ISPRS-Annals ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Annals ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9050 Copernicus Publications Göttingen, Germany 10.5194/isprs-annals-V-1-2020-95-2020 FRACTAL DIMENSION BASED SUPERVISED LEARNING FOR WOOD AND LEAF CLASSIFICATION FROM TERRESTRIAL LIDAR POINT CLOUDS Hui Z. 1 Xia Y. 1 Nie Y. 1 Chang Y. 1 Hu H. 1 Li N. 1 He Y. 1 Faculty of Geomatics, East China University of Technology, Nanchang, China 03 08 2020 V-1-2020 95 99 Copyright: © 2020 Z. Hui et al. 2020 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-annals.copernicus.org/articles/V-1-2020/95/2020/isprs-annals-V-1-2020-95-2020.html The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/V-1-2020/95/2020/isprs-annals-V-1-2020-95-2020.pdf

Terrestrial Laser scanner has been widely used in the field of forestry. Wood-leaf separation is the fundamental step to most applications of forestry. This paper presented a robust supervised learning method for wood and leaf classification by developing four new feature vectors. Fractal dimension is first calculated to indicate the difference of regularity or roughness between wood and leaf. Zenith angle and variation are presented to distinguish trunks or branches from leaves. The adaptive axis direction of cylinder is adopted to calculate the local point density precisely. Experimental results show that the supervised learning method using the four feature vectors presented in this paper can achieve a good classification performance. Both accuracy and <i>F</i>1 score are higher than the ones of the method using eigen value based feature vectors.