Go Decomposition-Based Model with Independent Component Analysis for Hyperspectral Anomaly Detection

The low-rank and sparse decomposition has found widespread application in hyperspectral image (HSI). To further improve the anomaly detection effect, an accurate and appropriate decomposition strategy is necessary. In this paper, we develop a go decomposition-based model with independent component analysis (ICA) for hyperspectral anomaly detection, referred as ICGD. This model utilizes data sphering to remove the first two order statistics, such that better represent HSI. Then, inspired by go decomposition thought, the pure low-rank background component and sparse anomaly component are generated. Through combining the obtained low-rank item and sparse item as the input of RX, anomaly detection can be realized. And experimental result on real hyperspectral data reveals the effectiveness of ICGD.