Multimedia Tools and Applications

, Volume 76, Issue 8, pp 11051–11063 | Cite as

Filtering LiDAR data based on adjacent triangle of triangulated irregular network

  • Yining Quan
  • Jianfeng Song
  • Xue Guo
  • Qiguang Miao
  • Yun Yang
Article
  • 150 Downloads

Abstract

The filtering of LiDAR points cloud data is a fundamental procedure in the production of Digital Elevation Model. Against the lack of using the relationship between the adjacent terrain and the points to be judged in the point cloud filtering, a LiDAR points cloud data filtering algorithm based on adjacent triangles in TIN (Triangulated Irregular Network) is proposed. It utilizes the elevation information of each triangle’s adjacent triangles to detect the building edge points, and acquires the building points by region growing, then detects the isolated points with the morphological filtering algorithm, finally determines the ground point set and generates DEM. We evaluate the performance of the proposed method on the ISPRS LiDAR reference dataset. Experimental results show that the algorithm can effectively remove non-ground points, keep the ground points and minimize total error rates effectively while maintaining acceptable Type I and Type II error rates.

Keywords

LiDAR data TIN Adjacent triangle Region growing 

Notes

Acknowledgments

The work was jointly supported by the National Natural Science Foundations of China under grant No. 61472302,61272280,U1404620,and 41271447; The Program for New Century Excellent Talents in University under grant No. NCET-12-0919; The Fundamental Research Funds for the Central Universities under grant No. K5051203020, K5051303018, JB150313,JB150317,and BDY081422,; Natural Science Foundation of Shaanxi Province, under grant No.2014JM8310 and, 2010JM8027; The Creative Project of the Science and Technology State of xi’an under grant No. CXY1441(1); The State Key Laboratory of Geo-information Engineering under grant No.SKLGIE2014-M-4-4.

References

  1. 1.
    Axelsson P (2000) DEM generation from laser scanner data using adaptive TIN models[J]. Int Arch Photogramm Remote Sens Spat Inf Sci 33(Part B4):110–117Google Scholar
  2. 2.
    Chen Q, Gong P, Baldocchi DD, Xie G (2007) Filtering airborne laser scanning data with morphological methods [J]. Photogramm Eng Remote Sens 73(2):175–185CrossRefGoogle Scholar
  3. 3.
    Feng Y, Jixian Z, et al (2009) Urban DEM generation from airborne Lidar data[C]. Urban Remote Sensing Event, 2009 Joint. IEEE, pp 1–5Google Scholar
  4. 4.
    Gong M, Liu J, Li H, Cai Q, Linzhi S (2015) A multiobjective sparse feature learning model for deep neural networks. IEEE Trans Neural Netw Learn Syst 26(12):3263–3277MathSciNetCrossRefGoogle Scholar
  5. 5.
    Han W, Li Y, Chen L (2012) High-precision DEM production in complex urban area using LiDAR data[C]. 2012 20th International Conference on Geoinformatics. IEEE, pp 1–5Google Scholar
  6. 6.
    Haugerud R, Harding DJ (2001) Some algorithms for virtual deforestation(VDF)of LIDAR topographic survey data[J]. Int Arch Photogramm Remote Senning Spat Inf Sci 34(W4):211–218Google Scholar
  7. 7.
    Kilian J, Haala N, Englich M (1996) Capture and evaluation of airborne laser scanner data. Int Arch photogramm Remote Sens Spat Inf Sci 31(Part B3):383–388Google Scholar
  8. 8.
    Kraus K, Pfeifer N (1998) Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS J Photogramm Remote Sens 53(4):193–203CrossRefGoogle Scholar
  9. 9.
    Liang C, Zhao W, Han P et al (2013) Building region derivation from LiDAR data using a reversed iterative mathematic morphological algorithm [J]. Opt Commun 286:244–250CrossRefGoogle Scholar
  10. 10.
    Meng X, Currit N, Zhao K (2010) Ground filtering algorithms for airborne LiDAR data: a review of critical issues[J]. Remote Sens 2(3):833–860CrossRefGoogle Scholar
  11. 11.
    Pingel TJ, Clarke KC, McBride WA (2013) An improved simple morphological filter for the terrain classification of airborne LIDAR data[J]. ISPRS J Photogramm Remote Sens 77:21–30CrossRefGoogle Scholar
  12. 12.
    Shao L, Hu P, Huang C (2004) The expatiation of DELAUNAY algorithm and a promising direction in application[J]. Sci Surv Mapp 29(6):68–71 (in Chinese)Google Scholar
  13. 13.
    Sithole G (2005) Segmentation and classification of airborne laser scanner data[D]. International Institute for Geo-information Science and Earth Observation (ITC) the degree of Master, NetherlandsGoogle Scholar
  14. 14.
    Sithole G, Vosselman G (2004) Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds[J]. ISPRS J Photogramm Remote Sens 59(2):85–101CrossRefGoogle Scholar
  15. 15.
    Vosselman G (2000) Slope based filtering of laser altimetry data[J]. Int Arch Photogramm Remote Sens Spat Inf Sci 33(Part B3):935–942Google Scholar
  16. 16.
    Wang H, Zhang Y, Li P, Zha X (2013) A method of deriving dem from airborne lidar points cloud data[C].Urban Remonte Sensing Event (JURSE), Joint, pp, 013–016Google Scholar
  17. 17.
    Wu C, Lu X, Li G et al (2013) Research on filtering algorithm for LiDAR data based on TIN[J]. Bull Surv Mapp 3:32–35 (in Chinese)Google Scholar
  18. 18.
    Yu H, Lu X et al (2010) Digital terrain model extraction from airborne LiDARdata in complex mining area[C]. 2010 18th International Conference on Geoinformatics. IEEE, pp 1–6Google Scholar
  19. 19.
    Zhang K, Chen S et al (2003) A progressive morphological filter for removing nonground measurements from airborne LIDAR data[J]. IEEE Trans Geosci Remote Sens 41(4):872–882CrossRefGoogle Scholar
  20. 20.
    Zhang J, Lin X (2013) Filtering airborne LiDAR data by embedding smoothness-constrained segmentation in progressive TIN densification[J]. ISPRS J Photogramm Remote Sens 81:44–59CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Yining Quan
    • 1
  • Jianfeng Song
    • 1
  • Xue Guo
    • 1
  • Qiguang Miao
    • 1
  • Yun Yang
    • 2
    • 3
  1. 1.School of Computer Science and TechnologyXidian UniversityShaanxiChina
  2. 2.Xi’an Research Institute of Surveying and MappingXi’anChina
  3. 3.State Key Laboratory of Geo-Information EngineeringXi’anChina

Personalised recommendations