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

F.C. Nex*, M. Dalla Mura

*Corresponding author for this work

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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.
Original languageEnglish
Title of host publicationProceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands
EditorsN. Kerle, M. Gerke, S. Lefevre
Place of PublicationEnschede
PublisherUniversity of Twente, Faculty of Geo-Information Science and Earth Observation (ITC)
Number of pages4
ISBN (Print)978-90-365-4201-2
Publication statusPublished - 14 Sept 2016
Event6th International Conference on Geographic Object-Based Image Analysis, GEOBIA 2016: Solutions & Synergies - University of Twente Faculty of Geo-Information and Earth Observation (ITC), Enschede, Netherlands
Duration: 14 Sept 201616 Sept 2016
Conference number: 6


Conference6th International Conference on Geographic Object-Based Image Analysis, GEOBIA 2016
Abbreviated titleGEOBIA
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