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Journal of Applied Remote Sensing

Using geometry tessellation and Markov chain Monte Carlo for segmentation of LiDAR point cloud data
Author(s): Quanhua Zhao; Xuemei Zhao; Yu Li; Abdur Raziq
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Paper Abstract

This paper presents a segmentation algorithm of LiDAR point cloud data by which geometric and distributional features of objects are extracted. In this proposed algorithm, each object is considered to occupy a statistically homogeneous region and its acquired elevations are modeled as a normal distribution. To segment the LiDAR point cloud into homogeneous regions, a Voronoi tessellation is first used to partition its domain into polygons. The number of polygons is given in practice. Each of the polygons is assigned a random label variable to indicate the region to which it belongs. By Bayesian inference, the joint probability of labels and distribution parameters conditional on the given dataset can be obtained up to a normalizing constant. A Markov chain Monte Carlo scheme is designed to simulate from the posterior and to estimate the model parameters. Finally, the optimal segmentation is obtained under maximum <italic<a posteriori</italic< estimation. Experiments on real point cloud data show that normal distribution parameters for each region quickly converge to their stable values, and the optimal segmentation results can be obtained within 20,000 iterations for all datasets. Experiments on simulated point cloud data demonstrate that the proposed algorithm can accurately estimate the model parameters.

Paper Details

Date Published: 21 September 2015
PDF: 15 pages
J. Appl. Remote Sens. 9(1) 095052 doi: 10.1117/1.JRS.9.095052
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Quanhua Zhao, Liaoning Technical Univ. (China)
Xuemei Zhao, Liaoning Technical Univ. (China)
Yu Li, Liaoning Technical Univ. (China)
Abdur Raziq, Liaoning Technical Univ. (China)

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