Anomaly Detection of Hyperspectral Images Based on Linear Unmixing and Low-Rank Representation

CHEN-GUANG PAN, TING-FA XU, JIAN-HUA HAO, A-XIN FAN, CHEN HUANG

Abstract


Anomaly detection is an important branch in hyperspectral detection. There are some challenging problems mainly due to the highly mixture nature and noise corruption. In this paper, we propose a hyperspectral anomaly detection method based on Linear Unmixing and Low-Rank Representation (LU-LRR) with construction of a more robust dictionary. Firstly, the Kernel-PCA (KPCA) is used to estimate the numbers of endmembers and mine high-order correlation among spectral bands of original hyperspectral data. Secondly, data are decomposed into a product form of endmember matrix and abundance matrix based on linear spectral unmixing method. Abundance matrix usually possesses more distinctive feature to distinguish anomaly from background compared with original data. Finally, LRR is exploited to decompose abundance matrix into low-rank component and sparse component indicating background and anomalies separately, and furtherly introduce a more robust subspace basis dictionary based on dictionary learning into low-rank component. Experiments in real hyperspectral images have demonstrated that LU-LRR is more effective than traditional anomaly detection methods

Keywords


Hyperspectral anomaly detection, Kernel-PCA, Linear spectral unmixing, Low-rank representation, Dictionary learning.Text


DOI
10.12783/dteees/peems2019/33981

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