Filtering method of rock points based on BP neural network and principal component analysis

doi:10.1007/s11704-016-6170-6

Front. Comput. Sci.

2018, Vol. 12

Issue (6) : 1149-1159 https://doi.org/10.1007/s11704-016-6170-6

RESEARCH ARTICLE

Filtering method of rock points based on BP neural network and principal component analysis

Jun XIAO(

), Sidong LIU, Liang HU, Ying WANG

School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China

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Abstract

Filtering is an essential step in the process of obtaining rock data. To the best of our knowledge, there are no special algorithms for use in the point clouds of rock masses. Existing filtering methods remove noisy points by fitting the surface of the ground and deleting the points above the surface around a range of values. This type of methods has certain limitations in rock engineering owing the uniqueness of the particular rockmass being studied. In this paper, a method for filtering the rock points is proposed based on a backpropagation (BP) neural network and principal component analysis (PCA). In the proposed method, a PCA is applied for feature extraction, and for obtaining the dimensional information, which can be used to effectively distinguish the rock and other points at different scales. A BP neural network, which has a strong nonlinear processing capability, is then used to obtain the exact points of rock with the above characteristics. In the present paper, the efficiency of the proposed technique is illustrated by classifying steep rocky slopes as rock and vegetation. A comparison with existing methods indicates the superiority of the proposed method in terms of the point cloud filtering of rock masses.

Keywords rock filter BP neural network principal component analysis

Corresponding Author(s): Jun XIAO

Just Accepted Date: 07 December 2016 Online First Date: 06 March 2018 Issue Date: 04 December 2018

Cite this article:

Jun XIAO,Sidong LIU,Liang HU, et al. Filtering method of rock points based on BP neural network and principal component analysis[J]. Front. Comput. Sci., 2018, 12(6): 1149-1159.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-016-6170-6
https://academic.hep.com.cn/fcs/EN/Y2018/V12/I6/1149

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