Finding Road Seeds in Aerial Images

Abstract

We describe RoadF, a road finding system designed to drive road tracking programs either for automatic extraction of road networks or for testing and performance evaluation. RoadF utilizes clues in the form of antiparallel edge pairs, and, by using a special algorithm to analyze noisy road center sequences, spans gaps in these clues to construct road hypotheses even when edge continuity is imperfect. As a result long road hypotheses are generated, and the longer a road hypothesis is, the more confidence there is in its being correct. In linking hypothesized road centers, RoadF ignores geometric constraints, leading to obvious mistakes that are later corrected by a smoothness checking phase of the road finding program. Further improvement of results is achieved by linking road hypotheses that are consistent in a geometric sense. The main contribution of this research is the treatment of imperfect continuity. Whereas previous research has centered mainly on improved detection of local road properties, successful detection of imperfect continuity makes utilization of length more effective in pruning away false detections. The technique we presented can be used in conjunction with any of the local road property tests employed in previous work, and it can be expected that it would improve the capability to distinguish between real roads and false alarms.