ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-4, 257-262, 2012
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-4/257/2012/
doi:10.5194/isprsannals-I-4-257-2012
© Author(s) 2012. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
20 Jul 2012
ENTITIES AND FEATURES FOR CLASSIFCATION OF AIRBORNE LASER SCANNING DATA IN URBAN AREA
S. Xu, S. Oude Elberink, and G. Vosselman Faculty of Geo-Information Science and Earth Observation, University of Twente, Netherlands
Keywords: Classification, Feature, Segmentation, Point Cloud Abstract. We aim at efficiently classifying ALS data in urban areas by choosing an optimal combination of features and entities. Three kinds of entities are defined, namely, single points, planar segments and segments obtained by mean-shift segmentation. Various features are computed for these three entities. All derived features are assigned to different steps of our method. Our method is composed of a sequence of rule based classifications. After a rule based classification for planar segments and a context rule based classification for walls and roof elements 85% of the data are well classified. Errors mainly appear in the area where rules are difficult to define, such as vegetation close to walls and above roofs. To eliminate these errors, we first group all the points in these areas into segments using mean shift, and then search for segments with potentially misclassified points using a distance ratio. These mean shift segments are then re-classified using another rule based classification. The overall quality of our classification method reaches to 98.1%.
Conference paper (PDF, 4992 KB)


Citation: Xu, S., Oude Elberink, S., and Vosselman, G.: ENTITIES AND FEATURES FOR CLASSIFCATION OF AIRBORNE LASER SCANNING DATA IN URBAN AREA, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-4, 257-262, doi:10.5194/isprsannals-I-4-257-2012, 2012.

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