Low-Rank and Collaborative Representation for Hyperspectral Anomaly Detection

Abstract

Recently, low-rank representation and collaborative representation for hyperspectral anomaly detection are widely studied. In this paper, a novel anomaly detector which combines low-rank and collaborative representations for hyperspectral anomaly detection (LRCRD) is proposed. Different from existing anomaly detection methods using low-rank and collaborative representation, the proposed method divides an image into two parts: background and anomaly targets. A background dictionary is used to represent the background whose coefficient matrix is constrained by low-rank and l 2 norm minimization. The sparsely distributed anomalies are determined by the residual matrix which is constrained by l 2,1 norm minimization. Considering different similarities between a testing pixel and a dictionary atom, a distance-weighted matrix is adopted. Moreover, construction of the background dictionary avoids the pollution of abnormal pixels and makes the detection result more stable. Experimental results show that the LRCRD performs better than state-of-the-art anomaly detection methods.