Knowledge-Based Image Analysis for 3D Road Reconstruction
1m and providing height information with 1-2m accuracy. We currently use 1:16,000 scale color imagery, with 30cm focal length, and 60% forward overlap, scanned with 14 microns at a Zeiss SCAI. The other input data include: A nationwide DTM (DHM25) with 25m grid spacing and accuracy of 2-3/5-7 m in lowland/Alps, the vectorised map data (VEC25) of 1:25,000 scale, and the raster map with its 6 different layers. The VEC25 data have a RMS error of ca. 5-7.5m and a maximum one of ca. 12.5m, including generalization effect. They are topologically correct, but due to their partly automated extraction from map, some errors exist. In addition, DSM data in the working area was generated from stereo images using MATCH-T of Inpho with 2m grid spacing.
2. Extraction Strategy & Implementation
Our approach makes full use of available information about the scene and contains a set of image analysis tools (see Fig. 1). The management of different information and the selection of image analysis tools are controlled by a knowledge-based system. The initial knowledge base is established by the information extracted from the existing geographic data and road design rules. This information is formed in object-oriented multiple object layers, i.e. roads are divided into various subclasses according to road type, land cover and terrain relief. It provides a global description of road network topology, and the local geometry for a road segment. Therefore we avoid developing a general road model, instead a specific model can be derived for each road segment. This model provides the initial 2D location of a road in the scene, as well as road attributes, such as road class, presence of road marks, and geometry (width, length, horizontal and vertical curvature, land cover and so on). A road segment is processed with an appropriate method corresponding to its model, and the knowledge base is automatically updated and refined using information gained from previous extraction of roads. The processing proceeds from the easiest subclasses to the most difficult ones. Since neither 2D nor 3D procedures alone are sufficient to solve the problem of road extraction, we make the transition from 2D image space to 3D object space as early as possible, and extract the road network with the mutual interaction between features of these spaces. More details of the general strategy can be found in Zhang and Baltsavias (2000).
Figure. 1 Strategy of road network extraction in ATOMI.
L+T: Swiss Federal Office of Topography, Bern
