  Terrestrial laser scanning technology provides an efficient and accuracy
solution for acquiring three-dimensional information of plants. The leaf-wood
classification of plant point cloud data is a fundamental step for some
forestry and biological research. An automatic sampling and training method for
classification was proposed based on tree point cloud data. The plane fitting
method was used for selecting leaf sample points and wood sample points
automatically, then two local features were calculated for training and
classification by using support vector machine (SVM) algorithm. The point cloud
data of ten trees were tested by using the proposed method and a manual
selection method. The average correct classification rate and kappa coefficient
are 0.9305 and 0.7904, respectively. The results show that the proposed method
had better efficiency and accuracy comparing to the manual selection method.
