Unsupervised statistical segmentation of multispectral SAR images using generalized mixture estimation

This work deals with the estimation of generalized mixtures with applications to unsupervised statistical multisensor image segmentation. A mixture is said to be “generalized” when the exact nature of the noise components is not known; one assumes, however, that each belongs to a finite known set of families of distributions. The authors propose some methods of estimation of such mixtures based on expectation-maximization (EM), and iterative conditional estimation (ICE) algorithms. The set of families of distributions is assumed to lie in Pearson´s system