Automating Parameter Learning for Classifying Terrestrial LiDAR Point Cloud Using 2D Land Cover Maps

Automating Parameter Learning for Classifying Terrestrial LiDAR Point Cloud Using 2D Land Cover Maps

The automating classification of point clouds capturing urban scenes is critical for supporting applications that demand three-dimensional (3D) models. Achieving this goal, however, is met with challenges because of the varying densities of the point clouds and the complexity of the 3D data. In orde...

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Bibliographic Details
Main Authors: Chen-Chieh Feng, Zhou Guo
Format: Article
Language:English
Published: MDPI AG 2018-07-01
Series:Remote Sensing
Subjects:
point cloud classification
2D map
segment-based
neighborhood scale
sample selection
Online Access:http://www.mdpi.com/2072-4292/10/8/1192

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http://www.mdpi.com/2072-4292/10/8/1192

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