SAR images as mixtures of Gaussian mixtures

We consider the problem of image segmentation by clustering local histograms with parametric mixture-of-mixture models. These models represent each cluster by a single mixture model of simple parametric components, typically truncated Gaussians. Clustering requires unsupervised inference of the model parameters, for which we derive a nested variant of the EM algorithm. This learning procedure is designed to deal with the large number of hidden variables required by the model. Results are presented for application of the algorithm to unsupervised segmentation of synthetic aperture radar (SAR) images.