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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ackermann, F.E., Kraus, K.: Grid based digital terrain models. na (2004)
Axelsson, P.: Dem generation from laser scanner data using adaptive tin models. Int. Arch. Photogrammetry Remote Sens. 33(4), 110–117 (2000)
El-Sheimy, N., Valeo, C., Habib, A.: Digital Terrain Modeling: Acquisition, Manipulation and Applications (Artech House Remote Sensing Library). Artech House, Norwood (2005)
Gimp: Normal map plugin (2019). https://code.google.com/archive/p/gimp-normalmap/
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)
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)
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)
Mongus, D., Žalik, B.: Parameter-free ground filtering of lidar data forautomatic DTM generation. ISPRS J. Photogrammetry Remote Sens. 67, 1–12 (2012)
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)
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)
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)
Pfeifer, N.: A subdivision algorithm for smooth 3D terrain models. ISPRS J. Photogrammetry Remote Sens. 59(3), 115–127 (2005)
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)
Shan, J., Toth, C.K.: Topographic Laser Ranging and Scanning: Principles and Processing. CRC Press, Boca Raton (2018)
Tavakkoli, A.: Novelty detection: an approach to foreground detection in videos. In: Pattern Recognition. IntechOpen (2009)
Tavakkoli, A.: Game Development and Simulation with Unreal Technology, 2nd edn. AK Peters/CRC Press, Boca Raton (2018)
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_44
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Easson, L., Tavakkoli, A., Greenberg, J. (2019). An Automatic Digital Terrain Generation Technique for Terrestrial Sensing and Virtual Reality Applications. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11844. Springer, Cham. https://doi.org/10.1007/978-3-030-33720-9_48
Download citation
DOI: https://doi.org/10.1007/978-3-030-33720-9_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33719-3
Online ISBN: 978-3-030-33720-9
eBook Packages: Computer ScienceComputer Science (R0)