Segmentation of Individual Trees From TLS and MLS Data
Segmentation of Individual Trees From TLS and MLS Data
复制标题
根据 TLS 和 MLS 数据分割单个树
DOI：
10.1109/jstars.2016.2565519
复制
发表时间：
2017
影响因子：
5.5
通讯作者：
Li Manchun
中科院分区：
文献类型：
--
作者：
Zhong Lishan;Cheng Liang;Xu Hao;Wu Yang;Chen Yanming;Li Manchun
利用地面激光扫描（TLS）和移动的激光扫描（MLS）数据可以获得丰富而精确的树木侧面信息。因此，它可以实现提取单木参数，如树高，冠幅，冠底高，胸径，它可以为森林研究和管理提供基础数据。这项研究提出了一个技术框架分割个别树木从TLS和MLS数据。该框架包括六个步骤：1）数据预处理，2）八叉树构建，3）空间聚类，4）树干检测，5）初始分割，6）重叠冠层分割。该框架主要有两个贡献：1）自上而下的层次分割方法，包括基于连通性的空间聚类（区域尺度），基于词干的初始分割（单木尺度），以及重叠树冠的精细分割（树冠尺度），提出降低技术难度，提高工艺效率; 2）提出了一种改进的归一化割方法节点相似度计算方法，该方法针对重叠树冠分割问题，即使相邻树木的树冠重叠，也能有效地将相邻树木分离出来。提出的框架进行了测试的lead-off地面激光雷达数据集和lead-on移动的激光雷达数据集。对于地面LiDAR数据，我们的框架实现了92.4%的完整性，95.4%的正确性，和F-分数为0.94。对于移动的LiDAR数据，相应的值分别为94.0%、93.7%和0.94。
Terrestrial laser scanning (TLS) and mobile laser scanning (MLS) data can be used to obtain abundant and precise side information of trees. Therefore, it can enable extracting individual tree parameters, such as the tree height, crown size, crown base height, and diameter at breast height, and it can provide basic data for forest research and management. This study proposes a technical framework for segmenting individual trees from TLS and MLS data. This framework contains six steps: 1) data preprocessing, 2) octree construction, 3) spatial clustering, 4) stem detection, 5) initial segmentation, and 6) overlapped canopy segmentation. This framework makes two main contributions: 1) a top-down hierarchical segmentation approach, including connectivity-based spatial clustering (regional scale), stem-based initial segmentation (individual tree scale), and fine segmentation of overlapped canopy (canopy scale), is proposed to reduce technical difficulties and improve process efficiency; and 2) a modified node similarity calculation for normalized cut method aiming at segmenting overlapped canopy, which can effectively separate neighboring trees even if their canopies are overlapped, is proposed. The proposed framework was tested on a leaves-off terrestrial LiDAR dataset and a leaves-on mobile LiDAR dataset. For terrestrial LiDAR data, our framework achieved completeness of 92.4%, correctness of 95.4%, and F-score of 0.94. For mobile LiDAR data, the corresponding values were 94.0%, 93.7%, and 0.94.