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森林区域机载LiDAR点云数据的改进八叉树滤波算法研究

杨铭 张晓丽 霍朗宁 高凌寒

杨铭, 张晓丽, 霍朗宁, 高凌寒. 森林区域机载LiDAR点云数据的改进八叉树滤波算法研究[J]. 北京林业大学学报, 2018, 40(11): 102-111. doi: 10.13332/j.1000-1522.20180130
引用本文: 杨铭, 张晓丽, 霍朗宁, 高凌寒. 森林区域机载LiDAR点云数据的改进八叉树滤波算法研究[J]. 北京林业大学学报, 2018, 40(11): 102-111. doi: 10.13332/j.1000-1522.20180130
Yang Ming, Zhang Xiaoli, Huo Langning, Gao Linghan. Improved octree filtering algorithm of airborne LiDAR data in forest environment[J]. Journal of Beijing Forestry University, 2018, 40(11): 102-111. doi: 10.13332/j.1000-1522.20180130
Citation: Yang Ming, Zhang Xiaoli, Huo Langning, Gao Linghan. Improved octree filtering algorithm of airborne LiDAR data in forest environment[J]. Journal of Beijing Forestry University, 2018, 40(11): 102-111. doi: 10.13332/j.1000-1522.20180130

森林区域机载LiDAR点云数据的改进八叉树滤波算法研究

doi: 10.13332/j.1000-1522.20180130
基金项目: 

国家重大科学仪器设备开发专项“机载双频激光雷达产品开发和应用” 2013YQ120343

详细信息
    作者简介:

    杨铭。主要研究方向:3S系统集成及应用开发。Email:59231516@qq.com 地址:100083北京市海淀区清华东路35号北京林业大学林学院

    责任作者:

    张晓丽,教授,博士生导师。主要研究方向:森林动态监测、定量遥感研究。Email: zhang-xl@263.net 地址:同上

  • 中图分类号: S771.8

Improved octree filtering algorithm of airborne LiDAR data in forest environment

  • 摘要: 目的利用机载LiDAR点云数据能准确获取地物点三维坐标的特点,本文对森林区域LiDAR数据进行滤波分析,旨在提高点云滤波精度。方法基于改进的八叉树模型,将复杂地形分解为大量山坡地形,通过改变节点尺寸,既保留了点云的原始信息,又增强了点云数据分割的准确性。针对森林区域地形起伏不定的实际情况,在滤波算法中引入坡度判断,在一定程度上改善了山坡低矮植被易被错分为地面点的情况。结果对于3组不同地形下的点云数据,滤波总错误率分别为4.57%、4.75%和5.83%。这一结果对森林区域不同地形下的点云滤波具有一定的实用性。结论本文提出的改进八叉树滤波算法可以充分利用数据结构特征实现快速、高精度的滤波,从而节约时间成本和运行成本,也为后续森林参数的提取奠定基础。

     

  • 图  1  八叉树示意图

    Figure  1.  Schematic diagram of octree

    图  2  节点编号示意图

    Figure  2.  Schematic diagram of node No.

    图  3  传统八叉树模型下的点云分割

    图中坐标均为点云的空间位置坐标。下同。

    Figure  3.  Point cloud segmentation based on conventional octree model

    The coordinates in the figure are all spatial position coordinates of point cloud. The same below.

    图  4  改进八叉树模型下的点云分割

    Figure  4.  Point cloud segmentation based on improved octree model

    图  5  数据1滤波实验结果

    Figure  5.  Results of filtering in data 1

    图  6  数据2滤波实验结果

    Figure  6.  Results of filtering in data 2

    图  7  数据3滤波实验结果

    Figure  7.  Results of filtering in data 3

    表  1  数据1滤波精度验证

    Table  1.   Precision verification of filtering in data 1

    点云数量
    Number of point cloud
    第Ⅰ类错误率
    Error rate of type Ⅰ
    第Ⅱ类错误率
    Error rate of type Ⅱ
    总错误率
    Total error rate
    418 6.40% 2.62% 4.57%
    注:第Ⅰ类错误率是指将地面点误分类为地物点的错误率;第Ⅱ类错误率是指将地物点分类为地面点的错误率;以目视解译的分类结果作为验证计算错误率。Notes: error rate of typeⅠ: the misclassification rate of classifying ground points into object points; error rate of typeⅡ: the misclassification rate of classifying object points into ground points; classification result of visual interpretation is used to verify the error rate of calculation.
    下载: 导出CSV

    表  2  数据2滤波精度验证

    Table  2.   Precision verification of filtering in data 2

    点云数量
    Number of point cloud
    第Ⅰ类错误率
    Error rate of typeⅠ
    第Ⅱ类错误率
    Error rate of typeⅡ
    总错误率
    Total error rate
    4 191 5.70% 3.35% 4.75%
    下载: 导出CSV

    表  3  数据3滤波精度验证

    Table  3.   Precision verification of filtering in data 3

    点云数量
    Number of point cloud
    第Ⅰ类错误率
    Error rate of typeⅠ
    第Ⅱ类错误率
    Error rate of typeⅡ
    总错误率
    Total error rate
    4 372 5.90% 5.79% 5.83%
    下载: 导出CSV
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  • 收稿日期:  2018-04-13
  • 修回日期:  2018-09-18
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