Hyperspectral Anomaly Detection via Sparse Dictionary Learning Method of Capped Norm | |
Yuan, Yuan1,2; Ma, Dandan3,4; Wang, Qi1,2 | |
作者部门 | 光谱成像技术研究室 |
2019 | |
发表期刊 | IEEE ACCESS |
ISSN | 2169-3536; |
卷号 | 7页码:16132-16144 |
产权排序 | 3 |
摘要 | Hyperspectral anomaly detection is a research hot spot in the field of remote sensing. It can distinguish abnormal targets from the scene just by utilizing the spectral differences and requiring no prior information. A series of anomaly detectors based on Reed-Xiaoli methods are very important and typical algorithms in this research area, which generally have the hypothesis about background subject to the Gaussian distribution. However, this assumption is inaccurate to describe a hyperspectral image with a complex scene in practice. Besides, due to the unavoidable existence of abnormal targets, background statistics will be affected which will reduce the detection performance. To address these problems, we propose a sparse dictionary learning method by using a capped norm to realize hyperspectral anomaly detection. Moreover, a new training data selection strategy based on clustering technique is also proposed to learn a more representative background dictionary. The main contributions are concluded in threefold: 1) neither making any assumptions on the background distribution nor computing the covariance matrix, the proposed method is more adaptive to all kinds of complex hyperspectral images in practice; 2) owing to the good qualities of the capped norm, the learned sparse background dictionary is resistant to the effect of anomalies and has stronger distinctiveness to anomalies from background; 3) without using the traditional sliding hollow window technique, the proposed method is more effective to detect different sizes of abnormal targets. The extensive experiments on four commonly used real-world hyperspectral images demonstrate the effectiveness of the proposed method and show its superiority over the benchmark methods. |
关键词 | Anomaly detection hyperspectral images sparse dictionary learning capped norm |
DOI | 10.1109/ACCESS.2019.2894590 |
收录类别 | SCI ; EI |
语种 | 英语 |
WOS记录号 | WOS:000459445500001 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
EI入藏号 | 20190806530011 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/31162 |
专题 | 光谱成像技术研究室 |
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.Chinese Acad Sci, Ctr Opt Imagery Anal & Learning, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
推荐引用方式 GB/T 7714 | Yuan, Yuan,Ma, Dandan,Wang, Qi. Hyperspectral Anomaly Detection via Sparse Dictionary Learning Method of Capped Norm[J]. IEEE ACCESS,2019,7:16132-16144. |
APA | Yuan, Yuan,Ma, Dandan,&Wang, Qi.(2019).Hyperspectral Anomaly Detection via Sparse Dictionary Learning Method of Capped Norm.IEEE ACCESS,7,16132-16144. |
MLA | Yuan, Yuan,et al."Hyperspectral Anomaly Detection via Sparse Dictionary Learning Method of Capped Norm".IEEE ACCESS 7(2019):16132-16144. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Hyperspectral Anomal(7643KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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