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A mathematical morphology-based multi-level filter of LiDAR data for generating DTMs

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Abstract

Light detecting and ranging (LiDAR) technology has become an effective way to generate high-resolution digital terrain models (DTMs). To generate DTMs, point measurements from non-ground features, such as buildings, vegetation and vehicles, have to be identified and removed while preserving the terrain points. This paper proposes an efficient mathematical morphology-based multi-level filter to generate DTMs from airborne LiDAR data. Preliminary non-ground points are first identified with the characteristics of the multi-echo airborne LiDAR data. The localized mathematical morphology opening operations are then immediately applied to the remaining points. By gradually increasing the window size of the filter and using a dynamic critical gradient threshold, the non-ground points are removed, while the ground points are preserved. Eight samples were chosen from eight sites provided with the ISPRS Commission III, Working Group 3, to evaluate the accuracy of our algorithm. Both the qualitative and quantitative experiment analyses show that our morphology-based multi-level filter method achieves promising results, not only in flat urban areas but also in rural areas, especially in preserving complex terrain details, while non-ground spatial objects are removed.

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References

  1. Vosselman G. Analysis of planimetric accuracy of airborne laser scanning surveys. Int Archiv Photogramm Rem Sens Spat Inf Sci, 2008, 37: 99–104

    Google Scholar 

  2. Höfle B, Hollaus M, Hagenauer J. Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data. ISPRS J Photogramm Rem Sens, 2012, 67: 134–147

    Article  Google Scholar 

  3. Sithole G, Vosselman G. Experimental comparison of filter algorithms for bare earth extraction from airborne laser scanning point clouds. ISPRS J Photogramm Rem Sens, 2004, 59: 85–101

    Article  Google Scholar 

  4. Kilian J, Haala N, Englich M. Capture and evaluation of airborne laser scanner data. Int Archiv Photogramm Rem Sens Spat Inf Sci, 1996, 31: 383–388

    Google Scholar 

  5. Zhang K Q, Chen S C, Whitman D, et al. A progressive morphological filter for removing nonground measurements from airborne LiDAR data. IEEE Trans Geosci Rem Sens, 2003, 41: 872–882

    Article  Google Scholar 

  6. Silván-Cárdenas J L, Wang L. A multi-resolution approach for filtering LiDAR altimetry data. ISPRS J Photogramm Rem Sens, 2006, 61: 11–22

    Article  Google Scholar 

  7. Kobler A, Pfeifer N, Ogrinc P, et al. Repetitive interpolation: A robust algorithm for DTM generation from aerial laser scanner data in forested terrain. Rem Sens Envir, 2007, 108: 9–23

    Article  Google Scholar 

  8. Kraus K, Pfeifer N. Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS J Photogramm Rem Sens, 1998, 53: 193–203

    Article  Google Scholar 

  9. Vosselman G. Slope based filtering of laser altimetry data. Int Archiv Photogramm Rem Sens Spat Inf Sci, 2000, 33: 935–942

    Google Scholar 

  10. Roggero M. Airborne laser scanning: clustering in raw data. Int Archiv Photogramm Rem Sens Spat Inf Sci, 2001, 34: 227–232

    Google Scholar 

  11. Sithole G. Filtering of laser altimetry data using a slope adaptive filter. Int Archiv Photogramm Rem Sens Spat Inf Sci, 2001, 34: 203–210

    Google Scholar 

  12. Shan J, Sampath A. Urban DEM generation from raw LiDAR data: A labeling algorithm and its performance. Photogramm Engin Rem Sens, 2005, 71: 217–226

    Article  Google Scholar 

  13. Zhang K Q, Whitman D. Comparison of three algorithms for filtering airborne LiDAR data. Photogramm Engin Rem Sens, 2005, 71: 313–324

    Article  Google Scholar 

  14. Chen Q, Gong P, Baldocchi D, et al. Filtering airborne laser scanning data with morphological methods. Photogramm Engin Rem Sens, 2007, 73: 175–185

    Article  Google Scholar 

  15. Chen Q. Improvement of the edge-based morphological (EM) method for LiDAR data filtering. Int J Rem Sens, 2009, 30: 1069–1074

    Article  Google Scholar 

  16. Pfeifer N, Reiter T, Briese C, et al. Interpolation of high quality ground models from laser scanner data in forested areas. Int Archiv Photogramm Rem Sens Spat Inf Sci, 1999, 32: 31–36

    Google Scholar 

  17. Pfeifer N, Stadler P, Briese C. Derivation of digital terrain models in the SCOP++ environment. In: Proceedings of OEEPE Workshop on Airborne Laser scanning and Interferometric SAR for Digital Elevation Models, Stockholm, 2001

  18. Lee H S, Younan N H. DTM extraction of LiDAR returns via adaptive processing. IEEE Trans Geosci Rem Sens, 2003, 41: 2063–2069

    Article  Google Scholar 

  19. Kraus K. A new method for surface reconstruction from laser scanner data. Int Archiv Photogramm Rem Sens Spat Inf Sci, 1997, 32: 80–86

    Google Scholar 

  20. Pfeifer N, Kostli A, Kraus K. Interpolation and filtering of laser scanner data-implementation and first results. Int Archiv Photogramm Rem Sens Spat Inf Sci, 1998, 32: 153–159

    Google Scholar 

  21. Brandtberg T, Warner T A, Landenberger R E, et al. Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density LiDAR data from the eastern deciduous forest in North America. Rem Sens Envir, 2003, 85: 290–303

    Article  Google Scholar 

  22. Lohmann P, Koch A, Schaeffer M. Approaches to the filtering of laser scanner data. Int Archiv Photogramm Rem Sens Spat Inf Sci, 2000, 33: 540–547

    Google Scholar 

  23. Haralick R M, Shapiro L G. Computer and Robot Vision. New York: Addison-Wesley, 1992

    Google Scholar 

  24. Petzold B, Axelsson P. Results of the OEEPE Working Group on laser data acquisition. Int Archiv Photogramm Rem Sens, 2000, 33: 718–741

    Google Scholar 

  25. Chen D, Zhang L Q, Li J. Urban building roof segmentation from airborne lidar point clouds. Int J Rem Sens, 33: 6497–6515

  26. Yang L, Zhang L Q, Kang Z Z, et al. An efficient rendering method for large vector data on large terrain models. Sci China Inf Sci, 2010, 53: 1122–1129

    Article  Google Scholar 

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Authors and Affiliations

  1. State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing, 100875, China

    Dong Chen, LiQiang Zhang, Zhen Wang & Hao Deng

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  1. Dong Chen
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  2. LiQiang Zhang
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  3. Zhen Wang
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  4. Hao Deng
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Correspondence to LiQiang Zhang.

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Chen, D., Zhang, L., Wang, Z. et al. A mathematical morphology-based multi-level filter of LiDAR data for generating DTMs. Sci. China Inf. Sci. 56, 1–14 (2013). https://doi.org/10.1007/s11432-012-4707-3

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  • DOI: https://doi.org/10.1007/s11432-012-4707-3

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