J4 ›› 2010, Vol. 40 ›› Issue (5): 1205-1210.

• 地球探测与信息技术 • 上一篇    下一篇

一种机载雷达点云数据的快速分类方法

李慧盈1|李文辉1|陈圣波2   

  1. 1.吉林大学 计算机科学与技术学院|长春 130012;
    2.吉林大学 地球探测科学与技术学院|长春 130026
  • 收稿日期:2009-12-24 出版日期:2010-09-26 发布日期:2010-09-26
  • 通讯作者: 陈圣波(1967-)男,河南罗山人,教授,博士生导师,主要从事遥感与地理信息系统领域研究 E-mail:chensb@jlu.edu.cn
  • 作者简介:李慧盈(1978-)|女|吉林长春人|博士研究生|讲师|主要从事图形图像处理及三维信息可视化领域研究|E-mail:lihuiying@jlu.edu.cn
  • 基金资助:

    国家“863”项目(2006AA12Z102);教育部新世纪优秀人才支持计划(NCET-07-0353):国家自然科学基金项目(60873147):高等学校博士学科点专项基金项目(20060183042)

A Method for Classify Point Clouds of Airborne Laser Scanning

LI Hui-ying1|LI Wen-hui1|CHEN Sheng-bo2   

  1. 1.College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    2.College of GeoExploration Science and Technology, Jilin University, Changchun 130026, China
  • Received:2009-12-24 Online:2010-09-26 Published:2010-09-26

摘要:

机载激光雷达扫描技术可以以点云形式快速获取地形表面高精度三维信息。基于激光雷达扫描数据及建筑物本身的拓扑信息就可以对建筑物进行精确的重建,而重建中最关键的技术是对点云数据进行分类,进而进行地物识别。对大规模三维点云数据进行快速分类,提出一种采用区域分割结合基于最小二乘平差的多项式拟合方法,将大量离散的三维点云分割后进行多项式拟合,并将二维数据分类转化为一维数据分类。在分类的基础上,将建筑物几何规则作为约束条件提取了房屋边缘。实验分析表明,该方法既能去除多余噪声,又能有效保留特征点,分类的总误差率低于3%。

关键词: 地物识别, 最小二乘逼近, 点云数据, 边缘提取

Abstract:

Airborne laser scan technique is able to acquire the three dimensional geographic information of areas and objects on the ground quickly with the form of point clouds. Based on LiDAR data and topographic information of objects, the model of building can be determined. We classify the point clouds into terrain and off-terrain points based on region segment polynomial approximation and the least squares adjustment, and then extract edge of building with the method of RANSAC and topographic knowledge. The experiment result shows that this method can delete noisy points and preserve feature points efficiently which the total error rate is less than 3%.

Key words: objects recognition, least squares approximations, point clouds, building edge extraction

中图分类号: 

  • TP751
[1] 杨长保, 姜琦刚, 刘万崧, 邱殿明. 基于ASTER数据的内蒙古东乌珠穆沁北部地区遥感蚀变信息提取[J]. J4, 2009, 39(6): 1163-1167.
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