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

Abstract

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 TV (ETV) with an endmember background dictionary (EBD) 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 EBD (ETVEBD) method elaborates on 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 EBD has more physical meanings. The proposed method is evaluated on four hyperspectral datasets, and the experiment results show that its performance is the best compared with the other seven state-of-the-art methods.