Automatic extraction of urban regions from multispectral SPOT satellite imagery

This paper presents a system for the automatic extraction of urban regions from multispectral satellite images using a pseudo-supervised method to classify unlabeled images. This is accomplished through a preclassification process which automatically extracts representative training data from images without the need for ground truth data or user intervention. In the preclassification stage, a technique is developed to find a set of prototypes from regions exhibiting urban-like characteristics, then a subspace is constructed by applying principle component analysis over the set of urban prototypes. Image points are then preclassified based upon their distance to the urban subspace. A set of representative training data is extracted from the preclassified urban and non-urban regions, which is used to train a Gaussian mixture model based Bayesian classifier. The final extraction of urban-like regions is determined by the trained Bayesian classifier using a maximum a posteriori (MAP) criterion. Our experimental results show that the proposed technique is effective in automatically extracting urban regions from SPOT multispectral satellite images.