Enhanced Total Variation Regularized Representation Model with Endmember Background Dictionary for Hyperspectral Anomaly Detection

In recent years, several representation models based on total variation (TV) have been proposed for hyperspectral imagery (HSI) anomaly detection. However, the TV terms of these works are directly imposed on the representation coefficient matrix, which can destroy the spatial structure of an HSI to some extent. Besides, as the spatial resolution of an HSI is relatively low, mixed pixels existing in an HSI can lead to anomaly component contamination, which can make the difference between background and anomalies not significant enough. To address these issues, a novel enhanced total variation (ETV) with endmember background dictionary for hyperspectral anomaly detection is proposed. The ETV is designed to be used on the row vectors of the representation coefficient matrix to enhance the spatial structure of an HSI in the presentation process. Furthermore, the proposed ETV regularized representation model with endmember background dictionary (ETVEBD) method elaborates a background dictionary constructed by endmembers of background pixels, which are pure spectral signatures of background pixels. The proposed EBD can decrease the influence of anomaly components in mixed pixels, and the coefficient matrix of the endmember background dictionary has more physical meanings. The proposed method is evaluated on four hyperspectral data sets and the experiment results show its performance is best when it is compared with other seven state-of-the-art methods.