ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 169-176, 2016
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-3/169/2016/
doi:10.5194/isprs-annals-III-3-169-2016
 
03 Jun 2016
CLASSIFICATION OF AIRBORNE LASER SCANNING DATA USING GEOMETRIC MULTI-SCALE FEATURES AND DIFFERENT NEIGHBOURHOOD TYPES
R. Blomley, B. Jutzi, and M. Weinmann Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Englerstr. 7, 76131 Karlsruhe, Germany
Keywords: ALS, LiDAR, Point Cloud, Features, Multi-Scale, Classification Abstract. In this paper, we address the classification of airborne laser scanning data. We present a novel methodology relying on the use of complementary types of geometric features extracted from multiple local neighbourhoods of different scale and type. To demonstrate the performance of our methodology, we present results of a detailed evaluation on a standard benchmark dataset and we show that the consideration of multi-scale, multi-type neighbourhoods as the basis for feature extraction leads to improved classification results in comparison to single-scale neighbourhoods as well as in comparison to multi-scale neighbourhoods of the same type.
Conference paper (PDF, 1195 KB)


Citation: Blomley, R., Jutzi, B., and Weinmann, M.: CLASSIFICATION OF AIRBORNE LASER SCANNING DATA USING GEOMETRIC MULTI-SCALE FEATURES AND DIFFERENT NEIGHBOURHOOD TYPES, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 169-176, doi:10.5194/isprs-annals-III-3-169-2016, 2016.

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