A new Riemannian geometry for the Compound Gaussian distribution is proposed. In particular, the Fisher information metric is obtained, along with corresponding geodesics and distance function. This new geometry is applied on a change detection problem on Multivariate Image Times Series: a recursive approach based on Riemannian optimization is developed. As shown on simulated data, it allows to reach optimal performance while being computationally more efficient.
@article{arxiv.2005.10087,
title = {Riemannian geometry for Compound Gaussian distributions: application to recursive change detection},
author = {Florent Bouchard and Ammar Mian and Jialun Zhou and Salem Said and Guillaume Ginolhac and Yannick Berthoumieu},
journal= {arXiv preprint arXiv:2005.10087},
year = {2020}
}