Anomaly detection using morphology-based collaborative representation in hyperspectral imagery

Abstract

A nonparametric anomaly detection method is proposed in this paper which does not consider any probability density function for data, and so can work well for real hyperspectral image containing complicated and non-normal background. Since the predominant part of an image is composed by background pixels and because of similarity of neighboring pixels in a local region, each background pixel can be approximated from its surrounding samples. To this end, the collaborative representation with a simple and closed form solution is used. To more benefit from the spatial information of hyperspectral image, the morphological filters are applied for extraction of contextual features and a decision fusion rule is used to utilize the spatial information of all principal components of data. The experimental results show the superior performance of the proposed detector compared to several state-of-the-art anomalous target detection methods. The area under ROC curve (AUC) values achieved by the proposed detector are 95.53, 96.34 and 99.72 for San Diego hyperspectral image, Indian hyperspectral image and the WorldView-2 multispectral image, respectively.