Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81226
Title: A multi-primitive-based hierarchical optimal approach for semantic labeling of ALS point clouds
Authors: Ge, X 
Wu, B 
Li, Y 
Hu, H 
Keywords: ALS point clouds
Classification
Labeling
Multiple primitives
Issue Date: 2019
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Remote sensing, 2019, v. 11, no. 10, 1243 How to cite?
Journal: Remote sensing 
Abstract: There are normally three main steps to carrying out the labeling of airborne laser scanning (ALS) point clouds. The first step is to use appropriate primitives to represent the scanning scenes, the second is to calculate the discriminative features of each primitive, and the third is to introduce a classifier to label the point clouds. This paper investigates multiple primitives to effectively represent scenes and exploit their geometric relationships. Relationships are graded according to the properties of related primitives. Then, based on initial labeling results, a novel, hierarchical, and optimal strategy is developed to optimize semantic labeling results. The proposed approach was tested using two sets of representative ALS point clouds, namely the Vaihingen datasets and Hong Kong's Central District dataset. The results were compared with those generated by other typical methods in previous work. Quantitative assessments for the two experimental datasets showed that the performance of the proposed approach was superior to reference methods in both datasets. The scores for correctness attained over 98% in all cases of the Vaihingen datasets and up to 96% in the Hong Kong dataset. The results reveal that our approach of labeling different classes in terms of ALS point clouds is robust and bears significance for future applications, such as 3D modeling and change detection from point clouds.
URI: http://hdl.handle.net/10397/81226
EISSN: 2072-4292
DOI: 10.3390/rs11101243
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The following publication Ge X, Wu B, Li Y, Hu H. A Multi-Primitive-Based Hierarchical Optimal Approach for Semantic Labeling of ALS Point Clouds. Remote Sensing. 2019; 11(10):1243 is available at https://doi.org/10.3390/rs11101243
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Ge_multi-primitive-based_hierarchical_optimal.pdf3.97 MBAdobe PDFView/Open
Access
View full-text via PolyU eLinks SFX Query
Show full item record
PIRA download icon_1.1View/Download Contents

Page view(s)

10
Citations as of Oct 22, 2019

Download(s)

6
Citations as of Oct 22, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.