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

Rey Jer You, Bo Cheng Lin

研究成果: Article同行評審

11 引文 斯高帕斯(Scopus)


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.

期刊Photogrammetric Engineering and Remote Sensing
出版狀態Published - 2011 十二月

All Science Journal Classification (ASJC) codes

  • Computers in Earth Sciences

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