Accurate Building Structure Recovery from High Resolution Aerial Imagery

This paper focuses on the problem of automatic extraction and modeling of buildings from stereoscopic pairs of high resolution aerial images covering urban areas. An increasing number of applications require accurate and up-to-date cartographic and 3-D data. roduce a set of algorithms based on an accurate, reliable, and discontinuity-preserving digital elevation model (DEM) computation. This method involves a gradient correlation, contour-adaptive windows with a geodesic weighting, a multi- resolution coarse to fine scheme, and a two-way filtering symmetrical validation. The DEMs are then segmented and each region is classified either as ground, vegetation, or building. The regions of interest, i.e., the buildings, are then individually processed, and a two-step modeling process is applied. First, building boundaries are detected and linearized: segments are extracted from the images, height and gray-level information computed in their neighborhoods is added to their description, and segments are grouped taking into account some constraints on neighborhood compatibility. Second, we develop an original method for the estimation of roof surface planes based on a Bayesian approach. This method directly provides the number of planes and their equations from the 3-D dataset of the regions of interest. thod involves thresholds, most of them tuned with respect to the physical characteristics of the scene, and only two parameters have to be adjusted. Results are shown and discussed on four different stereo pairs.