Segmentation and object-based classification for the extraction of the building class from LIDAR DEMs

Authors:
George Miliaresis;Nikolaos Kokkas
Affiliations:
Department of Geology, University of Patras, Rion 26504, Greece;Department of Geomatic Engineering, University College London, Gower Street, London WC1E 6BT, UK
Venue:
Computers & Geosciences
Year:
2007

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Abstract

A new method is presented for the extraction of a class for buildings from light detection and ranging (LIDAR) digital elevation models (DEMs) on the basis of geomorphometric segmentation principles. First, seed cells and region growing criteria are specified. Then an object partition framework is defined on the basis of region growing segmentation. Size filtering is applied to objects and connected components labelling identifies background and foreground objects that are parametrically represented on the basis of elevation and slope. K-means classification reveals a set of clusters. The interpretation of the spatial distribution of clusters assisted by the interpretation of cluster centroids, allows for the identification of the building class, as well as building sub-classes with different geomorphometric characteristics.