Riemannian geometry for compound Gaussian distributions: Application to recursive change detection

Abstract

A new Riemannian geometry for the zero-mean Compound Gaussian distribution with deterministic textures is proposed. In particular, the Fisher information metric (up to a factor) 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.

Publication
In Elsevier Signal Processing
Ammar Mian
Ammar Mian
Associate professor

Associate professor at Université Savoie Mont Blanc in Signal processing

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