A Bayesian method for multispectral image data classification

The problem of classifying multispectral image data is studied here. We propose a new Bayesian method for this. The method uses "a priori" spatial information modeled by means of a suitable Markov random field. The image data for each class are assumed to be i.i.d. following a multivariate Gaussian model with unknown mean and unknown diagonal covariance matrix. When the prior information is not used and the variances of the Gaussian model are equal, the method reduces to the standard K-means algorithm. All the parameters appearing in the posterior model are estimated simultaneously. The prior normalizing constant is approximated on the basis of the expectation of the energy function as obtained by means of Markov Chain Monte Carlo simulations. Some experimental results suggest calculating this expectation from a "standard" function by simple multiplication by the minimum value of the energy. A local solution to the problem of maximizing the posterior distribution is obtained by using the Iterated Conditional Modes algorithm. The implementation of this method is easy and the required computations are carried out quickly, The method was applied with success to classify simulated image data and real dynamic Magnetic Resonance Imaging data.