Model-Based Road Extraction from Images

In this paper we present an approach to the automatic extraction of roads from aerial images. We argue that a model for road extraction is needed in every step of the image interpretation process. The model needs to include knowledge about different aspects of roads, like geometry, radiometry, topology, and context. The main part of this paper discusses the parts of that knowledge that we have implemented so far. It is shown that roads can be successfully detected at various resolution levels of the same image. Furthermore, we show that combining the results obtained in each level helps to eliminate false hypotheses typical for each level. The approach has been successfully applied to a variety of images.