Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 461–465, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-461-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 461–465, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-461-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Oct 2019

18 Oct 2019

COMPARISON OF POINT AND SEGMENT BASED POINT CLOUD CLASSIFICATION METHOD IN URBAN SCENES

E. Hasanpour1, M. Saadatseresht1, and E. G. Parmehr2 E. Hasanpour et al.
  • 1School of Surveying and Geospatial Engineering, University of Tehran, Iran
  • 2Dept. of Geomatics, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Iran

Keywords: Classification, Point Cloud, Comparison of Features, Random Forest

Abstract. Point cloud classification is an essential requirement for effectively utilizing point cloud data acquired by different kind of sources such as Terrestrial Laser Scanning (TLS), Aerial LiDAR (Light Detection and Ranging), and Photogrammetry. Classification of point cloud is a process that points are separated into different point groups that each group has similar features. Point cloud classification can be done in three levels (point-based, segment-based, and object-based) and the choice of different level has significant impact on classification result. In this research, random forest classification method is utilized in which the point-wise and segment-wise spectral and geometric features are selected as the input of the classification. In our experiments, the results of point- and segment-based classification were compared. In addition, point-wise classification result for two different features (geometric with/without spectral features) has been compared and the results are presented. The experiments illustrated that segment based classification with both color and geometric features has the best overall accuracy of 83% especially near the object boundaries.