GEOMETRIC REFINEMENT OF LiDAR ROOF CONTOURS USING PHOTOGRAMMETRIC DATA AND MARKOV RANDOM FIELD MODEL

In this paper, a methodology is proposed for the geometric refinement of LiDAR building roof contours using high-resolution aerial images and Markov Random Field (MRF) models. The proposed methodology assumes that the 3D description of each building roof reconstructed from the LiDAR data (i.e., a polyhedron) is topologically correct and that it is only necessary to improve its accuracy. Since roof ridges are accurately extracted from LiDAR data, the main objective is to use high-resolution aerial images to improve the accuracy of roof outlines. In order to meet this goal, the available roof polyhedrons are first projected onto the image-space. Then, the projected polygons and the straight lines extracted from the image are used to establish an MRF description, which is based on relations (relative length, proximity, and orientation) between the two sets of straight lines. The energy function associated with the MRF is minimized using a minimizing algorithm, resulting in the grouping of straight lines for each roof object. Finally, each grouping of straight lines is topologically reconstructed based on the topology of the corresponding LiDAR polygon projected onto the image-space. The preliminary results showed that the proposed methodology is promising, since most sides of the refined polygons are geometrically better then corresponding projected LiDAR straight lines.