A Robust Change Detector for Highly Heterogeneous Multivariate Images.

Ammar Mian, Jean Philippe Ovarlez, Guillaume Ginolhac, Abdourrahmane M. Atto

Abstract: In this paper, we propose new detectors for Change Detection between two multivariate images. The data is supposed to fol-Iowa Compound Gaussian distribution. By using Likelihood Ratio Test (LRT) and Generalised LRT (GLRT) approaches, we derive our detectors. The CFAR behaviour has been studied and the simulations show that they outperform the classic Gaussian Detector when the data is highly heterogeneous.