Unsupervised multicomponent image segmentation combining a vectorial HMC model and ICA

This work extents the Hidden Markov Chain (HMC) model for the unsupervised segmentation of multicomponent images. Although the vectorial extension of the model is almost straightforward, we are faced to the problem of estimating a mixture of non-Gaussian multidimensional densities. In this work, we adopt an Independent Component Analysis (ICA) approach that allows the mutual dependance between the layers to be taken into account in the segmentation process. Classification results on a four bands SPOT-IV image illustrates the method. Also, a comparison is performed when only mutual independence or correlation between the components is assumed.