A Background Refinement Collaborative Representation Method with Saliency Weight for Hyperspectral Anomaly Detection

Abstract

Collaborative Representation Detection (CRD) is a very effective anomaly detection method, which is directly based on the concept that pixel under test (PUT) can be approximately linear represented by its spatial adjacent background pixels. If the adjacent background pixels are contaminated, the approximate value of PUT linearly represented by the surrounding pixels is inaccurate. In this work, an improved method for anomaly detection in hyperspectral imagery is proposed based on CRD. In our proposed method, the least squares technique first is adopted to obtain the preliminary linear representation coefficient, which is positively correlated with its contribution to PUT. Then, the purified background pixels are obtained according to the numerical value of the representation coefficient. Generally, the anomaly pixels are usually different from the background pixels, so saliency weight is imposed on the test pixel to make full use of the spatial information of inner window pixels around the test pixel. Extensive experiments for real hyperspectral datasets show that the proposed method outperforms the CRD method and other traditional detection methods.