Using Artificial Recurrent Neural Nets to Identify Spectral and Spatial Patterns for Satellite Imagery Classification of Urban Areas

Abstract

The majority of techniques used for satellite imagery classification usually perform poorly on discriminating urban land use classes, either because they have similar spectral signatures or because the patterns they exhibit are broader than satellite image pixels. In this paper we tackle a new classification methodology, based on spectral and spatial pattern analysis using artificial neural networks. First a self-organising classifier splits the spectrum of individual pixels on spectrally pure land cover classes. Then a second classifier self-organises critical regions of adjustable topology on the resultant image, and automatically classifies it into land use classes. Both classifiers are implemented on artificial recurrent neural networks inspired by Carpenter and Grossberg's adaptive ressonance theory (ART).