Image and Signal Processing Methods

Using geometry tessellation and Markov chain Monte Carlo for segmentation of LiDAR point cloud data

[+] Author Affiliations
Quanhua Zhao, Xuemei Zhao, Yu Li, Abdur Raziq

Liaoning Technical University, Institute for Remote Sensing Science and Application, School of Geomatics, Fuxin, Liaoning 123000, China

J. Appl. Remote Sens. 9(1), 095052 (Sep 21, 2015). doi:10.1117/1.JRS.9.095052
History: Received May 4, 2015; Accepted August 19, 2015
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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 a posteriori 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.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Quanhua Zhao ; Xuemei Zhao ; Yu Li and Abdur Raziq
"Using geometry tessellation and Markov chain Monte Carlo for segmentation of LiDAR point cloud data", J. Appl. Remote Sens. 9(1), 095052 (Sep 21, 2015). ; http://dx.doi.org/10.1117/1.JRS.9.095052


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