ICASSP 2018accepted0 citations
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.
BibTeX
@inproceedings{icassp2018_arobustchangedet,
title = {A Robust Change Detector for Highly Heterogeneous Multivariate Images},
author = {Ammar Mian and Jean Philippe Ovarlez and Guillaume Ginolhac and Abdourrahmane M. Atto},
booktitle = {ICASSP 2018},
year = {2018}
}