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利用灰度体元模型的机载LiDAR 3D道路提取

王丽英 段孟柳

王丽英, 段孟柳. 利用灰度体元模型的机载LiDAR 3D道路提取. 自动化学报, 2020, 46(11): 2439-2447 doi: 10.16383/j.aas.c180527
引用本文: 王丽英, 段孟柳. 利用灰度体元模型的机载LiDAR 3D道路提取. 自动化学报, 2020, 46(11): 2439-2447 doi: 10.16383/j.aas.c180527
Wang Li-Ying, Duan Meng-Liu. Grayscale voxel model based airborne LiDAR 3D road extraction. Acta Automatica Sinica, 2020, 46(11): 2439-2447 doi: 10.16383/j.aas.c180527
Citation: Wang Li-Ying, Duan Meng-Liu. Grayscale voxel model based airborne LiDAR 3D road extraction. Acta Automatica Sinica, 2020, 46(11): 2439-2447 doi: 10.16383/j.aas.c180527

利用灰度体元模型的机载LiDAR 3D道路提取

doi: 10.16383/j.aas.c180527
基金项目: 

国家自然科学基金 41471315

辽宁省自然科学基金 20170540419

辽宁省教育厅科学技术研究项目 LJ2019JL015

详细信息
    作者简介:

    段孟柳  中铁二十四局集团新余工程有限公司助理工程师. 2017年获得辽宁工程技术大学摄影测量与遥感专业硕士学位.主要研究方向为机载LiDAR基础理论与应用研究. E-mail:dml19930103@163.com

    通讯作者:

    王丽英  博士, 辽宁工程技术大学测绘与地理科学学教授. 2011年获得辽宁工程技术大学地图制图学与地理信息工程专业博士学位.主要研究方向为LiDAR基础理论与应用研究.本文通信作者. E-mail: wangliyinglntu@163.com

Grayscale Voxel Model Based Airborne LiDAR 3D Road Extraction

Funds: 

National Natural Science Foundation of China 41471315

Natural Science Foundation of Liaoning Province 20170540419

Science and Technology Research Project of the Educational Department of Liaoning Province LJ2019JL015

More Information
    Author Bio:

    DUAN Meng-Liu  Assistant engineer at the China Railway 24th Bureau Group CO., LTD. She received her master degree in 2017 from Liaoning Technical University. Her research interest covers studies on theory and application of airborne LiDAR

    Corresponding author: WANG Li-Ying  Ph. D., professor at the School of Geomatics, Liaoning Technical University. She received her Ph. D. degree in 2011 from Liaoning Technical University. Her main research interest covers studies on theory and application of LiDAR. Corresponding author of this paper
  • 摘要: 面向机载LiDAR数据的道路提取算法的常用数据结构存在局限: 2D格网及TIN表达多次回波数据时存在的信息损失会影响提取结果的完整性且提取结果为2D形式; 点云的空间结构及拓扑信息难以利用, 由此导致算法设计的困难.为此, 提出了一种基于灰度体元模型的3D道路提取算法.算法首先将LiDAR数据规则化为灰度体元模型(灰度为体元内LiDAR点的平均强度值的量化表示); 然后选取道路种子体元进而搜寻并标记种子及其3D连通区域为道路体元; 最后利用数学形态学优化提取结果.基于ISPRS提供的包含不同复杂程度的城区路网LiDAR数据测试"邻域尺度"和"灰度差阈值"参数的敏感性及提出的算法的精度.实验结果表明: 56邻域为最佳邻域尺度、2为最佳灰度差阈值; 道路提取的平均质量、完整度及正确率分别为70%、86.77%及81.13%;对相对平坦的单层路网及起伏较大的复杂路网均可成功提取.
    Recommended by Associate Editor WU Yi-Hong
    1)  本文责任编委  吴毅红
  • 图  1  GVM构建流程图

    Fig.  1  Flowchart of GVM construction

    图  2  邻域尺度

    Fig.  2  Adjacency size

    图  3  实验数据

    Fig.  3  Experiment data

    图  4  道路提取结果

    Fig.  4  Road extraction results

    图  5  道路网

    Fig.  5  Road network

    图  6  CSite2的Terrasolid提取结果误差图

    Fig.  6  Error distribution of Terrasolid for CSite2

    图  7  2D和3D道路提取算法结果对比

    Fig.  7  Comparison of 2D and 3D road extraction algorithms

    表  1  各不同邻域尺度及$T_{i}$下的道路提取算法总误差

    Table  1  Total errors of the proposed algorithm with different adjacent sizes and $T_{i}$

    CSite2/CSite3提取结果总误差(%)
    $T_{i}$6邻域18邻域26邻域56邻域64邻域
    146.30/40.6432.26/31.8725.89/24.1520.47/17.9417.98/12.44
    239.57/32.3228.34/25.0219.07/18.8712.81/8.2918.73/15.65
    334.25/30.0626.16/19.2120.83/10.3315.58/13.8423.16/20.45
    437.29/33.1230.21/20.0424.56/12.8726.74/16.5129.86/25.31
    下载: 导出CSV

    表  2  提出的算法的精度

    Table  2  The accuracy of the proposed algorithm

    数据$R_{com}$(%)$R_{cor}$(%)$R_{q}$(%)
    CSite284.8380.7670.57
    CSite388.7181.5073.84
    下载: 导出CSV

    表  3  Terrasolid道路提取精度

    Table  3  The road extraction accuracy of Terrasolid

    数据$R_{com}$ (%) $R_{cor}$ (%)$R_{q}$ (%)
    CSite288.264.159.1
    CSite393.856.954.8
    下载: 导出CSV
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  • 收稿日期:  2018-08-02
  • 录用日期:  2018-12-18
  • 刊出日期:  2020-11-24

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