Application of neural networks algorithms to QuickBird imagery for classification and change detection of urban areas

Several studies have reported the potentialities of high resolution multi-spectral imagery for classifying and monitoring urban areas [1], [2], [3]. In this paper we present the results obtained by processing high resolution multi-spectral QuickBird images of an urban area. The high resolution QuickBird data have been used for two different purposes: for an automatic image classification using neural network techniques and for a change detection analysis. In the first case, we have carried out a pixel-based classification procedure aimed at the discrimination among 4 main classes: buildings, roads, vegetated areas, bare soil; then we have examined the potentialities of Kohonen maps for discovering new subclasses within those already established: e.g. for the asphalt category, different subclasses such as highways pixels and the other different types of roads such as secondary street pixels have been identified. In the second case we have processed multitemporal QuickBird images for detecting major changes occurred over the selected test area, like news buildings not visible in the first image.