21 September 2015 Using geometry tessellation and Markov chain Monte Carlo for segmentation of LiDAR point cloud data
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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
© 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)
Quanhua Zhao, Quanhua Zhao, Xuemei Zhao, Xuemei Zhao, Yu Li, Yu Li, Abdur Raziq, Abdur Raziq, } "Using geometry tessellation and Markov chain Monte Carlo for segmentation of LiDAR point cloud data," Journal of Applied Remote Sensing 9(1), 095052 (21 September 2015). https://doi.org/10.1117/1.JRS.9.095052 . Submission:
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