Advertisement

An Automatic Digital Terrain Generation Technique for Terrestrial Sensing and Virtual Reality Applications

  • Lee Easson
  • Alireza TavakkoliEmail author
  • Jonathan Greenberg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11844)

Abstract

The identification and modeling of the terrain from point cloud data is an important component of Terrestrial Remote Sensing (TRS) applications. The main focus in terrain modeling is capturing details of complex geological features of landforms. Traditional terrain modeling approaches rely on the user to exert control over terrain features. However, relying on the user input to manually develop the digital terrain becomes intractable when considering the amount of data generated by new remote sensing systems capable of producing massive aerial and ground-based point clouds from scanned environments. This article provides a novel terrain modeling technique capable of automatically generating accurate and physically realistic Digital Terrain Models (DTM) from a variety of point cloud data. The proposed method runs efficiently on large-scale point cloud data with real-time performance over large segments of terrestrial landforms. Moreover, generated digital models are designed to effectively render within a Virtual Reality (VR) environment in real time. The paper concludes with an in–depth discussion of possible research directions and outstanding technical and scientific challenges to improve the proposed approach.

Keywords

Digital Terrain Model Terrestrial Remote Sensing Geological Landmass Modeling 

References

  1. 1.
    Ackermann, F.E., Kraus, K.: Grid based digital terrain models. na (2004)Google Scholar
  2. 2.
    Axelsson, P.: Dem generation from laser scanner data using adaptive tin models. Int. Arch. Photogrammetry Remote Sens. 33(4), 110–117 (2000)Google Scholar
  3. 3.
    El-Sheimy, N., Valeo, C., Habib, A.: Digital Terrain Modeling: Acquisition, Manipulation and Applications (Artech House Remote Sensing Library). Artech House, Norwood (2005)Google Scholar
  4. 4.
  5. 5.
    Kobler, A., Pfeifer, N., Ogrinc, P., Todorovski, L., Oštir, K., Džeroski, S.: Repetitive interpolation: a robust algorithm for DTM generation from aerial laser scanner data in forested terrain. Remote Sens. Environ. 108(1), 9–23 (2007)CrossRefGoogle Scholar
  6. 6.
    Li, Y., Yong, B., Wu, H., An, R., Xu, H.: An improved top-hat filter with sloped brim for extracting ground points from airborne lidar point clouds. Remote Sens. 6(12), 12885–12908 (2014)CrossRefGoogle Scholar
  7. 7.
    Mongus, D., Lukač, N., Žalik, B.: Ground and building extraction from lidar data based on differential morphological profiles and locally fitted surfaces. ISPRS J. Photogrammetry Remote Sens. 93, 145–156 (2014)CrossRefGoogle Scholar
  8. 8.
    Mongus, D., Žalik, B.: Parameter-free ground filtering of lidar data forautomatic DTM generation. ISPRS J. Photogrammetry Remote Sens. 67, 1–12 (2012)CrossRefGoogle Scholar
  9. 9.
    Mongus, D., Žalik, B.: Computationally efficient method for the generation of a digital terrain model from airborne lidar data using connected operators. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 7(1), 340–351 (2013)CrossRefGoogle Scholar
  10. 10.
    Næsset, E.: Vertical height errors in digital terrain models derived from airborne laser scanner data in a boreal-alpine ecotone in Norway. Remote Sens. 7(4), 4702–4725 (2015)CrossRefGoogle Scholar
  11. 11.
    Özcan, A.H., Ünsalan, C.: Lidar data filtering and dtm generation using empirical mode decomposition. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 10(1), 360–371 (2016)CrossRefGoogle Scholar
  12. 12.
    Pfeifer, N.: A subdivision algorithm for smooth 3D terrain models. ISPRS J. Photogrammetry Remote Sens. 59(3), 115–127 (2005)CrossRefGoogle Scholar
  13. 13.
    Pingel, T.J., Clarke, K.C., McBride, W.A.: An improved simple morphological filter for the terrain classification of airborne lidar data. ISPRS J. Photogrammetry Remote Sens. 77, 21–30 (2013)CrossRefGoogle Scholar
  14. 14.
    Shan, J., Toth, C.K.: Topographic Laser Ranging and Scanning: Principles and Processing. CRC Press, Boca Raton (2018)CrossRefGoogle Scholar
  15. 15.
    Tavakkoli, A.: Novelty detection: an approach to foreground detection in videos. In: Pattern Recognition. IntechOpen (2009)Google Scholar
  16. 16.
    Tavakkoli, A.: Game Development and Simulation with Unreal Technology, 2nd edn. AK Peters/CRC Press, Boca Raton (2018)CrossRefGoogle Scholar
  17. 17.
    Tavakkoli, A., Nicolescu, M., Bebis, G.: Automatic robust background modeling using multivariate non-parametric Kernel density estimation for visual surveillance. In: Bebis, G., Boyle, R., Koracin, D., Parvin, B. (eds.) ISVC 2005. LNCS, vol. 3804, pp. 363–370. Springer, Heidelberg (2005).  https://doi.org/10.1007/11595755_44CrossRefGoogle Scholar
  18. 18.
    Van Sinh, N., Ha, T.M., Thanh, N.T.: Filling holes on the surface of 3D point clouds based on tangent plane of hole boundary points. In: Proceedings of the 7th Symposium on Information and Communication Technology, pp. 331–338 (2016)Google Scholar
  19. 19.
    Zhang, J., Lin, X.: Filtering airborne lidar data by embedding smoothness-constrained segmentation in progressive tin densification. ISPRS J. Photogrammetry Remote Sens. 81, 44–59 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lee Easson
    • 1
  • Alireza Tavakkoli
    • 1
    Email author
  • Jonathan Greenberg
    • 1
  1. 1.University of NevadaRenoUSA

Personalised recommendations