Hyperspectral Anomaly Detection Based on Total Variation and Structured Dictionary

This paper presents a novel method for hyperspectral anomaly detection based on total variation and structured dictionary. Generally, a hyperspectral imagery can be modeled as a superposition of two components: background and anomalies. Since each pixel in the background can be well represented by some of the other background pixels and the anomalies to be detected can be approximately represented by some potential anomalous pixels. Therefore, each test pixel can be represented using a structured dictionary consisting of background and potential anomalous pixels. Moreover, considering the spatial homogeneity of natural background and the sparse nature of the anomalies, two regularization terms named total variation and sparisty are imposed in the formulation. The experimental results on simulated and real hyperspectral data sets validated the effectiveness of our proposed method compared to several conventional and state-of-the-art anomaly detection methods.