Building feature extraction from airborne lidar data based on tensor voting algorithm

Rey-Jer You, Bo Cheng Lin

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

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 regiongrowing. 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.

Original languageEnglish
Number of pages1
JournalPhotogrammetric Engineering and Remote Sensing
Volume77
Issue number12
DOIs
Publication statusPublished - 2011 Jan 1

Fingerprint

Optical radar
lidar
Tensors
Feature extraction
Roofs
roof
Eigenvalues and eigenfunctions
eigenvalue
voting
method
Experiments
experiment

All Science Journal Classification (ASJC) codes

  • Computers in Earth Sciences

Cite this

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Building feature extraction from airborne lidar data based on tensor voting algorithm. / You, Rey-Jer; Lin, Bo Cheng.

In: Photogrammetric Engineering and Remote Sensing, Vol. 77, No. 12, 01.01.2011.

Research output: Contribution to journalArticle

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