph","authors":"Yifei Fan;Xinbao Wang;Shichao Chen;Zixun Guo;Jia Su;Mingliang Tao;Ling Wang","doi":"10.1109/LGRS.2025.3555560","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3555560","url":null,"abstract":"The detection of small floating targets is a challenging problem for maritime surveillance radar. To achieve effective detection within complex sea clutter background, an innovative graph feature detector is proposed in this letter. First, the received radar sequences are converted into graphs to capture the correlation of signals. Then, three graph features weight peak height (WPH), graph complexity (GC), and graph entropy (GE) of weighted difference visibility graph (WDVG) are proposed. The topological properties of the WDVGs constructed from the phase domain of radar echoes is analyzed, which provides insights into the underlying dynamics structures of the observed phenomena. In the detection part, an improved false alarm rate controllable (FAC) concave detector is designed, which is based on the concave hull-learning algorithm. Experiments results based on the real measured IPIX radar datasets confirm that the proposed method has a better performance compared with the existing feature-based methods, especially under shorter observation time (0.128 s).","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":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839900","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

Depiction of Subsurface Leak Areas Based on Adaptive Sensitive Frequency Attribute Analysis 基于自适应敏感频率属性分析的地下泄漏区域描述

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-27 DOI: 10.1109/LGRS.2025.3554216

Qi Cheng;Fan Cui;Guoqi Dong;Guixin Zhang;Ran Wang;Mengli Zhang

{"title":"Depiction of Subsurface Leak Areas Based on Adaptive Sensitive Frequency Attribute Analysis","authors":"Qi Cheng;Fan Cui;Guoqi Dong;Guixin Zhang;Ran Wang;Mengli Zhang","doi":"10.1109/LGRS.2025.3554216","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3554216","url":null,"abstract":"Ground penetrating radar (GPR) frequency attributes are commonly used to describe subsurface structures and characterize anomalous media. Single-frequency slices face challenges in capturing the broadband characteristics of GPR data, so the fusion of extracted multi-frequency components using a fusion algorithm can be effective. However, selecting appropriate attributes and mapping them to media characterization remain unresolved challenges. In this study, we propose a workflow based on adaptive sensitive frequency attribute analysis (ASFAA) to address these issues. First, the generalized S-transform (GST) is used to calculate the multi-frequency attributes of GPR data. Then, a sensitive feature analysis method combining hierarchical clustering and correlation analysis is employed to reduce redundancy in frequency attributes. Multi-frequency data are fused using the potential of heat-diffusion for affinity-based transition embedding (PHATE), which performs affinity-based diffusion embedding. The workflow is tested with synthetic and field data, yielding characterization results consistent with both the forward model results and actual leak extents. Therefore, the proposed workflow effectively integrates multi-frequency components, demonstrating its capability to delineate leak extents.","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":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848796","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

FreqAF: A New Frequency Attention Fusion Spectral Estimation Method for Radar Super-Resolution Imaging FreqAF：一种新的雷达超分辨率成像频率注意力融合谱估计方法

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-27 DOI: 10.1109/LGRS.2025.3555259

Yvyang Gao;Ganggang Dong

{"title":"FreqAF: A New Frequency Attention Fusion Spectral Estimation Method for Radar Super-Resolution Imaging","authors":"Yvyang Gao;Ganggang Dong","doi":"10.1109/LGRS.2025.3555259","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3555259","url":null,"abstract":"Frequency estimation was a fundamental problem in radar imaging. The classical Fourier spectral analysis suffered from the Rayleigh limit. The imaging performance deteriorated rapidly in low SNR conditions. In addition, the prior knowledge on the number of signal sources was required. To solve the problems, a new data-driven spectral estimation method via frequency attention fusion (FreqAF) was proposed in this letter. Different from the preceding works, the signal spectral were estimated by a deep architecture neural network automatically. The echo signal was first dechirped according to the radar parameters. It is then fed into a deep architecture for spectral estimation. The proposed architecture was composed of three phases, the decomposition, the FreqAF, and the projection. In the decomposition phase, the individual single-frequency components were estimated from the input dechirped signal. The components were dynamically fused in a delicate FreqAF module. The frequencies were obtained finally in the projection phase. Numerical experiments are performed to verify the proposed method.","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":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786355","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

Fed-RSSC: A Semi-Decentralized Federated Framework for Remote Sensing Scene Classification Fed-RSSC：用于遥感场景分类的半分散式联盟框架

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-27 DOI: 10.1109/LGRS.2025.3555251

Jing Jin;Feng Wang

{"title":"Fed-RSSC: A Semi-Decentralized Federated Framework for Remote Sensing Scene Classification","authors":"Jing Jin;Feng Wang","doi":"10.1109/LGRS.2025.3555251","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3555251","url":null,"abstract":"High-resolution remote sensing (HRRS) scene classification is critical in various applications. HRRS data usually contain sensitive geographical and environmental information, such as the locations of critical assets, urban planning details, and military bases. To safeguard such private information, local governments have implemented regulations and policies that govern the sharing of HRRS data. However, existing scene classification methods typically rely on centralized training and assume that data are directly shared with a centralized server, posing significant privacy risks. To address these concerns, we propose federated remote sensing scene classification (Fed-RSSC), a novel framework enabling the collaborative training of a joint model while ensuring data remains localized. We further demonstrate that federated learning (FL) is an effective approach to tackling privacy issues in HRRS scene classification. Moreover, to reduce high communication overhead, Fed-RSSC, a semi-decentralized architecture, is designed with a local consensus aggregation (LCA) strategy based on device-to-device (D2D) communication. Consequently, Fed-RSSC significantly reduces reliance on direct communication between the server and clients, thereby enhancing both communication efficiency and scalability. Extensive experiments on the NWPU-RESISC45, AID, and UC-Merced datasets validate the effectiveness and scalability of Fed-RSSC, demonstrating its superiority in scene classification.","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":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143824653","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