Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification | |
Wang, Qi1,2,3; He, Xiang1,2; Li, Xuelong4 | |
刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
2019-02 | |
卷号 | 57期号:2页码:911-923 |
关键词 | Block-diagonal structure hyperspectral image (HSI) classification low-rank representation (LRR) |
ISSN号 | 0196-2892;1558-0644 |
DOI | 10.1109/TGRS.2018.2862899 |
产权排序 | 4 |
英文摘要 | 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. |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000456936500022 |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/31165] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
通讯作者 | Wang, Qi |
作者单位 | 1.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 |
推荐引用方式 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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