Hyperspectral Anomaly Detection Method Based on Adaptive Background Extraction

Abstract

Anomaly detection based on clustering is a classic method that supplies a simplified manner to describe a cluttered background. However, traditional clustering methods need to know the number of clusters in advance and attempt to classify all the background pixels at one time. In addition, compared with large background clusters, small clusters are hard to discriminate due to their small populations. In this paper, an anomaly detection method based on adaptive background extraction is proposed. We apply an unsupervised clustering method to determine the cluster centers according to only the similarity of the spectral signature. To reduce the influence of the population, we propose to extract background clusters iteratively. Every iteration, we only cluster the larger clusters and extract them from the data-set. In the next iteration, the remaining pixels are clustered again. Without interference from the larger clusters, the centers of smaller clusters will appear obviously. The clustering process stop when the number of remaining pixels nears the appearance probability of anomaly (generally approximately 10%∼20%). Then, only anomalies and few background pixels remain to test. Finally, every extracted background cluster, as a viewer, is applied to measure the anomaly salience of the test pixels. In addition, a weighted summation is proposed to fuse the different salience values from different viewers. Simulation experiments on two sets of real data are presented to demonstrate the superiority of the proposed method.