Building footprint extraction based on radiometric and geometric constraints in airborne oblique images

Automatic building detection plays an important role in many applications. Multiple overlapped airborne images as well as Lidar point clouds are among the most popular data sources used for this purpose. Multi-view overlapped oblique images bear both height and colour information, and additionally we explicitly have access to the vertical extent of objects; therefore, we explore the usability of this data source solely to detect and outline buildings in this paper. First, a building roof region is initialised by using a scoring scheme in order to taking the common regions of multiple views. Second, we introduce an approach to integrate the weak evidences extracted from airborne oblique images, including multi-view stereo matched points, 3D lines from line matching and detected façades. Those weak evidences are integrated under a Markov Random Field framework as constraints for roof region refinement. The building footprints are lastly extracted from the refined roof regions of the building. The proposed method is tested with different building types.