Spectral clustering of straight-line segments for roof plane extraction from airborne LiDAR point clouds

Zhang, C, He, Y and Simpson Fraser, C 2018, 'Spectral clustering of straight-line segments for roof plane extraction from airborne LiDAR point clouds', IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 2, pp. 267-271.


Document type: Journal Article
Collection: Journal Articles

Title Spectral clustering of straight-line segments for roof plane extraction from airborne LiDAR point clouds
Author(s) Zhang, C
He, Y
Simpson Fraser, C
Year 2018
Journal name IEEE Geoscience and Remote Sensing Letters
Volume number 15
Issue number 2
Start page 267
End page 271
Total pages 5
Publisher IEEE
Abstract This letter presents a novel approach to automated extraction of roof planes from airborne light detection and ranging data based on spectral clustering of straight-line segments. The straight-line segments are derived from laser scan lines, and 3-D line geometry analysis is employed to identify coplanar line segments so as to avoid skew lines in plane estimation. Spectral analysis reveals the spectrum of the adjacency matrix formed by the straight-line segments. Spectral clustering is then performed in feature space where the clusters are more prominent, resulting in a more robust extraction of roof planes. The proposed approach has been tested on ISPRS benchmark data sets, with the results showing high quality in terms of completeness, correctness, and geometrical accuracy, thus confirming that the proposed approach can extract roof planes both accurately and efficiently.
Subject Artificial Intelligence and Image Processing not elsewhere classified
Electrical and Electronic Engineering not elsewhere classified
Geomatic Engineering not elsewhere classified
Keyword(s) Light detection and ranging (LiDAR)
plane extraction
reconstruction
roof
spectral clustering
straight line
DOI - identifier 10.1109/LGRS.2017.2785380
Copyright notice © 2018 IEEE
ISSN 1545-598X
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