Frontiers of Computer Science

, Volume 12, Issue 6, pp 1149–1159 | Cite as

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

  • Jun XiaoEmail author
  • Sidong Liu
  • Liang Hu
  • Ying Wang
Research Article


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.


rock filter BP neural network principal component analysis 


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This work was supported by the National Natural Science Foundation of China (Grant No. 61471338), Youth Innovation Promotion Association CAS (2015361), Key Research Program of Frontier Sciences, CAS (QYZDY-SSW-SYS004), Beijing Nova program (Z171100001117048), and President Fund of UCAS.

Supplementary material

11704_2016_6170_MOESM1_ESM.ppt (9.1 mb)
Supplementary material, approximately 9.14 MB.


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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina

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