Building Feature Extraction from Airborne Lidar Data Based on Tensor Voting Algorithm

This study presents a novel approach based on the tensor voting framework for extracting building features from airborne lidar data. Geometric features of lidar points are represented by a tensor field in this paper. For the extraction of roof patches, a region-growing method with principal features...

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Bibliographic Details
Published in:Photogrammetric engineering and remote sensing 2011-12, Vol.77 (12), p.1221-1231
Main Authors: You, Rey-Jer, Lin, Bo-Cheng
Format: Article
Language:English
Subjects:
Animal, plant and microbial ecology
Applied geophysics
Biological and medical sciences
Earth sciences
Earth, ocean, space
Eigenvalues
Exact sciences and technology
Extraction
Fundamental and applied biological sciences. Psychology
General aspects. Techniques
Internal geophysics
Lidar
Mathematical analysis
Roofs
Strength
Teledetection and vegetation maps
Tensors
Voting
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Summary:This study presents a novel approach based on the tensor voting framework for extracting building features from airborne lidar data. Geometric features of lidar points are represented by a tensor field in this paper. For the extraction of roof patches, a region-growing method with principal features is developed from the properties of eigenvalues and eigenvectors of the tensor field. Additionally, three new indicators for the strength of features are presented to reduce the effect of the number of points on feature identification, and a supervised method is proposed to determine the threshold of planar feature strength for the region-growing. The extraction of ridge and edge lines from the segmented roof patches is also discussed. Experiments based on airborne lidar data are described to demonstrate the effectiveness of the proposed method, with those the results compared with the PCA method.
ISSN:0099-1112
2374-8079
DOI:10.14358/PERS.77.12.1221