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Satellite image classification is an important technique used in remote sensing for the computerized analysis and pattern recognition of satellite data, which facilitates the automated interpretation of a large amount of information. Today, there exist many types of classification algorithms, such as parallelepiped and minimum distance classifiers, but it is still necessary to improve their performance in terms of accuracy rate. On the other hand, over the last few decades, cellular automata have been used in remote sensing to implement processes related to simulations. Although there is little previous research of cellular automata related to satellite image classification, they offer many advantages that can improve the results of classical classification algorithms. This paper discusses the development of a new classification algorithm based on cellular automata which not only improves the classification accuracy rate in satellite images by using contextual techniques but also offers a hierarchical classification of pixels divided into levels of membership degree to each class and includes a spatial edge detection method of classes in the satellite image. [ABSTRACT FROM PUBLISHER]