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遥感技术与应用  2011, Vol. 26 Issue (1): 117-122    DOI: 10.11873/j.issn.1004-0323.2011.1.117
图像处理     
一种改进的基于平面拟合的机载LiDAR点云滤波方法
陈磊,赵书河
(南京大学地理与海洋科学学院,江苏 南京210093)
An Improved Plane Fitting based Filtering Algorithm for Airborne LiDAR Data
CHEN Lei,ZHAO Shu-he
(School of Geographic & Oceanographic Sciences,Nanjing University,Nanjing 210093,China)
 全文: PDF(3914 KB)  
摘要:

在分析现有方法优劣的基础上,提出一种改进的基于平面拟合的机载LiDAR点云滤波方法。首先基于区域生长算法对重采样的LiDAR数据图像分割,提取最大连通区域为初始地面,然后基于坡度阈值剔除初始地面内的地物点,最后对筛选后的地面点克里金插值实现滤波。采用国际摄影测量与遥感学会(ISPRS)提供的测试数据进行实验,研究了设置不同坡度阈值对滤波结果产生的影响。结果表明,坡度阈值取0.5时滤波效果最优,整体错误率仅为4.29%。与传统的基于平面拟合的滤波方法相比较,该方法更简单实用。

关键词: LiDAR平面拟合滤波区域生长    
Abstract:

Due to the strengths and weaknesses of the existing filtering methods,an improved plane fitting algorithm is presented for filtering of airborne LiDAR data.Firstly,resampled LiDAR data were segmented using region growing method,and then the largest connected region was selected as the initial ground area.Secondly,non\|ground points in the initial ground area were removed by a slope threshold.Finally,the ground points were interpolated using Kriging method and the filtering results were gained.The dataset provided by International Society for Photogrammetry and Remote Sensing (ISPRS) were selected as the test data.The influences of different slope thresholds on the filtering results were given after filtering experiments.The result shows that the optimal filtering effect is the best when the slope threshold sets to 0.5.The overall error rate can be only 4.29%.Contrast to the traditional filtering method based on plane fitting,the proposed method is more simple and practical.

Key words: LiDAR    Plane fitting    Filtering    Region growing
收稿日期: 2010-07-20 出版日期: 2011-05-05
:  TN958.98  
基金资助:

现代工程测量国家测绘局重点实验室开发基金资助项目(TJES1005)和国家自然科学基金项目(40501047)联合资助。

作者简介: 陈磊(1985-),男,江苏盐城人,硕士研究生,主要从事激光雷达数据处理研究。Email:tq0fxzz@163.com。
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引用本文:

陈磊,赵书河. 一种改进的基于平面拟合的机载LiDAR点云滤波方法[J]. 遥感技术与应用, 2011, 26(1): 117-122.

CHEN Lei,ZHAO Shu-he. An Improved Plane Fitting based Filtering Algorithm for Airborne LiDAR Data. Remote Sensing Technology and Application, 2011, 26(1): 117-122.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2011.1.117        http://www.rsta.ac.cn/CN/Y2011/V26/I1/117

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