Unsupervised segmentation of multispectral images using hierarchical MRF model

This paper proposes an Markov random field (MRF) model-based method for unsupervised segmentation of multispectral images, in which the intra-class correlation of multispectral data as well as the class correlation are taken into account. In this method a set of multispectral images is modeled by a hierarchical MRF model. The proposed segmentation method is an iterative method composed of parameter estimation and segmentation which is based on the framework of the expectation-maximization (EM) method. Making use of an approximation for the Baum function in the expectation step, parameter estimation is reduced to the conventional maximum likelihood (ML) estimation given the current estimate of the hidden class label. The estimation of the class label, which corresponds to image segmentation, is carried out by a deterministic relaxation method proposed by us.