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Lookup NU author(s): Dr Wen Xiao
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
© 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.
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
Print publication date: 01/09/2023
Online publication date: 01/09/2021
Acceptance date: 02/04/2018
Date deposited: 02/11/2023
ISSN (print): 0099-1112
ISSN (electronic): 2374-8079
Publisher: American Society for Photogrammetry and Remote Sensing
URL: https://doi.org/10.14358/PERS.87.20-00080
DOI: 10.14358/PERS.87.20-00080
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