THE SEGMENTATION OF POINT CLOUDS WITH K-MEANS AND ANN (ARTIFICAL NEURAL NETWORK)

Kuçak R. A., Özdemir E., Erol S.

ISPRS Hannover Workshop / High-Resolution Earth Imaging for Geospatial Information (HRIGI) Workshop / City Models, Roads and Traffic (CMRT) Workshop / Image Sequence Analysis (ISA) Workshop / European Calibration and Orientation (EuroCOW) Workshop, Hannover, Germany, 6 - 09 June 2017, pp.595-598 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.5194/isprs-archives-xlii-1-w1-595-2017
  • City: Hannover
  • Country: Germany
  • Page Numbers: pp.595-598
  • Istanbul Technical University Affiliated: Yes

Abstract

Segmentation of point clouds is recently used in many Geomatics Engineering applications such as the building extraction in urban areas, Digital Terrain Model (DTM) generation and the road or urban furniture extraction. Segmentation is a process of dividing point clouds according to their special characteristic layers. The present paper discusses K-means and self-organizing map (SOM) which is a type of ANN (Artificial Neural Network) segmentation algorithm which treats the segmentation of point cloud. The point clouds which generate with photogrammetric method and Terrestrial Lidar System (TLS) were segmented according to surface normal, intensity and curvature. Thus, the results were evaluated.