Knowledge-Based Image Analysis for 3D Road Reconstruction
Key Words
Road reconstruction, Context, Knowledge base, Spatial reasoning
Abstract
The extraction of road networks from aerial images is one of the current challenges in digital photogrammetry and computer vision. In this paper, we present our developed system for 3D road network reconstruction from aerial images using knowledge-based image analysis. In contrast to other approaches, the developed system integrates knowledge processing of color image data and information from digital geographic databases, extracts and fuses multiple object cues, thus takes into account context information, employs existing knowledge, rules and models, and treats each road subclass accordingly. The key of the system is the use of knowledge as much as possible to increase success rate and reliability of the results, working in 2D images and 3D object space, and use of 2D and 3D interaction when needed. Another advantage of the developed system is that it can correctly and reliably handle problematic areas caused by shadows and occlusions.
1. Introduction
The extraction of roads from digital images has drawn considerable attention lately. The existing approaches cover a wide variety of strategies, using different resolution aerial or satellite images. Overviews can be found in Gruen et al. (1995, 1997a) and Foerstner and Pluemer (1997). A semi-automatic scheme requires human interaction to provide interactively some information to control the extraction. Roads are then extracted by profile matching (Airault et al., 1996, Vosselman and de Gunst, 1997), cooperative algorithms (McKeown et al., 1988), and dynamic programming or LSB-Snakes (Gruen and Li, 1997b). The automatic methods usually extract reliable hypotheses for road segments through line and edge detection and then establish connections between road segments to form road networks (Wang and Trinder, 2000). Contextual information is taken into account to guide the extraction of roads (Ruskone, 1996). Roads can be detected in multi resolution images (Baumgartner and Hinz, 2000). The existing approaches show individually that the use of road models and varying strategies for different types of scenes are promising. However, all the methods are based on relatively simplistic road models, and most of them do not make use of a prior information, thus they are very sensitive to disturbances like cars, shadows or occlusions, and do not always provide good quality results. Furthermore, most approaches work in single 2D images, thus neglecting valuable information inherent in 3D processing.
In this paper, we present a knowledge-based system for automatic extraction of 3D roads from stereo aerial images which integrates knowledge processing of colour image data and existing digital spatial databases. The system combines different input data that provides complementary, but also redundant information about road existence, therefore it can account for problematic areas caused by occlusions and shadows, and the success rate and the reliability of the extraction results are increased. The system has been developed in the project ATOMI (for details of ATOMI, see Eidenbenz et al., 2000) to improve road centerlines from digitized 1:25,000 topographic maps by fitting them to the real landscape, improving the planimetric accuracy to
