To improve the accuracy of wood and leaf points classification for individual tree, this paper proposed a separation method based on mode points evolution from terrestrial LiDAR point clouds. In the proposed method, the Mean Shift method was used to first acquire the mode points, which were then adopted as nodes to build a network graph for the individual tree. By path retracing and calculating the visiting frequency of each node, the wood seed nodes were detected. To obtain more wood nodes, the wood seed nodes were evolved based on three constraints, namely the shortest path length of the evolved nodes to the base node should be smaller, the evolved nodes should not belong to the leaf nodes that have been detected by path retracing and the verticality of the evolved nodes should be similar as the wood seed nodes. After wood nodes evolution, the segments corresponding to each wood seed node were merged together to obtain the final wood points. The proposed method has been evaluated using nine tree samples with seven different tree species. Experimental results showed that the proposed method can achieve an average wood and leaf classification accuracy of 0.892. The average F1 score for wood was 0.871, while the average F1 score for leaf was 0.900. Compared to two other famous wood and leaf classification methods, the proposed method can achieve better classification results.

为提高单棵树木叶点分类精度，提出一种基于模式点演化的陆地激光雷达点云分离方法。在所提出的方法中，首先使用Mean Shift方法获取模式点，然后将其作为节点来构建单个树的网络图。通过路径回溯和计算每个节点的访问频率，检测到木材种子节点。为了获得更多的木材节点，木材种子节点基于三个约束进行进化，即进化节点到基节点的最短路径长度应该更小，进化节点不应该属于路径检测到的叶节点进化节点的回溯和垂直度应该与木材种子节点相似。木节进化后，将每个木材种子节点对应的段合并在一起以获得最终的木材点。已使用具有七种不同树种的九棵树样本对所提出的方法进行了评估。实验结果表明，所提出的方法可以达到0.892的平均木叶分类精度。木材的平均 F1 分数为 0.871，而叶子的平均 F1 分数为 0.900。与其他两种著名的木材和树叶分类方法相比，所提出的方法可以取得更好的分类结果。892. 木材的平均 F1 分数为 0.871，而叶子的平均 F1 分数为 0.900。与其他两种著名的木材和树叶分类方法相比，所提出的方法可以取得更好的分类结果。892. 木材的平均 F1 分数为 0.871，而叶子的平均 F1 分数为 0.900。与其他两种著名的木材和树叶分类方法相比，所提出的方法可以取得更好的分类结果。