方浩, 李红军. 点云数据直线检测及其在人工林树木计数中的应用[J]. 武汉大学学报 ( 信息科学版), 2024, 49(2): 208-215. DOI: 10.13203/j.whugis20210407
引用本文: 方浩, 李红军. 点云数据直线检测及其在人工林树木计数中的应用[J]. 武汉大学学报 ( 信息科学版), 2024, 49(2): 208-215. DOI: 10.13203/j.whugis20210407

点云数据直线检测及其在人工林树木计数中的应用

  • 摘要: 基于激光点云数据进行人工林调查时,由于激光扫描时树木的遮挡与自遮挡、树木被砍伐等原因造成扫描的点云数据有缺失,遗漏树木的位置判断不准确,森林调查结果误差大,解决这一问题的关键是实现缺失树木的填补。定义了离散点集共线度的概念,构建了一个基于点集共线度最大化模型并结合直线检测进行缺失数据填补的方法。模拟数据实验结果:该方法的平均准确率为97.28%;人工林数据实验结果:该方法检测到9棵缺失树的位置,共线度由0.219 3增大为0.270 5。实验结果表明,该方法不仅可以实现缺失位置的最优推断,加强填补后数据的共线关系,也可应用于人工林的缺失树木计数。

     

    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 0.219 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.

     

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