Multiband segmentation based on a hierarchical Markov model

Abstract

We develop a new multiscale Markov segmentation model for multiband images. Using quadtree multiple resolution analysis of a multiband image, we use both inter- and intra-scale spatial Markov statistical dependencies. Bayesian inference is used to assess the appropriate number of segments. We exemplify the excellent results which can be obtained with this approach using synthetic images, and in two case studies involving multiband astronomical image sets.