Hyperspectral Anomaly Detection via Background Purification and Spatial Difference Enhancement

Hyperspectral anomaly detection is one of the most important applications in the field of hyperspectral image (HSI) processing. However, hyperspectral anomaly detectors still face several challenges, including the limited use of spatial information and the unavoidable anomaly pollution problem. To cope with the above problems, we propose a hyperspectral anomaly detector, termed COPCRD, which enhances the prevailing collaborative-representation-based detector (CRD) using COPula-based Outlier Detection (COPOD) for background purification and guided filter for spatial difference enhancement. COPCRD mainly solves the anomaly pollution problem and further considers the spatial information of hyperspectral data to enhance discrimination of backgrounds and anomalies. Experimental results on four hyperspectral datasets reveal that the proposed method is more accurate than four state-of-the-art anomaly detectors.