Anomaly detection algorithm for hyperspectral images based on background endmember extraction and kernel RX algorithm

Abstract

The kernel RX algorithm improves the separability between target and background pixels by mapping hyperspectral image data from the low dimensional space into high dimensional feature space. However, the kernel matrix of the background is generated by all image pixels without considering the interference of anomaly target pixels which will make the miss rate increase and consume large memory. To resolve the problem, an anomaly detection algorithm based on background endmember extraction and kernel RX algorithm is introduced. Firstly, the RX algorithm is applied for image processing to filter out obvious anomaly pixels. Then endmember extraction algorithm is used to extract the background endmember according to which the kernel matrix is generated. Experimental results show the effectiveness of the algorithm in improving the detection performance.