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Multiview Subspace Tensor Self-Representation for SAR Image Semi-Supervised Classification

文献类型：期刊论文

作者Yang, Yang; Tang, Yongqiang; Bai, Jiangbo; Zhang, Lu; Zhang, Wensheng
刊名IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
出版日期2024
卷号21页码:5
关键词Image classification multiview learning semi-supervised learning subspace self-representation learning synthetic aperture radar (SAR)
ISSN号1545-598X
DOI10.1109/LGRS.2024.3415150
通讯作者Tang, Yongqiang(yongqiang.tang@ia.ac.cn)
英文摘要Synthetic aperture radar (SAR) image classification has proven its significant importance in automatic remote sensing. However, current methods demand a large volume of training data to ensure satisfactory generalization capability. Given the difficulty in obtaining sufficient labeled samples, semi-supervised learning for SAR image classification becomes extremely important. However, existing studies are hindered by sample relationship modeling and single sample descriptions, leading to limited performance. To address this issue, we propose an innovative approach: multiview subspace tensor self-representation learning with label propagation for SAR image classification. Initially, our method extracts global sample relationship matrices of SAR samples using subspace self-representation learning with affine and nonnegative constraints. To enhance the single sample feature description, we construct multiview tensor learning by stacking the subspace representation matrices of different SAR view features. Finally, the SAR image multiview subspace self-representation matrix is treated as the probability indication matrix for classification. The experiments on MSTAR using extreme 10% labeled samples have shown that the proposed method yields performance boosts of 22.3% and 12.2% to the state-of-the-art method under two rigorous evaluation settings, respectively. The remarkably superior experimental results effectively validate the effectiveness of our proposals.
资助项目Innovation 2030 Major S&T Projects of China[2021ZD0113600] ; National Natural Science Foundation of China[62173328] ; National Natural Science Foundation of China[62206293]
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001256485700010
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Innovation 2030 Major S&T Projects of China ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/59118]  
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者Tang, Yongqiang
作者单位Chinese Acad Sci, Inst Automat, State Key Lab Multimodel Artificial Intelligence S, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Yang, Yang,Tang, Yongqiang,Bai, Jiangbo,et al. Multiview Subspace Tensor Self-Representation for SAR Image Semi-Supervised Classification[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2024,21:5.
APA Yang, Yang,Tang, Yongqiang,Bai, Jiangbo,Zhang, Lu,&Zhang, Wensheng.(2024).Multiview Subspace Tensor Self-Representation for SAR Image Semi-Supervised Classification.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,21,5.
MLA Yang, Yang,et al."Multiview Subspace Tensor Self-Representation for SAR Image Semi-Supervised Classification".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 21(2024):5.

入库方式： OAI收割

来源：自动化研究所

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