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Hyperspectral Anomaly Detection via Sparse Dictionary Learning Method of Capped Norm
Yuan, Yuan1,2; Ma, Dandan3,4; Wang, Qi1,2
作者部门光学影像学习与分析中心
2019
发表期刊IEEE ACCESS
ISSN2169-3536;
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
DOI10.1109/ACCESS.2019.2894590
收录类别SCI
语种英语
WOS记录号WOS:000459445500001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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.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
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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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