Hyperspectral anomaly detection using differential image

Abstract

A hyperspectral anomaly detector, which uses the benefits of a proposed background feature extraction method and the kernel trick, is introduced in this study. The proposed method is differential image based detector (DID). The DID method uses the differential image to estimate the variations of background in the feature extraction phase. The proposed feature extraction method slows the variations of background. So, it suppresses the background signal and highlights the anomalous signal. The separation between anomalous targets and background clutter is increased in the projected feature space. Before feeding the transformed data into the Reed-Xiaoli (RX) detector, the kernel learning method is applied. The kernel technique transforms the projected data with a linear non-Gaussian model to a potentially high-dimensional feature space with the non-linear Gaussian domain. Experiments are conducted on two real hyperspectral images. The experimental results indicate that the proposed DID method outperforms RX and some state-of-the-art anomaly detection approaches.