[1]蔡香玉,杨 林,吕海洋.基于径向基函数神经网络的机载LiDAR点云空洞填补方法[J].南京师范大学学报(工程技术版),2017,17(03):057.[doi:10.3969/j.issn.1672-1292.2017.03.009]
 Cai Xiangyu,Yang Lin,Lü Haiyang.Filling Method of Airborne LiDAR Point Cloud Hole Based onthe Radial Basis Function Neural Network[J].Journal of Nanjing Normal University(Engineering and Technology),2017,17(03):057.[doi:10.3969/j.issn.1672-1292.2017.03.009]
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基于径向基函数神经网络的机载LiDAR点云空洞填补方法
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南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
17卷
期数:
2017年03期
页码:
057
栏目:
计算机工程
出版日期:
2017-09-30

文章信息/Info

Title:
Filling Method of Airborne LiDAR Point Cloud Hole Based onthe Radial Basis Function Neural Network
文章编号:
1672-1292(2017)03-0057-06
作者:
蔡香玉1234杨 林1234吕海洋1234
(1.南京师范大学虚拟地理环境教育部重点实验室,江苏 南京 210023)(2.江苏省地理环境演化国家重点实验室培育建设点,江苏 南京 210023)(3.江苏省地理信息资源开发与利用协同创新中心,江苏 南京 210023)(4.南京师范大学地理科学学院,江苏 南京 210023)
Author(s):
Cai Xiangyu1234Yang Lin1234Lü Haiyang1234
(1.Key Laboratory of Virtual Geographic Environment of Ministry of Education,Nanjing Normal University,Nanjing 210023,China)(2.State Key Laboratory Cultivation Base of Geographical Environment Evolution of Jiangsu Province,Nanjing 210023,China)(3.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing 210023,China)(4.School of Geography Science,Nanjing Normal University,Nanjing 210023,China)
关键词:
空间插值LiDAR点云空洞填补RBF神经网络
Keywords:
spatial interpolationLiDAR point cloudfilling holeRBFneural network
分类号:
P208
DOI:
10.3969/j.issn.1672-1292.2017.03.009
文献标志码:
A
摘要:
机载LiDAR技术为地表三维数据的获取和DEM、DSM的构建提供了有利的条件. 由于建筑物和植被遮挡等原因,造成了点云的缺失,形成区域的空洞,给地表建模带来不便,需要对LiDAR点云数据进行插值处理以修复缺失的数据. 对径向基函数(RBF)神经网络构建插值模型进行了研究,利用该模型对点云中缺失的空洞区域进行修复. 通过利用一部分采样点对RBF神经网络进行学习训练,得到模型中参数的具体值,然后利用这些参数值对空洞区进行插值. 实验验证了RBF神经网络模型的有效性及插值精度.
Abstract:
Airborne LiDAR technology provides favorable conditions for the acquisition of 3D data and the construction of DEM,DSM. Such reasons as buildings and vegetation shelter result in the lack of point cloud and the formation of regional holes,which make the surface modeling inconvenient. LiDAR point cloud data Interpolation is needed to repair the missing data. The RBF neural network interpolation model is studied by using the model to repairempty area in the point cloud. A part of the sampling points is used to train RBF neural network to get the specific values of parameters in model,then these parameters are used to interpolate the empty area. Through experiments,the effectiveness and the interpolation precision of the RBF neural network model are verified.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2017-01-10.
基金项目:国家自然科学基金(41631175,41471102).
通讯联系人:杨林,博士,副教授,研究方向:数字摄影测量和考古GIS. E-mail:yangcius@126.com
更新日期/Last Update: 2017-09-30