Hyperspectral Anomaly Detection Using Collaborative Representation with PCA Remove Outlier

Abstract

Anomaly detection is one of the most important applications of hyperspectral imagery. In this paper, we propose a hyperspectral image anomaly detection method using collaborative representation detector with PCA remove outlier (PCAroCRD). The algorithm has two versions: global and local. In the collaborative representation detection (CRD) algorithm, if the test pixel is an anomalous pixel and several samples from background are also anomalous and similar to test pixel, then the judge error is likely to happen. In the proposed algorithm, PCA is adopted to extract main pixels information and remove abnormal pixels from background so as to obtain higher detection accuracy before estimating the samples. Experimental results indicate that the proposed algorithm outperforms the original collaborative representation detector (CRD), kernel version of CRD (KCRD), advanced CRD (CRDBORAD), and the traditional detection methods such as Global Reed-Xiaoli (RX) algorithm and the Local RX.