  Wood-leaf classification is an essential and fundamental prerequisite in the
analysis and estimation of forest attributes from terrestrial laser scanning
(TLS) point clouds,including critical measurements such as diameter at breast
height(DBH),above-ground biomass(AGB),wood volume.To address this,we introduce
the Wood-Leaf Classification Network(WLC-Net),a deep learning model derived
from PointNet++,designed to differentiate between wood and leaf points within
tree point clouds.WLC-Net enhances classification accuracy,completeness,and
speed by incorporating linearity as an inherent feature,refining the
input-output framework,and optimizing the centroid sampling technique.WLC-Net
was trained and assessed using three distinct tree species datasets,comprising
a total of 102 individual tree point clouds:21 Chinese ash trees,21 willow
trees,and 60 tropical trees.For comparative evaluation,five alternative
methods,including PointNet++,DGCNN,Krishna Moorthy's method,LeWoS, and Sun's
method,were also applied to these datasets.The classification accuracy of all
six methods was quantified using three metrics:overall accuracy(OA),mean
Intersection over Union(mIoU),and F1-score.Across all three datasets,WLC-Net
demonstrated superior performance, achieving OA scores of 0.9778, 0.9712, and
0.9508;mIoU scores of 0.9761, 0.9693,and 0.9141;and F1-scores of 0.8628,
0.7938,and 0.9019,respectively.The time costs of WLC-Net were also recorded to
evaluate the efficiency.The average processing time was 102.74s per million
points for WLC-Net.In terms of visual inspect,accuracy evaluation and
efficiency evaluation,the results suggest that WLC-Net presents a promising
approach for wood-leaf classification,distinguished by its high accuracy. In
addition,WLC-Net also exhibits strong applicability across various tree point
clouds and holds promise for further optimization.
