Difference-value background based on the subset of the category in hyperspectral anomaly detection

Abstract

Hyperspectral anomaly detection (HAD) is an important unsupervised target detection technique. Background modelling techniques occupy an important component in HAD. The background modelling process is easily disturbed by the anomalous target, resulting in a large error between the estimated value and the real value. To solve this problem, this paper proposes a new background difference value method. Firstly, the hyperspectral datasets are divided into several subsets by endmembers, and then the subsets with fewer samples are removed from the original sample set to a new background set. Finally, the background model parameters are estimated by the new background set. Experimental results show that the proposed algorithm has better detection performance in complex scenes.