SEM Algorithm and Unsupervised Statistical Segmentation of Satellite Images

This work addresses Bayesian unsupervised satellite image segmentation. We propose, as an alternative to global methods like MAP or MPM, the use of contextual ones, which is partially justified by previous works. We show, via a simulation study, that spatial or spectral context contribution is sensitive to image parameters such as homogeneity, means, variances, and spatial or spectral correlations of the noise. From this one may choose the best context contribution according to the estimated values of the above parameters. The parameter estimation step is treated by the SEM, a densities mixture estimator which is a stochastic variant of the EM algorithm. Another simulation study shows good robustness of the SEM algorithm with respect to different image parameters. Thus modification of the behavior of the contextual methods, when the SEM-based unsupervised approaches are considered, remains limited and the conclusions of the supervised simulation study stay valid. We propose an “adaptive unsupervised method” using more relevant contextual features. Furthermore, we apply different SEM-based unsupervised contextual segmentation methods to two real SPOT images and observe that the results obtained are consistently better than those obtained by a classical histogram based method.