Image segmentation is one of the most challenging steps in image processing. Its results are used by many other tasks regarding information extraction from images. In remote sensing, segmentation generates regions according to found targets in a satellite image, like roofs, streets, trees, vegetation, agricultural crops, or deforested areas. Such regions differentiate land uses by classification algorithms. In this paper we investigate a way to perform segmentation using a strategy to classify and merge spectrally and spatially similar pixels. For this purpose we use a geographical extension of the Self-Organizing Maps (SOM) algorithm, which exploits the spatial correlation among near pixels. The neurons in the SOM will cluster the objects found in the image, and such objects will define the image segments.
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