期刊
REMOTE SENSING
卷 11, 期 13, 页码 -出版社
MDPI
DOI: 10.3390/rs11131578
关键词
anomaly detection; hyperspectral; low-rank representation; local window; spatial constraint
类别
资金
- Natural Science Foundation of China [41871337, 41471356]
- Shan'xi Key Research and Development Program [2018ZDXM-GY-023]
Hyperspectral imagery contains abundant spectral information. Each band contains some specific characteristics closely related to target objects. Therefore, using these characteristics, hyperspectral imagery can be used for anomaly detection. Recently, with the development of compressed sensing, low-rank-representation-based methods have been applied to hyperspectral anomaly detection. In this study, novel low-rank representation methods were developed for anomaly detection from hyperspectral images based on the assumption that hyperspectral pixels can be effectively decomposed into a low-rank component (for background) and a sparse component (for anomalies). In order to improve detection performance, we imposed a spatial constraint on the low-rank representation coefficients, and single or multiple local window strategies was applied to smooth the coefficients. Experiments on both simulated and real hyperspectral datasets demonstrated that the proposed approaches can effectively improve hyperspectral anomaly detection performance.
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