Correlation Between Surface Modeling and Pulse Width of FWF-Lidar

  • Fanar M. AbedEmail author
Conference paper
Part of the Advances in Science, Technology & Innovation book series (ASTI)


Surface modeling is the process of creating a 3D representation of any surface by manipulating polygons, vertices, and edges in three dimensions. The 3D model represents a physical body using a collection of 3D points in space and connected by several geometric entities. These processes mainly depend on the scenario used to generate the 3D points and the filtering methodology. Lidar is an active remote sensing technique, which has rapidly developed over the last decades to remotely determine the geometry of the Earth’s surface in a rapid and accurate way. However, the FWF-lidar system provides extra information for better 3D digital representation of the features and further improves the modelling for different applications. This paper discussed the correlation between FWF-lidar physical information and the potential to improve the quality of surface modeling. It also discusses the improvements in geometric point quality when integrating pulse width value in the filtering process based on a developed filtering scenario. In this scenario the pulse width value is used as an index to distinguish surface features and improve geometric filtering process. The scenario was tested and analyzed in vegetated and urban areas to show the improvements. The results show decreasing discrepancies between overlapping flight lines in terms of mean and STD values after integrating the pulse width values following Gaussian modeling.


Remote sensing Lidar FWF Filtering Pulse width 3D modeling 


  1. 1.
    Abed, F., Mills, J., Miller, P.: Calibrated full-waveform airborne laser scanning for 3D object segmentation. Remote Sens. 6(5), 4109–4132 (2014)CrossRefGoogle Scholar
  2. 2.
    Abed, F., Altaie, M., Kzar, M.: Fractal theory based pulse detection analysis of ALS data. The Egypt. J. Remote Sens. Space Sci. (Submitted) (2018)Google Scholar
  3. 3.
    Jutzi, B., Stilla, U.: Range determination with waveform recording laser systems using a Wiener filter. ISPRS J. Photogram. Remote Sens. 61(2), 95–107 (2006)CrossRefGoogle Scholar
  4. 4.
    Lin, Y.-C., Mills, J., Smith-Voysey, S.: Rigorous pulse detection from full-waveform airborne laser scanning data. Int. J. Remote Sens. 31(5), 1303–1324 (2010)CrossRefGoogle Scholar
  5. 5.
    Mallet, C., Bretar, F., Roux, M., Soergel, U., Heipke, C.: Relevance assessment of full-waveform Lidar data for urban area classification. ISPRS J. Photogram. Remote Sens. 66(6), S71–S84 (2011)CrossRefGoogle Scholar
  6. 6.
    Shanoer, M., Abed, F.: Evaluate 3D laser point clouds registration for cultural heritage documentation. The Egypt. J. Remote Sens. Space Sci. (2017). Scholar
  7. 7.
    Wagner, W., Ullrich, A., Ducic, V., Melzer, T., Studnicka, N.: Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner. ISPRS J. Photogram. Remote Sens. 60(2), 100–112 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of Baghdad, College of EngineeringBaghdadIraq

Personalised recommendations