Sparse Representation Based Hyperspectral Anomaly Detection via Adaptive Background Sub-Dictionaries

Hyperspectral anomaly detection has drawn much attention in recent years. In this paper, in order to effectively extract anomalies in hyperspectral images, a novel sparse-representation based hyperspectral anomaly detection method via adaptive background sub-dictionaries is proposed. Firstly, a background estimation strategy is proposed to provide representative background information. Based on the estimated background, a global dictionary is constructed by utilizing K-means clustering algorithm. Next, Several active atoms are selected from the global dictionary to form a sub-dictionary to adaptively approximate the local region in each dual-window. This sub-dictionary construction strategy can remove potential anomaly contamination in local regions. Finally, a re-weighting strategy is proposed to enhance the performance of sparse-representation-based anomaly detector. Experimental results demonstrate that our method can effectively extract anomalies and suppress background simultaneously.