Top-Down Approach to the Automatic Extraction of Individual Trees from Scanned Scene Point Cloud Data

Urban trees are essential elements in outdoor scenes recorded via terrestrial laser scanning. Although considerable interest has been centered on tree detection and reconstruction in recent years, trees cannot be easily extracted from dense and unorganized data because of the complexity and diversity of trees. In this paper, we present a top-down approach for detecting trees from point cloud data acquired for dense urban areas. Appropriate feature subsets are chosen, and then the candidate tree clusters are selected via a binary classification. After distinguishing the 3D points belonging to tree-like objects, individual trees are extracted by spectral clustering. Furthermore, a weighted constraint rule is proposed to refine the individual tree clusters. The methodology is tested on five real-world datasets that include different varieties of trees. The results reveal that most of the individual trees can be correctly detected and extracted. The results are quantitatively evaluated and reveal a global F1 value of approximately 97 percent and a precision of approximately 98 percent. Comparative analysis on the datasets is also provided to prove the effectiveness of our proposed method.