Spectral-spatial hyperspectral image classification via locality and structure constrained low-rank representation | |
He, Xiang1; Wang, Qi1,2; Li, Xuelong3,4 | |
2018-10-31 | |
会议名称 | 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 |
会议录名称 | 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings |
卷号 | 2018-July |
页码 | 5744-5747 |
会议日期 | 2018-07-22 |
会议地点 | Valencia, Spain |
出版者 | Institute of Electrical and Electronics Engineers Inc. |
产权排序 | 3 |
摘要 | Low-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 |
作者部门 | 光谱成像技术研究室 |
DOI | 10.1109/IGARSS.2018.8517342 |
收录类别 | EI |
ISBN号 | 9781538671504 |
语种 | 英语 |
EI入藏号 | 20191206669247 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/31389 |
专题 | 光谱成像技术研究室 |
作者单位 | 1.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 |
推荐引用方式 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. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Spectral-spatial hyp(411KB) | 会议论文 | 限制开放 | CC BY-NC-SA | 请求全文 |
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