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Spectral-spatial hyperspectral image classification via locality and structure constrained low-rank representation
He, Xiang1; Wang, Qi1,2; Li, Xuelong3,4
2018-10-31
Conference Name38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Source Publication2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
Volume2018-July
Pages5744-5747
Conference Date2018-07-22
Conference PlaceValencia, Spain
PublisherInstitute of Electrical and Electronics Engineers Inc.
Contribution Rank3
AbstractLow-rank representation (LRR) has been applied widely in most fields due to its considerable ability to explore the low-dimensional subspace embedding in high-dimensional data. However, there are still some problems that LRR can't effectively exploit the local structure and the representation for the given data is not discriminative enough. To tackle the above issues, we propose a novel locality and structure constrained low-rank representation (LSLRR) for hyperspectral image (HSI) classification. First, a distance metrics, which combines spectral and spatial similarity, is proposed to constrain the local structure. This makes two pixels in HSI with small distance have high similarity. Second, we exploit the classwise block-diagonal structure for the training data to learn the more discriminative representation for the testing data. And the experimental results verify the effectiveness and superiority of LSLRR comparing with other state-of-the-art methods. © 2018 IEEE
Department光学影像学习与分析中心
DOI10.1109/IGARSS.2018.8517342
Indexed ByEI
ISBN9781538671504
Language英语
EI Accession Number20191206669247
Citation statistics
Document Type会议论文
Identifierhttp://ir.opt.ac.cn/handle/181661/31389
Collection光学影像学习与分析中心
Affiliation1.School of Computer Science, Center for OPTical IMagery Analysis and Learning, Northwestern Polytechnical University, Xi'an, Shaanxi; 710072, China;
2.Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an, Shaanxi; 710072, China;
3.Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi; 710119, China;
4.University of Chinese Academy of Sciences, Beijing; 100049, China
Recommended Citation
GB/T 7714
He, Xiang,Wang, Qi,Li, Xuelong. Spectral-spatial hyperspectral image classification via locality and structure constrained low-rank representation[C]:Institute of Electrical and Electronics Engineers Inc.,2018:5744-5747.
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