Scene classification of urban areas exploiting multi-view high resolution aerial images

Many supervised and unsupervised algorithms for the automated and reliable classification of large regions using high resolution data have been presented in the remote sensing community in the last decades. Most of these approaches exploit a single input data: high resolution orthophotos or 3D point clouds. An increasing number of contributions has more recently exploited the combined use of orthophotos and LiDAR DSM taking advantage from the complementarity of these inputs. Nevertheless, very few applications have considered the use of overlapping multi-view images on the same area for classification. In this paper the first tests to investigate this classification architecture are presented. Different typologies of DSM and orthophoto as well as a variable number of images on the same area have been considered in the experiments. The preliminary results on the two available test areas will be shown, in order to draw the first conclusions on this approach and discuss the further developments of this research.