Automatic and Robust Aerial Road Detection Based on Multihypothesis Generalized Kalman Filter Using Fully and Partially Occluded Models.

A heretofore unsolved challenge is the completely automatic and accurate estimation of road boundaries in aerial images when the roads may be partially or completely locally occluded and clutter may be prevalent. In this paper we introduce a roadfinder that is effective in meeting this challenge. The roadfinder begins with one or more seeds on each long road, and then accurately estimates the remaining boundaries, which can be found completely automatically by the algorithm described in IEEE Pami96 by Barzohar Cooper. The algorithm is robust to missing boundary edges on one side of the road and on both sides of the road simultaneously. These arise from shadows and occlusion by trees, poles, small structures, etc. It is also robust to clutter within the road caused by cars or trucks, and to clutter resulting from intersecting or close parallel roads. The algorithm is based on simple clutter and occlusion models and a combined Multihypothesis Generalized Kalman Filter (MGKF).