Hyperspectral Anomaly Detection Method Based on Low Rank Total Variation Regu-larization

Abstract

Hyperspectral remote sensing technology provides abundant spectral information for exploring objects and supplies a better data source for anomaly detection. However, anomaly detection is still a challenging task without any valuable prior information. Aiming at this problem, a hyperspectral anomaly detection method based on low-rank and TV regularization constraint is proposed in this paper. Firstly, the hyperspectral image is linearly and nonlinearly unmixed to generate two abundance maps, and these two maps are fused with the original hyperspectral image. Secondly, the spectral dictionary of background targets in hyperspectral image is constructed according to their features in the fused data, and a low-rank representation model of the image is generated. Thirdly, an anomaly detection regularization model is established according to the characteristics of normal and abnormal targets. Finally, the model is optimized to generate the anomaly detection result. Experiments are carried out in the real hyperspetral datasets, and the detection results demonstrate that the proposed method is able to achieve a promising anomaly detection performance.

Keywords

• hyperspectral data
• anomaly detection
• low-rank representation
• regularization