A Structure-Aware Global Optimization Method for Reconstructing 3-D Tree Models From Terrestrial Laser Scanning Data
作者:Wang, Z (Wang, Zhen)[ 1 ] ; Zhang, LQ (Zhang, Liqiang)[ 1 ] ; Fang, T (Fang, Tian)[ 2 ] ; Mathiopoulos, PT (Mathiopoulos, P. Takis)[ 3,4 ] ; Qu, HM (Qu, Huamin)[ 2 ] ; Chen, D (Chen, Dong)[ 5 ] ; Wang, YB (Wang, Yuebin)[ 1 ]
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷: 52  期: 9  页: 5653-5669
DOI: 10.1109/TGRS.2013.2291815
出版年: SEP 2014

摘要
A 3-D tree structure plays an important role in many scientific fields, including forestry and agriculture. For example, terrestrial laser scanning (TLS) can efficiently capture high-precision 3-D spatial arrangements and structure of trees as a point cloud. In the past, several methods to reconstruct 3-D trees from the TLS point cloud were proposed. However, in general, they fail to process incomplete TLS data. To address such incomplete TLS data sets, a new method that is based on a structure-aware global optimization approach (SAGO) is proposed. The SAGO first obtains the approximate tree skeleton from a distance minimum spanning tree (DMst) and then defines the stretching directions of the branches on the tree skeleton. Based on these stretching directions, the SAGO recovers missing data in the incomplete TLS point cloud. The DMst is applied again to obtain the refined tree skeleton from the optimized data, and the tree skeleton is smoothed by employing a Laplacian function. To reconstruct 3-D tree models, the radius of each branch section is estimated, and leaves are added to form the crown geometry. The developed methodology has been extensively evaluated by employing a dozen TLS point clouds of various types of trees. Both qualitative and quantitative performance evaluation results have indicated that the SAGO is capable of effectively reconstructing 3-D tree models from grossly incomplete TLS point clouds with significant amounts of missing data.

通讯作者地址: Wang, Z (通讯作者)
Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.
地址:
[ 1 ] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
[ 2 ] Hong Kong Univ Sci & Technol, Kowloon, Hong Kong, Peoples R China
[ 3 ] Natl Observ Athens Metaxa & V Pavlou, Inst Astron Astrophys Space Applicat & Remote Sen, Athens 14536, Greece
[ 4 ] Univ Athens, Dept Informat & Telecommun, Athens 15784, Greece
[ 5 ] Nanjing Forestry Univ, Nanjing 210037, Jiangsu, Peoples R China