Classification and Representation of Networks from Satellite Images

Classification is one of the major issue in image analysis and processing for remote sensing applications. Though classification based on texture analysis —landuse, forests, cities, etc— is the purpose of numerous works, classification of curvilinear networks is hardly processed. However, it is of major interest, in particular for image indexing and images matching, because it is a main feature whose global shape does not change with sensors nor point of view. This paper introduces a new approach aiming at (i) building the networks from extracted curvilinear-like features and (ii) classifying them into roads, highways, rivers. The main idea is to use a decision tree taking into account a priori knowledge. Classification and graph building are achieved simultanously using a hypothesis generation/propagation scheme. The resulting network is encoded as a graph with a multi-scale description. Illustrations given on satellite optical SPOT images show encouraging results.