ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume IV-3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 217–223, 2018
https://doi.org/10.5194/isprs-annals-IV-3-217-2018
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 217–223, 2018
https://doi.org/10.5194/isprs-annals-IV-3-217-2018

  23 Apr 2018

23 Apr 2018

COMPARISON OF SINGLE AND MULTI-SCALE METHOD FOR LEAF AND WOOD POINTS CLASSIFICATION FROM TERRESTRIAL LASER SCANNING DATA

Hongqiang Wei, Guiyun Zhou, and Junjie Zhou Hongqiang Wei et al.
  • School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China

Keywords: Scale, Leaf and wood classification, Terrestrial Laser Scanning, Tree point cloud, Machine Learning

Abstract. The classification of leaf and wood points is an essential preprocessing step for extracting inventory measurements and canopy characterization of trees from the terrestrial laser scanning (TLS) data. The geometry-based approach is one of the widely used classification method. In the geometry-based method, it is common practice to extract salient features at one single scale before the features are used for classification. It remains unclear how different scale(s) used affect the classification accuracy and efficiency. To assess the scale effect on the classification accuracy and efficiency, we extracted the single-scale and multi-scale salient features from the point clouds of two oak trees of different sizes and conducted the classification on leaf and wood. Our experimental results show that the balanced accuracy of the multi-scale method is higher than the average balanced accuracy of the single-scale method by about 10 % for both trees. The average speed-up ratio of single scale classifiers over multi-scale classifier for each tree is higher than 30.