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Road network extraction from aerial images has received attention in photogrammetry and computer vision for decades. We present a concept for road network reconstruction from aerial images using knowledge-based image analysis. In contrast to other approaches, the proposed approach uses multiple cues about the object existence, employs existing knowledge, rules and models, and treats each road subclass differently to increase success rate and reliability of the results. Finding 3D edges on the road and especially the road borders is a crucial component of our procedure and is the focus of this paper. We developed an algorithm for automatically matching line segments across images. The algorithm exploits line geometrical and photometrical attributes and line geometrical structures. A framework to integrate these information sources using probability relaxation is developed and implemented to deliver locally consistent matches. Results of straight line matching are presented and future work is discussed.

Knowledge-based image analysis for 3D edge extraction and road reconstruction