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FANG Hao, LI Hongjun. Counting of Plantation Trees Based on Line Detection of Point Cloud Data[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210407
Citation: FANG Hao, LI Hongjun. Counting of Plantation Trees Based on Line Detection of Point Cloud Data[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210407

Counting of Plantation Trees Based on Line Detection of Point Cloud Data

doi: 10.13203/j.whugis20210407
Funds:

This research was supported by the 2020 Postgraduate Curriculum Construction Project of Beijing Forestry University (HXKC2005)

  • Received Date: 2021-07-29
  • Objectives:When performing plantation surveys using laser point cloud data, there are missing points in the scanned point cloud data due to the occlusion and self-occlusion of trees during laser scanning, the felling of trees and other reasons. So, the locations of the missing trees are inaccurate, and the forest survey results have large errors. The key to solving this problem is to realize the filling of the missing tree point cloud. Methods:This paper defines a concept named degree-of-collinearity, and constructs a method based on degree-of-collinearity combined with straight line detection to fill in missing data. Results:For the experimental results of simulated data, the average accuracy of the proposed algorithm is 97.28%; for the experimental results of plantation data, the proposed algorithm detects the location of 9 missing trees, and the degree-of-collinearity rises from 0.2193 to 0.2705. Conclusions:The experimental results show that this method can realize the optimal inference of missing location, strengthen the collinear relationship of filled data and can also be applied to count the missing trees in the artificial forest.
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    [2] Zhang X P, Li H J, Dai M R, et al.Data-Driven Synthetic Modeling of Trees[J].IEEE Transactions on Visualization and Computer Graphics, 2014, 20(9):1214-1226
    [3] Izzuddin R M, Pien C F, Jedol D, et al.Missing Value Imputation for PM10 Concentration in Sabah Using Nearest Neighbour Method (NNM) and Expectation-Maximization (EM) Algorithm[J].Asian Journal of Atmospheric Environment, 2020, 14(1):62-72
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    [7] Lu X H, Liu Y H, Li K.Fast 3D Line Segment Detection from Unorganized Point Cloud[EB/OL].2019:arXiv:1901.02532.https://arxiv.org/abs/1901.02532
    [8] Schnabel R, Wahl R, Klein R.Efficient RANSAC for Point-Cloud Shape Detection[J].Computer Graphics Forum, 2007, 26(2):214-226
    [9] Qi C R, Litany O, He K M, et al.Deep Hough voting for 3D object detection in point clouds[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV).Seoul, Korea (South).:9276-9285
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Counting of Plantation Trees Based on Line Detection of Point Cloud Data

doi: 10.13203/j.whugis20210407
Funds:

This research was supported by the 2020 Postgraduate Curriculum Construction Project of Beijing Forestry University (HXKC2005)

Abstract: Objectives:When performing plantation surveys using laser point cloud data, there are missing points in the scanned point cloud data due to the occlusion and self-occlusion of trees during laser scanning, the felling of trees and other reasons. So, the locations of the missing trees are inaccurate, and the forest survey results have large errors. The key to solving this problem is to realize the filling of the missing tree point cloud. Methods:This paper defines a concept named degree-of-collinearity, and constructs a method based on degree-of-collinearity combined with straight line detection to fill in missing data. Results:For the experimental results of simulated data, the average accuracy of the proposed algorithm is 97.28%; for the experimental results of plantation data, the proposed algorithm detects the location of 9 missing trees, and the degree-of-collinearity rises from 0.2193 to 0.2705. Conclusions:The experimental results show that this method can realize the optimal inference of missing location, strengthen the collinear relationship of filled data and can also be applied to count the missing trees in the artificial forest.

FANG Hao, LI Hongjun. Counting of Plantation Trees Based on Line Detection of Point Cloud Data[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210407
Citation: FANG Hao, LI Hongjun. Counting of Plantation Trees Based on Line Detection of Point Cloud Data[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210407
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