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The unsupervised classification has a very important role in the analysis ofmultispectral images, given its ability to assist the extraction of a prioriknowledge of images. Algorithms like k-means and fuzzy c-means has long beenused in this task. Computational Intelligence has proven to be an importantfield to assist in building classifiers optimized according to the quality ofthe grouping of classes and the evaluation of the quality of vectorquantization. Several studies have shown that Philosophy, especially theDialectical Method, has served as an important inspiration for the constructionof new computational methods. This paper presents an evaluation of four methodsbased on the Dialectics: the Objective Dialectical Classifier and theDialectical Optimization Method adapted to build a version of k-means withoptimal quality indices; each of them is presented in two versions: a canonicalversion and another version obtained by applying the Principle of MaximumEntropy. These methods were compared to k-means, fuzzy c-means and Kohonen'sself-organizing maps. The results showed that the methods based on Dialecticsare robust to noise, and quantization can achieve results as good as thoseobtained with the Kohonen map, considered an optimal quantizer.

Avalia\c{c}\~ao do m\'etodo dial\'etico na quantiza\c{c}\~ao de imagens multiespectrais