Estimating Tree Structural Parameters via Automatic Tree Segmentation From LiDAR Point Cloud Data
Estimating Tree Structural Parameters via Automatic Tree Segmentation From LiDAR Point Cloud Data
复制标题
通过 LiDAR 点云数据的自动树分割估计树结构参数
DOI：
10.1109/jstars.2021.3135491
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发表时间：
2022
影响因子：
5.5
通讯作者：
Hosoi Fumiki
中科院分区：
文献类型：
--
作者：
Itakura Kenta;Miyatani Satoshi;Hosoi Fumiki
在本文中，我们提出了一种使用光检测和测距（LiDAR）点云数据的自动树木分割方法。即使在崎岖不平的地面上，树木分割也能准确执行，并在 1000 多个样本上得到验证。例如，数据集 2 中检测到了 374 棵树中的 371 棵，误差是由远离激光雷达区域的点密度低的树引起的。准确地进行了分割，包括分支，从而检索了叶面积等高级参数。为了从分割的树木中获得有关叶面积的参数，提出了一种利用地面激光雷达方法获得的三维点云中的叶子和分支点进行分类的方法。在对输入点云进行预处理（例如通过体素化）后，计算快速点特征直方图（FPFH）特征。然后，使用训练数据集训练用于分类叶和枝的分类器，以使用测试数据计算测试精度。此外，还执行了使用 FPFH 特征和 k 均值算法的无监督分类方法。因此，使用监督方法，分类的召回率和精度值分别为 98.14% 和 96.03%。
In this article, we proposed an automated tree segmentation method using light detection and ranging (LiDAR) point cloud data. Tree segmentation was performed accurately even with bumpy ground, and was validated on more than 1000 samples. For example, 371 out of 374 trees were detected from dataset 2, and the error was caused by the trees with low point densities located in the area far from the LiDAR. Segmentation was accurately performed, including the branches, leading to the retrieval of high-level parameters such as the leaf areas. To obtain the parameters regarding the leaf area from the segmented trees, a method for classifying the leaf and branch points in the three-dimensional point clouds obtained using a terrestrial LiDAR method was proposed. After preprocessing the input point cloud, such as by voxelization, the fast point feature histogram (FPFH) features were calculated. Then, the classifier for classification into leaves and branches was trained using the training dataset to calculate the test accuracy with the test data. Moreover, an unsupervised method for classification using the FPFH feature andk-means algorithm was also performed. Consequently, the recall and precision values of the classification were determined as 98.14% and 96.03%, respectively, with the supervised approach.