Hyperspectral anomaly detection using low-rank representation and learned dictionary

This paper proposes an anomaly detection method based on low-rank representation and learned dictionary for hyperspectral imagery. The model of low-rank representation, which fits the linear mixing model of hyperspectral imagery more precisely compared with other low-rank decomposition algorithms such as robust principle component analysis (RPCA), was introduced to settle the anomaly detection problem for hyperspectral imagery. To improve its robustness to initialized parameters, a learned dictionary that represents only background information was adopted in the proposed method. Experiments on synthetic and real hyperspectral datasets illustrated that the proposed method is capable of improving detection results. Meanwhile, it is robust to initialized parameters and can be viewed as an effective technique to detect anomalies in hyperspectral imagery.