ObjectiveMost researches on TLS point cloud classification always calculate high dimensional features. But the higher the dimensional features calculated, the more the calculating consumption and running memory needed. So to solve the problem, we designed five geometric features of nearby points to train existing classifier. And then it was used to label the forest point clouds into ground, stem and leaf.

MethodThe 140 neighbors gotten by fast KDtree were used to compute the five features, including eigen values of covariance matrix, normal vector, curvature, variance and maximum distance of elevation. And the classifier could be trained with all of them. In order to check the stability of the five features in forest point cloud classification, both random forest and xgboost were introduced. The data in this research were all obtained from Mongolian oak plantation by TLS.

ResultIn test sets, the ratios between correct prediction samples and total samples were 0.932 1 and 0.936 3 with random forest and xgboost. Both classifier precision in ground, stem and leaf reached 0.97, 0.93 and 0.91 or more. And compared with random forest, the xgboost’s performance in the three categories had millesimal advantage.

ConclusionOn the basis of ensuring the accuracy of point cloud classification, the five features are not only with less dimensions but also helpful to enhance the efficiency of feature computation. It shows they can deal with point cloud classification problem in forest well and have high stability. And the classification result in this research will be helpful for forest parameter extraction and biomass estimation.