Tentative Tests on Two Rapid Multispectral Classifiers for Classifying Point Clouds

Conference paper (2017)

Authors

M. Zheng OLD Department of GIS Technology , Wuhan University
M.J.P.M. Lemmens OLD Department of GIS Technology
P.J.M. van Oosterom OLD Department of GIS Technology
Research Group
OLD Department of GIS Technology
Copyright: © 2017 M. Zheng, M.J.P.M. Lemmens, P.J.M. van Oosterom
Point clouds Classification Feature encoding
More Info
expand_more

Published Date

2017

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Research Group

OLD Department of GIS Technology

Abstract

This paper focusses on the feasibility of classifiers, developed for classifying multispectral images, for assigning classes to point clouds of urban scenes. The motivation of our research is that dense point clouds require fast classification methods to extract meaningful information within a reasonable amount of time and multispectral classifiers do have this property. We employ two encoding methods acting on one feature: the altitude above street level. We emphasize computation time and therefore we use just one feature in this prelimina