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An Automatic Tree Skeleton Extracting Method Based on Point Cloud of Terrestrial Laser Scanner 时间:2017-11-06 21:48   来源:未知   作者:admin   点击: 次 Abstract:Tree skeleton could describe the shape and topological structure of a tree, which are useful to forest researchers. Terrestrial laser scanner (TLS) can scan trees with high accuracy and speed to acquire the point cloud data, which could be used to extract tree skeletons. An adaptive extracting method of tree skeleton based on the point cloud data of TLS was proposed in this paper. The point cloud data were segmented by artificial filtration and -means clustering, and the point cloud data of trunk and branches remained to extract skeleton. Then the skeleton nodes were calculated by using breadth first search (BFS) method, quantifying method, and clustering method. Based on their connectivity, the skeleton nodes were connected to generate the tree skeleton, which would be smoothed by using Laplace smoothing method. In this paper, the point cloud data of a toona tree and peach tree were used to test the proposed method and for comparing the proposed method with the shortest path method to illustrate the robustness and superiority of the method. The experimental results showed that the shape of tree skeleton extracted was consistent with the real tree, which showed the method proposed in the paper is effective and feasible.
1. Introduction
As we know, the laser has advantages of good monochromaticity and strong direction. With the rapid development of laser measurement technology, there have been some products such as airborne laser scanning, mobile laser scanning, and terrestrial laser scanning.
   In recent years, TLS has been more mature with the development of computer technology and digital image processing technology [1–5]. TLS could obtain the three-dimensional information of the object by measuring many sample points of the objects [6–8].
More and more researchers have applied TLS to forest inventory and tree structural parameter extraction [9–14]. These characteristics of trees, such as the diameter of breast height (DBH), tree height, crown width, and branch structure, are important to the forest inventory. TLS is characterized by high precision and large data volume, which is very suitable for data acquisition. It can effectively avoid waste of labor and time consumption and improve the accuracy of the collected information. Based on these point cloud data obtained by TLS, we can measure many parameters of trees, such as tree DBH [15], tree height, biomass, and extraction of tree skeleton [16–19].
The tree skeleton is a concise description of the three-dimensional structure of trees, which can describe the shape and topological structure of a tree [20–23], and tree skeleton contains information about the geometry of the trees, branching patterns, and branching angles. And tree skeleton can also reflect the growth process and characteristics of trees [24]. The tree skeleton is the basis for studying the morphological structure of the trees. How to effectively and accurately obtain the tree skeleton is the key factor to analysis of the morphology of trees.
  Some algorithms were designed to extract the tree skeleton from point cloud data [25–31]. First of all, the skeleton nodes which can represent trunk and main branches would be extracted. And then skeleton nodes could be connected according to the tree structure. Finally, the tree skeleton would be generated.
  In order to realize this idea, Xu et al. had presented a method about extracting tree skeleton from point cloud data based on the shortest path algorithm and clustering algorithm [32].
  Yan et al. had used -means clustering method and cylinder detection method to get the tree skeleton [33]. In Delagrange and Rochon’s study [34], the skeleton is connected according to minimum spanning tree method. Wang et al. had presented a global optimization method based on perceptual structure [35, 36]. Bucksch and Lindenbergh had proposed a method of extracting tree skeleton from point cloud data based on octree [37].