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遥感技术与应用  2019, Vol. 34 Issue (6): 1261-1268    DOI: 10.11873/j.issn.1004-0323.2019.6.1261
数据与图像处理     
一种顾及地形特征的布料模拟滤波改进方法
李雅盟(),李朝奎(),王书涵,方军
湖南科技大学 地理空间信息技术国家地方联合工程实验室,湖南 湘潭 411201
A CSF-Modified Filtering Method based on Topography Feature
Yameng Li(),Chaokui Li(),Shuhan Wang,Jun Fang
National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology
 全文: PDF(4109 KB)   HTML
摘要:

为了提高激光点云滤波算法在地形复杂区域的精度、效率以及自适应性,基于布料模拟滤波算法,提出了一种面向大范围复杂场景的顾及地形特征的点云滤波方法。该方法首先采用基于坡度的动态格网分割方法,对剔除粗差后的点云建立格网索引;其次利用每个格网的邻域格网中的最低点建立曲面方程拟合高程值,通过计算真实高程与拟合高程差值实现高程归一化;然后使用布料模拟算法模拟布料下降过程得到地形布料的最终形态,进而通过阈值限定实现地面点提取。在地形复杂的测试区使用相同滤波参数进行算法改进前后对比测试,结果表明:改进算法的正确率由原CSF算法的88.9%提高到改进后算法的95.19%;I类误差、II类误差分别由9.71%、1.39%下降到4.57%、0.24%,且滤波时长由164 s缩减至60.9 s。本文提出的改进算法在保证大范围复杂场景区域滤波正确率的基础上,对不同地形具有较强的自适应性,且提高了滤波计算效率。

关键词: 机载激光点云点云滤波布料模拟滤波地形特征高程归一化    
Abstract:

In order to improve the precision, efficiency and robustness in complicated terrain conditions, a CSF-modified filtering method was presented based on Cloth Simulation Filtering(CSF). Firstly, with the method of dynamic grids based on slope, we established a gird index for the point cloud of which the gross errors are eliminated. Secondly, the terrain surface equation is produced by the nadir in every girds to fit elevation value of every point. And the difference between real and fitted value is the elevation normalized. Finally, with CSF algorithm to simulate gradually filtering process to refine the cloth and ground points are obtained by the final cloth and limited threshold. We verify the presented algorithm and the original CSF algorithm with the same filtering parameters in the complicated test area. The accuracy increases from 88.9% of original CSF algorithm to 95.19% of the CSF-modified algorithm. And error of type I and type II decrease from 9.71% and 1.39% to 4.57% and 0.24% respectively. In addition, the filtering duration is shortened from 164 s to 60.9 s. The result shows that on the basis of ensuring the accuracy of filtering in a wide range of complicated terrain, the modified method is not only adaptable for complicated situation areas but also helpful to enhance the efficiency of filtering computation.

Key words: LiDAR    Filtering    CSF    Topography feature    Elevation normalization
收稿日期: 2018-10-28 出版日期: 2020-03-23
ZTFLH:  P237  
基金资助: 国家自然科学基金项目(41571374);国家重点研发计划(2018YFB0504500);湖南省自然科学基金项目(2018JJ3158)
通讯作者: 李朝奎     E-mail: lym7049@outlook.com;chkl_hn@163.com
作者简介: 李雅盟(1993-),女,江苏徐州人,硕士研究生,主要从事机载LiDAR点云数据后处理研究。E?mail:lym7049@outlook.com
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引用本文:

李雅盟,李朝奎,王书涵,方军. 一种顾及地形特征的布料模拟滤波改进方法[J]. 遥感技术与应用, 2019, 34(6): 1261-1268.

Yameng Li,Chaokui Li,Shuhan Wang,Jun Fang. A CSF-Modified Filtering Method based on Topography Feature. Remote Sensing Technology and Application, 2019, 34(6): 1261-1268.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.6.1261        http://www.rsta.ac.cn/CN/Y2019/V34/I6/1261

图1  “弹簧—质点”模型 [20]
图2  CSF滤波算法的原理[19]
图3  CSF滤波算法内力作用(Zhang[19])
图4  CSF算法易出现的两种错误
图5  CSF改进滤波算法示意图
图6  多级格网种子点获取流程
图7  研究区域影像及LiDAR点云数据
范围3 000 m×1 500 m
点云密度3.278个/m2
高差243.47 m
最大建筑物长度120 m
点数12 044 134个
地面点个数6 373 906个
非地面点个数5 670 228个
表1  研究区域LiDAR点云数据
图8  高程归一化处理后点云
图9  图9改进滤波算法的地面点
参考数据滤波后数据参考数据点
地面点非地面点
地面点abe=a+b
非地面点cdf=c+d
滤波后点数g=a+ch=b+dn=a+b+c+d
表2  滤波误差的定义
序号地形参数使用后处理格网大小/m迭代次数/次阈值/m时长/sI类误差/%II类误差/%正确率/%
0relief21 0000.51649.711.3988.90
1relief25000.551.97.140.2392.63
2relief25000.553.34.570.2495.19
3steep slope25000.550.86.230.2993.48
4steep slope25000.542.93.110.496.49
5flat25000.540.69.880.2189.91
6flat25000.541.66.900.3292.78
7relief21 0000.560.94.570.2495.19
8relief15000.5227.51.970.8297.21
9relief25000.850.42.700.7396.57
10relief25001.051.22.431.0696.51
表3  滤波算法各参数设置及精度评定
图10  原始CSF滤波算法的地面点
图11  真实DTM
图12  改进滤波算法的DTM
图13  原始CSF滤波算法的DTM
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