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Gaussian mixture model of ground filtering based on hierarchical curvature constraints for airborne lidar point clouds

Lookup NU author(s): Dr Wen Xiao


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© 2021 American Society for Photogrammetry and Remote Sensing.This paper proposes a Gaussian mixture model of a ground filtering method based on hierarchical curvature constraints. Firstly, the thin plate spline function is iteratively applied to interpolate the reference surface. Secondly, gradually changing grid size and curvature threshold are used to construct hierarchical constraints. Finally, an adaptive height difference classifier based on the Gaussian mixture model is proposed. Using the latent variables obtained by the expectation-maximization algorithm, the posterior probability of each point is computed. As a result, ground and objects can be marked separately according to the calculated possibility. 15 data samples provided by the International Society for Photogrammetry and Remote Sensing are used to verify the proposed method, which is also compared with eight classical filtering algorithms. Experimental results demonstrate that the average total errors and average Cohen’s kappa coefficient of the proposed method are 6.91% and 80.9%, respectively. In general, it has better performance in areas with terrain discontinuities and bridges.

Publication metadata

Author(s): Ye L, Zhang K, Xiao W, Sheng Y, Su D, Wang P, Zhang S, Zhao N, Chen H

Publication type: Article

Publication status: Published

Journal: Photogrammetric Engineering and Remote Sensing

Year: 2021

Volume: 87

Issue: 9

Pages: 615-630

Online publication date: 01/09/2021

Acceptance date: 02/04/2018

ISSN (print): 0099-1112

ISSN (electronic): 2374-8079

Publisher: American Society for Photogrammetry and Remote Sensing


DOI: 10.14358/PERS.87.20-00080


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