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

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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.

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Acknowledgments

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.

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Authors and Affiliations

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

    Jun Xiao, Sidong Liu, Liang Hu & Ying Wang

Authors
  1. Jun Xiao
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  2. Sidong Liu
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  3. Liang Hu
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  4. Ying Wang
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Corresponding author

Correspondence to Jun Xiao.

Additional information

Jun Xiao is now an associate professor in University of Chinese Academy of Sciences, China. He obtained his PhD degree in the Graduate University of the Chinese Academy of Sciences, China in 2008. His research interests include computer graphics, computer vision, image processing and 3D reconstruction.

Sidong Liu received a Master’s degree in computer application technology from University of Chinese Academy of Sciences, China in 2016. He received his Bachelor’s degree in software engineering from Zhejiang University, China in 2011. His research interests include computer graphics, pattern recognition and point cloud processing.

Liang Hu is now a PhD candidate in computer application technology at University of Chinese Academy of Sciences, China. He received the Bachelor’s degree in software engineering from Xidian University, China in 2012. His research interests include computer graphic, point cloud processing and 3D reconstruction.

YingWang is now a professor in University of Chinese Academy of Sciences, China. She received her PhD degree in Beijing Institute of Technology, China in 1996. Her research interests include computer graphics, visualization and computer vision.

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Xiao, J., Liu, S., Hu, L. et al. Filtering method of rock points based on BP neural network and principal component analysis. Front. Comput. Sci. 12, 1149–1159 (2018). https://doi.org/10.1007/s11704-016-6170-6

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  • DOI: https://doi.org/10.1007/s11704-016-6170-6

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