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It is interesting to be able to analyze town development, to make streets maps automatically or just, to mask the urban areas in satellite images. The objective of this study is to extract and classify urban areas from remote sensing images. The method combines different types of operators. At first, we define automatically a mask of the urban areas by combining a classification algorithm with edge extraction algorithms. Then these urban areas are segmented according to the streets, railways and rivers to obtain urban districts. Finally, we define a measure of urban density. In this paper, we focus on the urban extraction algorithm and the urban segmentation process.
Lots of methods have been defined to extract and classify urban areas: for example the combination of mathematical morphology and Bayesian classification algorithms [INRIA, 1990] and the combination of Fourier transformation and neural network classification algorithm [K. Weigl, 1993].
The method proposed by BESSETTES and DESACHY [V. Bessettes, 1997] [V. Bessettes, 1996] works on multispectral SPOT-XS and P+XS images. To extract and classify urban areas, they combine different types of operators in order to improve the final classification.
