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The problem of supervised classification of the satellite image is consideredto be the task of grouping pixels into a number of homogeneous regions in spaceintensity. This paper proposes a novel approach that combines a radial basicfunction clustering network with a growing neural gas include utility factorclassifier to yield improved solutions, obtained with previous networks. Thedouble objective technique is first used to the development of a method toperform the satellite images classification, and finally, the implementation toaddress the issue of the number of nodes in the hidden layer of the classicRadial Basis functions network. Results demonstrating the effectiveness of theproposed technique are provided for numeric remote sensing imagery. Moreover,the remotely sensed image of Oran city in Algeria has been classified using theproposed technique to establish its utility.

A new bio-inspired method for remote sensing imagery classification