Methods for LiDAR point cloud classification using local neighborhood statistics
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Authors
Kim, Angela M.
Olsen, Richard C.
Kruse, Fred A.
Subjects
LiDAR
point cloud
classification
statistics
local neighborhood
point cloud
classification
statistics
local neighborhood
Advisors
Date of Issue
2013
Date
Publisher
SPIE
Language
Abstract
LiDAR data are available in a variety of publicly-accessible forums, providing high-resolution, accurate 3-
dimensional information about objects at the Earth’s surface. Automatic extraction of information from LiDAR
point clouds, however, remains a challenging problem. The focus of this research is to develop methods for
point cloud classification and object detection which can be customized for specific applications. The methods
presented rely on analysis of statistics of local neighborhoods of LiDAR points. A multi-dimensional vector
composed of these statistics can be classified using traditional data classification routines. Local neighborhood
statistics are defined, and examples are given of the methods for specific applications such as building extraction
and vegetation classification. Results indicate the feasibility of the local neighborhood statistics approach
and provide a framework for the design of customized classification or object detection routines for LiDAR
point clouds.
Type
Article
Description
The article of record as published may be found at http://dx.doi.org/10.1117/12.2015709
Series/Report No
Department
Remote Sensing Center and Physics
Organization
Naval Postgraduate School
Identifiers
NPS Report Number
Sponsors
Funder
Format
10 p.
Citation
Proc. of SPIE Vol. 8731 873103-1, 10 p.
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.