Tree Classification in Complex Forest Point Clouds Based on Deep Learning

Xinhuai Zou, Ming Cheng, Cheng Wang, Yan Xia, Jonathan Li

Abstract: Recently, the classification of tree species using 3-D point clouds has drawn wide attention in surveys and forestry investigations. This letter proposes a new voxel-based deep learning method to classify tree species in 3-D point clouds collected from complex forest scenes. The proposed method includes three steps: 1) individual tree extraction based on the density of the point clouds; 2) low-level feature representation through voxel-based rasterization; and 3) classification of tree species by a deep learning model. Two data sets of 3-D forest point clouds acquired by terrestrial laser scanning systems are used to evaluate the proposed method. The method achieves an average classification accuracy of 93.1% and 95.6% on the two data sets. Furthermore, in comparative experiments, the proposed method exhibits performance superior to that of the other 3-D tree species classification methods.