Target extraction from LiDAR point cloud data using irregular geometry marked point process

被引：1
|
作者：
Zhao Q.-H. [1 ]
Zhang H.-Y. [1 ]
Li Y. [1 ]
机构：
[1] Institute for Remote Sensing Science and Application, School of Geomatics, Liaoning Technical University, Fuxin
来源：
Zhao, Quan-Hua (zqhlby@163.com) | 2018年 / Chinese Academy of Sciences卷 / 26期
关键词：
Bayesian inference; LiDAR point cloud data; Marked Point Process (MPP); Maximum A Posteriori (MAP); Reversible Jump Markov Chain Monte Carlo (RJMCMC);
D O I：
10.3788/OPE.20182605.1201
中图分类号：
学科分类号：
摘要：
In order to realize the arbitrary shape object extraction from LiDAR point cloud data, a method based on irregular marked point process was proposed. Firstly, a random point process was defined on ground plan, in which random point positioned the object projection on the plan. Then the marks associating individual points were defined with a set of nodes to depict the shape of object on the ground plan. Assumed that the elevation values of ground points followed an independent and identical Gauss distribution, and that of objects were also characterized by Gauss distributions individually. According to the Bayesian inference, the object extraction model was obtained; The RJMCMC algorithm was designed to simulate the posterior distribution and estimate the parameters. Finally, the optimal target extraction model was obtained according to the maximum a posteriori. LiDAR point cloud data was extracted by using the proposed method. According to the experimental results, it can be seen that the detection accuracy of the algorithm is above 80%, the highest accuracy is 99.43%. In this paper, the traditional rule mark process is extended to irregular marking process, and it can be used to fit the geometry of arbitrary shape target effectively. Experimental results show that this method can effectively fit the arbitrary shape objects. © 2018, Science Press. All right reserved.
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页码：1201 / 1210
页数：9
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