Handheld mobile laser scanning (HMLS) can quickly acquire point cloud data, and has the potential to conduct forest inventory at the plot scale. Considering the problems associated with HMLS data such as large discreteness and difficulty in classification, different classification models were compared in order to realize efficient separation of stem, branch and leaf points from HMLS data. First, the HMLS point cloud was normalized and ground points were removed, then the neighboring points were identified according to three KNN algorithms and eight geometric features were constructed. On this basis, the random forest classifier was used to calculate feature importance and perform dataset training. Finally, the classification accuracy of different KNN algorithms-based models was evaluated. Results showed that the training sample classification accuracy based on the adaptive radius KNN algorithm was the highest (0.9659) among the three KNN algorithms, but its feature calculation time was also longer; The validation accuracy of two test sets was 0.9596 and 0.9201, respectively, which is acceptable, and the misclassification mainly occurred in the branch junction of the canopy. Therefore, the optimal classification model can effectively achieve the classification of stem, branch and leaf points from HMLS point cloud under the premise of comprehensive training.

中文翻译：

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基于不同KNN算法和随机森林算法的手持式激光扫描树点云分类

手持式移动激光扫描（HMLS）可以快速获取点云数据，并具有在样地范围内进行森林清查的潜力。考虑到与HMLS数据相关的问题，如离散度大和分类困难，比较了不同的分类模型，以实现HMLS数据中茎，枝和叶点的有效分离。首先，对HMLS点云进行归一化，去除地面点，然后根据三种KNN算法识别出相邻点，并构建了八个几何特征。在此基础上，使用随机森林分类器来计算特征重要性并执行数据集训练。最后，评估了不同的基于KNN算法的模型的分类精度。结果表明，在三种KNN算法中，基于自适应半径KNN算法的训练样本分类精度最高（0.9659），但其特征计算时间也较长。两个测试集的验证准确度分别为0.9596和0.9201，这是可以接受的，并且错误分类主要发生在树冠的分支结处。因此，最优分类模型可以在综合训练的前提下，有效地从HMLS点云中实现茎，枝，叶点的分类。分类错误主要发生在冠层的分支结处。因此，最优分类模型可以在综合训练的前提下，有效地从HMLS点云中实现茎，枝，叶点的分类。分类错误主要发生在冠层的分支结处。因此，最优分类模型可以在综合训练的前提下，有效地从HMLS点云中实现茎，枝，叶点的分类。