TreeCurveNet - An improved CurveNet for Tree Species Classification

Nowadays, remote sensing is widely used for large-scale forest surveys. The use of LiDAR (Light Detection and Ranging, LiDAR) made it possible to obtain detailed 3D point clouds of scanned areas, significantly increasing the efficiency of identification. The breakthrough in 3D object classification has opened new opportunities for the practical application of deep learning methods to identify forest tree species, which is a key task for forest management. In this paper, we propose a CurveNet-based model more suitable for tree species classification - TreeCurveNet. TreeCurveNet uses a deterministic algorithm to generate sampling curves. The results of tree classification experiments show that the TreeCurveNet model has the highest accuracy result of 86.5% compared to PointNet, PointNet + +, and CurveNet models.