  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.
