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基于机载LiDAR数据的林下地形提取算法比较与组合分析

汪垚 张志玉 倪文俭 刘见礼 张大凤

汪垚, 张志玉, 倪文俭, 刘见礼, 张大凤. 基于机载LiDAR数据的林下地形提取算法比较与组合分析[J]. 北京林业大学学报, 2017, 39(12): 25-35. doi: 10.13332/j.1000-1522.20170300
引用本文: 汪垚, 张志玉, 倪文俭, 刘见礼, 张大凤. 基于机载LiDAR数据的林下地形提取算法比较与组合分析[J]. 北京林业大学学报, 2017, 39(12): 25-35. doi: 10.13332/j.1000-1522.20170300
WANG Yao, ZHANG Zhi-yu, NI Wen-jian, LIU Jian-li, ZHANG Da-feng. Comparison of filter algorithms and combination analysis for DEM extracting based on airborne laser scanning point clouds[J]. Journal of Beijing Forestry University, 2017, 39(12): 25-35. doi: 10.13332/j.1000-1522.20170300
Citation: WANG Yao, ZHANG Zhi-yu, NI Wen-jian, LIU Jian-li, ZHANG Da-feng. Comparison of filter algorithms and combination analysis for DEM extracting based on airborne laser scanning point clouds[J]. Journal of Beijing Forestry University, 2017, 39(12): 25-35. doi: 10.13332/j.1000-1522.20170300

基于机载LiDAR数据的林下地形提取算法比较与组合分析

doi: 10.13332/j.1000-1522.20170300
基金项目: 

国家自然科学基金项目 41301395

国家自然科学基金项目 41471311

国家自然科学基金项目 41371357

详细信息
    作者简介:

    汪垚。主要研究方向:森林遥感。Email:wangyao@radi.ac.cn   地址:100101  北京市朝阳区大屯路甲20号北中国科学院遥感与数字地球研究所

    责任作者:

    张志玉,博士,助理研究员。主要研究方向:森林遥感。Email:zhangzy@radi.ac.cn   地址:同上

  • 中图分类号: S771.8

Comparison of filter algorithms and combination analysis for DEM extracting based on airborne laser scanning point clouds

  • 摘要: 激光雷达(LiDAR)克服了传统测量技术的缺点, 成为了获取DEM的新型手段。针对不同地形林区,选择合理的点云滤波算法,是提取林下地形的关键步骤。本研究在黑龙江省凉水自然保护区内选择了3块具有代表性的区域,分别为平缓山地林区、陡峭山地林区和复杂地区。以1:10 000地形图矢量化生成的高精度DEM为参考,评价了迭代线性最小二乘法、基于坡度法、不规则三角网法(TIN)点云滤波算法在3种地形的适应性。结果表明:不同算法有不同的适应区域。3种方法在平缓山地林区都具有良好的效果,决定系数(R2)均达到了0.98,均方根误差(RMSE)均低于0.21 m。迭代线性最小二乘法在复杂地区滤波效果最好,R2为0.94,RMSE为0.21 m;不规则三角网法在陡峭山地林区效果最好,R2为0.99,RMSE为1.43 m。但是,单一的方法在复杂区域情况下、陡峭山地林区,有明显的分类误差,会将植被分为地面点。为提高林下地形提取精度,本文提出不同滤波方法的组合双重滤波,结果表明,迭代线性最小二乘法和不规则三角网方法组合可以在减少参数调整情况下得到良好滤波效果,对复杂地区、陡峭山地林区滤波效果大大改善。

     

  • 图  1  3块典型样地

    Figure  1.  Three typical plots

    图  2  原始数字高程模型示意图

    Figure  2.  Sketch map of original digital elevation model

    图  3  使用不同方法过滤后地面点产生的数字渲染图

    Figure  3.  Digital surface models generated from the filtered bare-earth points using different filtering algorithms

    图  4  精度评估

    Figure  4.  Accuracy assessment

    图  5  使用滤波组合方法产生的数字渲染图

    a.不规则三角网和基于坡度的陡峭山地林区; b.迭代线性最小二乘和基于坡度的陡峭山地林区;c.不规则三角网和迭代线性最小二乘的陡峭山地林区;d.基于坡度和不规则三角网的陡峭山地林区;e.基于坡度和迭代线性最小二乘的陡峭山地林区;f.迭代线性最小二乘和不规则三角网的陡峭山地林区;g.不规则三角网和基于坡度的复杂地区;h.迭代线性最小二乘和基于坡度的复杂地区;i.不规则三角网和迭代线性最小二乘的复杂地区;j.基于坡度和不规则三角网的复杂地区;k.基于坡度和迭代线性最小二乘的复杂地区;l.迭代线性最小二乘和不规则三角网的复杂地区。

    Figure  5.  Digital surface models resulted from different combined filtering algorithms

    a, based on TIN and slope of steep mountainous forest area; b, based on iterative linear least squares prediction and slope of steep mountainous forest area; c, based on TIN and iterative linear least squares prediction of steep mountainous forest area; d, based on slope and TIN of steep mountainous forest area; e, based on slope and iterative linear least squares prediction of steep mountainous forest area; f, based on iterative linear least squares prediction and TIN of steep mountainous forest area; g, based on TIN and slope of complicated area; h, based on iterative linear least squares prediction and slope of complicated area; i, based on TIN and iterative linear least squares prediction of complicated area; j, based on slope and TIN of complicated area; k, based on slope and iterative linear least squares prediction of complicated area; l, based on iterative linear least squares prediction and TIN of complicated area.

    图  6  精度评估

    a.不规则三角网和基于坡度; b.迭代线性最小二乘法和基于坡度; c.不规则三角网和迭代线性最小二乘法; d.基于坡度和不规则三角网; e.基于坡度和迭代线性最小二乘法; f.迭代线性最小二乘法和不规则三角网。

    Figure  6.  Accuracy assessment

    a, based on TIN and slope degree; b, based on iterative linear least squares prediction and slope degree; c, based on TIN and iterative linear least squares prediction; d, based on slope degree and TIN; e, based on slope degree and iterative linear least squares prediction; f, based on iterative linear least squares prediction and TIN.

    表  1  迭代线性最小二乘法理想参数表

    Table  1.   Parameters in iterative linear least squares prediction filtering algorithm

    区域类型
    Region type
    参数a
    Parameter a
    参数b
    Parameter b
    参数g
    Parameter g
    参数ω
    Parameter ω
    网格大小
    Grid size/m
    迭代次数
    Iterative time
    平缓Gently slope 1 4 0.1 0.4 8 10
    陡峭Steep area 1 4 0.1 0.3 5 6
    复杂Complicated area 1 4 0 0.4 6 8
    下载: 导出CSV

    表  2  坡度滤波参数表

    Table  2.   Parameters in filtering algorithm based on slope

    区域类型
    Region type
    网格化窗口尺寸
    Grid size
    窗口扩展因子
    Grid size extension factor
    坡度阈值
    Slope threshold
    平缓Gently slope 1 9 0.6
    陡峭Steep area 1 8 0.7
    复杂Complicated area 1 10 0.5
    下载: 导出CSV

    表  3  不同滤波算法组合

    Table  3.   Different combined filtering algorithms

    顺序
    Sequence
    组合1
    Combination 1
    组合2
    Combination 2
    组合3
    Combination 3
    组合4
    Combination 4
    组合5
    Combination 5
    组合6
    Combination 6

    First
    不规则三角网
    Triangulated irregular network (TIN)
    迭代线性最小二乘法
    Iterative linear least
    squares prediction
    基于坡度
    Based on slope
    不规则三角网
    TIN
    基于坡度
    Based on slope
    迭代线性最小二乘法
    Iterative linear least
    squares prediction

    Second
    基于坡度
    Based on slope
    基于坡度
    Based on slope
    迭代线性最小二乘法
    Iterative linear least
    squares prediction
    迭代线性最小二乘法
    Iterative linear least
    squares prediction
    不规则三角网
    TIN
    不规则三角网
    TIN
    下载: 导出CSV
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  • 收稿日期:  2017-08-25
  • 修回日期:  2017-11-14
  • 刊出日期:  2017-12-01

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