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

被引：0
|
作者：
Fang H. [1 ]
Li H. [1 ]
机构：
[1] College of Science, Beijing Forestry University, Beijing
来源：
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | 2024年 / 49卷 / 02期
关键词：
degree of collinearity; discrete point set; line detection; missing data completion; tree count;
D O I：
10.13203/j.whugis20210407
中图分类号：
学科分类号：
摘要：
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 0.219 3 to 0.270 5. 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. © 2024 Editorial Department of Geomatics and Information Science of Wuhan University. All rights reserved.
引用
收藏
页码：208 / 215
页数：7
相关论文
共 26 条
  • [1] Luo Yubo, Huang Hongyu, Tang Liyu, Et al., Tree Height and Diameter Extraction with 3D Reconstruction in a Forest Based on TLS, Remote Sensing Technology and Application, 34, 2, pp. 243-252, (2019)
  • [2] Roberts J W, Tesfamichael S, Gebreslasie M, Et al., Forest Structural Assessment Using Remote Sensing Technologies:An Overview of the Current State of the Art[J], Southern Hemisphere Forestry Journal, 69, 3, pp. 183-203, (2007)
  • [3] Li W K, Guo Q H, Jakubowski M K, Et al., A New Method for Segmenting Individual Trees from the LiDAR Point Cloud [J], Photogrammetric Engineering & Remote Sensing, 78, 1, pp. 75-84, (2012)
  • [4] Wang Shangshang, Wang Jun, Sun Si, Landscape Trees Counting by Remote Sensing Technology Based on UAV, Journal of Zhongkai University of Agriculture and Engineering, 30, 3, pp. 37-42, (2017)
  • [5] Wu Tiandong, Qi Shu, Identification and Statistical Study on Tree Number of Eucalyptus Plantation Based on OpenCV, Forestry Science & Technology, 45, 5, pp. 37-38, (2020)
  • [6] Cao Wei, Chen Dong, Shi Yufeng, Et al., Progress and Prospect of LiDAR Point Clouds to 3D Tree Models, Geomatics and Information Science of Wuhan University, 46, 2, pp. 203-220, (2021)
  • [7] 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, 20, 9, pp. 1214-1226, (2014)
  • [8] Liu Yawen, Zhang Ying, Chen Quan, Vehicle Point Cloud Data Enhancement Method Combined with Panoramic Image, Geomatics and Information Science of Wuhan University, 45, 7, pp. 1015-1020, (2020)
  • [9] Rumaling M I, Chee F P, Dayou J, Et al., Missing Value Imputation for PM10 Concentration in Sabah Using Nearest Neighbour Method and Expectation-Maximization Algorithm[J], Asian Journal of Atmospheric Environment, 14, 1, pp. 62-72, (2020)
  • [10] Ozturk A., Accuracy Improvement in Air-Quality Forecasting Using Regressor Combination with Missing Data Imputation[J], Computational Intelligence, 37, 1, pp. 226-252, (2021)
123