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    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
    shu
    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
    shu

    森林区域机载LiDAR点云数据的改进八叉树滤波算法研究

    Improved octree filtering algorithm of airborne LiDAR data in forest environment

      摘要
    • 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。

       

      Abstract:
      ObjectiveBy airborne LiDAR point cloud data, the 3D coordinates of ground points can be obtained accurately. In order to enhance the accuracy of point cloud filtering, a filter method of point cloud data was proposed by the filtering analysis using airborne LiDAR point cloud data in Dayekou forest area of Gansu Province, western China.
      MethodBased on the octree filtering algorithm, the complex terrain in forest was decomposed into a large number of slopes by the improved model. The accuracy of data segmentation was enhanced by reducing the nodes'size, with retaining the original information of the data. Additionally, for the uneven or tough terrain of forest, the slope analysis was introduced into the filter algorithm, to prevent the undergrowth points from being classified into ground points.
      ResultThe experiment results showed that the method could improve the segmentation accuracy of point cloud data to some extent, and the total errors of filtering in three different terrains were 4.57%, 4.75%, 5.83%, respectively. The proved filtering model was practical to filter point cloud under different terrains in forest area.
      ConclusionThus, the improved octree filtering algorithm proposed in this paper can make full use of the data structure features to achieve fast and high-precision filtering. Meanwhile, it saves time and cost, and lays the foundation for the subsequent extraction of forest parameters.

       

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