|Table of Contents|

Filling Method of Airborne LiDAR Point Cloud Hole Based onthe Radial Basis Function Neural Network(PDF)

南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2017年03期
Page:
57-
Research Field:
计算机工程
Publishing date:

Info

Title:
Filling Method of Airborne LiDAR Point Cloud Hole Based onthe Radial Basis Function Neural Network
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)
Keywords:
spatial interpolationLiDAR point cloudfilling holeRBFneural network
PACS:
P208
DOI:
10.3969/j.issn.1672-1292.2017.03.009
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.

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Last Update: 2017-09-30