Unsupervised classification and segmentation of satellite images are examined in this work. The classification is based on Bayes` criterion, which tries to minimize the expected value of the classification error. The algorthms developed were proposed postulating that the classes in the image are well modeled by gaussian random vectors. Conventional classifiers, which take into account only pixelwise information, were treated as vector quantizers. Specifically, it was proposed a classification algorithm based on entropy constrained vector. The behaviour of the classifiers is examined observing the discrepancy between classifications, comparing classified images with reference-images and classifyng sinthetic images. The percentage of pixels whitch are assigned to the same class as in the reference-images ranged from 80,0% to 95,0%. This good behaviour of the classidiers is limited by the fact that, in theirs structures, are taken into account only isolated pixel information. We have sought, by classifying segments, to introduce contextual information into the classifiers structure. The segments classidiers. A segmentation algorithm, which introduces contextual information into pixelwise classifier by a markovian approach, is presented.