Multi-view fusion of road objects supported by self-diagnosis

Abstract

In this paper, we present work on automatic road extraction from high-resolution aerial imagery taken over urban areas. In order to deal with the high complexity of this type of scenes, we integrate detailed knowledge about roads and their context using explicitly formulated scale-dependent models. The knowledge about how and when certain parts of the road and context model are optimally exploited is condensed in the extraction strategy. Special focus is on the extension of our previous road extraction system to full multi-image capability. To exploit information from multiple views, a fusion strategy for road objects (e.g. lanes) has been developed. It is based on internally computed quality measures and embedded in the system's concept of self-diagnostic extraction algorithms. The analysis of the final results show benefits but also remaining deficiencies of this approach.