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Here we compare the Boltzmann-Gibbs-Shannon (standard) with the Tsallisentropy on the pattern recognition and segmentation of coloured images obtainedby satellites, via "Google Earth". By segmentation we mean split an image tolocate regions of interest. Here, we discriminate and define an image partitionclasses according to a training basis. This training basis consists of threepattern classes: aquatic, urban and vegetation regions. Our numericalexperiments demonstrate that the Tsallis entropy, used as a feature vectorcomposed of distinct entropic indexes $q$ outperforms the standard entropy.There are several applications of our proposed methodology, once satelliteimages can be used to monitor migration form rural to urban regions,agricultural activities, oil spreading on the ocean etc.

Satellite image classification and segmentation using non-additive entropy