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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
  • 22 Downloads

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 

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Notes

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.

Supplementary material

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

References

  1. 1.
    Gigli G, Casagli N. Semi-automatic extraction of rock mass structural data from high resolution lidar point clouds. International Journal of Rock Mechanics and Mining Sciences, 2011, 48(2): 187–198CrossRefGoogle Scholar
  2. 2.
    Jaboyedoff M, Oppikofer T, Abellán A, Derron M H, Loye A. Use of LIDAR in landslide investigations: a review. Natural Hazards, 2012, 61(1): 5–28CrossRefGoogle Scholar
  3. 3.
    Heritage G L,Milan D J. Terrestrial Laser Scanning of grain roughness in a gravel-bed river. Geomorphology, 2009, 113(1): 4–11CrossRefGoogle Scholar
  4. 4.
    Zhang K Q, Chen S C, Whitman D, Yan J H, Zhang C C. A progressive morphological filter for removing nonground measurements from airborne LIDAR data. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(4): 872–882CrossRefGoogle Scholar
  5. 5.
    Vosselman G. Slope based filtering of laser altimetry data. International Archives of Photogrammetry and Remote Sensing, 2000, 33: 935–942Google Scholar
  6. 6.
    Akel N A, Zilberstein O, Doytsher Y. Automatic DTM extraction from dense raw LIDAR data in urban areas. In: Proceedings of International Federation of Surveyors Working Week. 2003Google Scholar
  7. 7.
    Axelsson P. DEM generation from laser scanner data using adaptive TIN models. International Archives of Photogrammetry and Remote Sensing, 2000, 33: 111–118Google Scholar
  8. 8.
    Pingel T J, Clarke K C,McbrideWA. An improved simple morphological filter for the terrain classification of airborne LIDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 77(1): 21–30CrossRefGoogle Scholar
  9. 9.
    Zhang J X, Lin X G. Filtering airborne LiDAR data by embedding smoothness-constrained segmentation in progressive TIN densification. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 81: 44–59CrossRefGoogle Scholar
  10. 10.
    Sui L, Zhang Y B, Zhang S, ChenW. Filtering of airborne LiDAR point cloud data based on progressive TIN. Geomatics and Information Science of Wuhan University, 2011, 36(10): 1159–1163Google Scholar
  11. 11.
    Zhang Y K, Zhang X P, Zha H B, Zhang J S. A survey of topologically structural representation and computation of 3D point cloud data. Journal of Image and Graphics, 2008, 13(8): 1576–1587Google Scholar
  12. 12.
    Sithole G, Mapurisa W T. 3D object segmentation of point clouds using profiling techniques. South African Journal of Geomatics, 2012, 1(1): 60–76Google Scholar
  13. 13.
    Shi R M, Qi X L. Research on mixed indexing model for cloud points. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium. 2012, 5301–5303Google Scholar
  14. 14.
    Ma H C, Wang Z Y. Distributed data organization and parallel data retrieval methods for huge laser scanner point clouds. Computers and Geosciences, 2011, 37(2): 193–201CrossRefGoogle Scholar
  15. 15.
    Gong J, Zhu Q, Zhong R F, Zhang Y T, Xie X. An efficient point cloud management method based on a 3D R-tree. Photogrammetric Engineering and Remote Sensing, 2012, 78(4): 373–381CrossRefGoogle Scholar
  16. 16.
    Lichti D D. Spectral filtering and classification of terrestrial laser scanner point clouds. The Photogrammetric Record, 2005, 20(111): 218–240CrossRefGoogle Scholar
  17. 17.
    Franceschi M, Teza G, Preto N, Pesci A, Galgaro A. Discrimination between marls and limestones using intensity data from terrestrial laser scanner. ISPRS Journal of Photogrammetry and Remote Sensing, 2009, 64(6): 522–528CrossRefGoogle Scholar
  18. 18.
    Kaasalainen S, Jaakkola A, Kaasalainen M, Krooks A, Kukko A. Analysis of incidence angle and distance effects on terrestrial laser scanner intensity: search for correction methods. Remote Sensing, 2011, 3(10): 2207–2221CrossRefGoogle Scholar
  19. 19.
    Vandapel N, Huber D F, Kapuria A, Hebert M. Natural terrain classification using 3-D ladar data. In: Proceedings of IEEE International Conference on Robotics and Automation. 2004, 5117–5122Google Scholar
  20. 20.
    Lalonde J F, Vandapel N, Huber D F, Hebert M. Natural terrain classification using three-dimensional ladar data for ground robot mobility. Journal of Field Robotics, 2006, 23(10): 839–861CrossRefGoogle Scholar
  21. 21.
    Brodu N, Lague D. 3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: applications in geomorphology. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 68(1): 121–134CrossRefGoogle Scholar
  22. 22.
    Burrough P A. Fractal dimensions of landscapes and other environmental data. Nature, 1981, 294(5838): 240–242CrossRefGoogle Scholar
  23. 23.
    Wendt H, Abry P, Jaffard S. Bootstrap for empirical multifractal analysis. IEEE Signal Processing Magazine, 2007, 24(4): 38–48CrossRefGoogle Scholar
  24. 24.
    Peng X, Lu J W, Zhang Y, Yan R. Automatic subspace learning via principal coefficients embedding. IEEE Transactions on Cybernetics, 2016, 47(11): 3583–3596CrossRefGoogle Scholar
  25. 25.
    Peng X, Zhang L, Zhang Y, Tan K K. Learning locality-constrained collaborative representation for robust face recognition. Pattern Recognition, 2013, 47(9): 2794–2806CrossRefGoogle Scholar
  26. 26.
    Ding Y Q, Fu Y M, Zhu F, Zan X. Comparison of missing data filling methods in bridge health monitoring system. In: Proceedings of IEEE International Conference on Cognitive Informatics and Cognitive Computing. 2013: 442–445Google Scholar
  27. 27.
    Moavenian M, Khorrami H. A qualitative comparison of Artificial Neural Networks and Support Vector Machines in ECG arrhythmias classification. Expert Systems with Applications, 2010, 37(4): 3088–3093CrossRefGoogle Scholar
  28. 28.
    Zhong H M, Miao C Y, Shen Z Q, Feng Y H. Comparing the learning effectiveness of BP, ELM, I-ELM, and SVM for corporate credit ratings. Neurocomputing, 2014, 128(5): 285–295CrossRefGoogle Scholar
  29. 29.
    Lato M, Kemeny J, Harrap R M, Bevan G. Rock bench: establishing a common repository and standards for assessing rockmass characteristics using LiDAR and photogrammetry. Computers and Feosciences, 2013, 50(1): 106–114CrossRefGoogle Scholar

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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