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Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification
Wang, Qi1,2,3; He, Xiang1,2; Li, Xuelong4
Department光学影像学习与分析中心
2019-02
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892;1558-0644
Volume57Issue:2Pages:911-923
Contribution Rank4
Abstract

Hyperspectral image (HSI) classification, which aims to assign an accurate label for hyperspectral pixels, has drawn great interest in recent years. Although low-rank representation (LRR) has been used to classify HSI, its ability to segment each class from the whole HSI data has not been exploited fully yet. LRR has a good capacity to capture the underlying low-dimensional subspaces embedded in original data. However, there are still two drawbacks for LRR. First, the LRR does not consider the local geometric structure within data, which makes the local correlation among neighboring data easily ignored. Second, the representation obtained by solving LRR is not discriminative enough to separate different data. In this paper, a novel locality- and structure-regularized LRR (LSLRR) model is proposed for HSI classification. To overcome the above-mentioned limitations, we present locality constraint criterion and structure preserving strategy to improve the classical LRR. Specifically, we introduce a new distance metric, which combines both spatial and spectral features, to explore the local similarity of pixels. Thus, the global and local structures of HSI data can be exploited sufficiently. In addition, we propose a structural constraint to make the representation have a near-block-diagonal structure. This helps to determine the final classification labels directly. Extensive experiments have been conducted on three popular HSI data sets. And the experimental results demonstrate that the proposed LSLRR outperforms other state-of-the-art methods.

KeywordBlock-diagonal structure hyperspectral image (HSI) classification low-rank representation (LRR)
DOI10.1109/TGRS.2018.2862899
Indexed BySCI
Language英语
WOS IDWOS:000456936500022
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/31165
Collection光学影像学习与分析中心
Corresponding AuthorWang, Qi
Affiliation1.Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China
2.Northwestern Polytech Univ, Ctr Opt Imagery Anal & Learning, Xian 710072, Shaanxi, Peoples R China
3.Northwestern Polytech Univ, Unmanned Syst Res Inst, Xian 710072, Shaanxi, Peoples R China
4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China
Recommended Citation
GB/T 7714
Wang, Qi,He, Xiang,Li, Xuelong. Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2019,57(2):911-923.
APA Wang, Qi,He, Xiang,&Li, Xuelong.(2019).Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,57(2),911-923.
MLA Wang, Qi,et al."Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 57.2(2019):911-923.
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