False Discovery Rate Approach to Unsupervised Image Change Detection

 False Discovery Rate Approach to Unsupervised Image Change Detection

 We address the problem of unsupervised change detection on two or more coregistered images of the same object or scene at several time instants. We propose a novel empirical-Bayesian approach that is based on a false discovery rate formulation for statistical inference on local patchbased samples. This alternative error metric allows to efficiently adjust the family-wise error rate in case of the considered largescale testing problem. The designed change detector operates in an unsupervised manner under the assumption of the limited amount of changes in the analyzed imagery.