Automatic Generation of High Quality 3D Urban Buildings from Aerial Images
Automatic Generation of High Quality 3D Urban Buildings from Aerial Images
(Version 11/5/07)
Ahmed F. Elaksher and James S. Bethel
ABSTRACT: High quality 3D building databases are essential inputs for urban area Geographic Information Systems. Since manual generation of these databases is very costly and time consuming, the development of automated algorithms is of great need. This article presents a new algorithm to automatically extract accurate and reliable 3D building information. High overlapping aerial images are used as input to the algorithm. Radiometric and geometric properties of buildings are utilized to distinguish building roofs in the images. This is accomplished using image segmentation and neural network techniques. A rule-based system is used to extract the vertices of the roof polygons in all images. The 3D coordinates of these vertices are computed using photogrammetric mathematical models. The algorithm is tested on a number of buildings in a complex urban scene. Results showed a detection rate of 99% and a false alarm rate of 5.0%. The root mean square error for the extracted building vertices is 0.25 meter using 1:4000 scale aerial photographs scanned at 30 micron.
