Automatic finding of main roads in aerial images by using geometric-stochastic models and estimation

Abstract

An automated approach to finding main roads in aerial images is presented. The approach is to build geometric-probabilistic models for road image generation. Gibbs distributions are used. Then, given an image, roads are found by MAP (maximum aposteriori probability) estimation. The MAP estimation is handled by partitioning an image into windows, realizing the estimation in each window through the use of dynamic programming, and then, starting with the windows containing high confidence estimates, using dynamic programming again to obtain optimal global estimates of the roads present. The approach is model-based from the outset. It produces two boundaries for each road, or four boundaries when a midroad barrier is present. >