Copula-set measures on topographic maps for change detection

This paper addresses the problem of change detection in multispectral satellite images. We introduce a new information theoretic-based metric between the images associated with a Markov Random Field spatial regularization. The proposed metric is parametrized through copulas and implemented over component trees representation of the images. Such topographic map based metric associated to an Ising model exhibits interesting results for both abrupt and slow changes while being robust to global illumination and contrast changes. Experiments are conducted on SPOT images of the amazonian basin for landcover monitoring.