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

This paper presents an automated approach to finding main roads in aerial images. The approach is to build geometric-probabilistic models for road image generation. We use Gibbs distributions. Then, given an image, roads are found by MAP (maximum a posteriori 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 and is completely different than those appearing in the published literature. It produces two boundaries for each road, or four boundaries when a mid-road barrier is present.