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Improvement Scheme of Airborne LiDAR Strip Adjustment

  • Lee, Dae Geon (Department of Geoinformation Engineering, Sejong University) ;
  • Lee, Dong-Cheon (Department of Geoinformation Engineering, Sejong University)
  • Received : 2018.09.20
  • Accepted : 2018.10.24
  • Published : 2018.10.31

Abstract

LiDAR (Light Detection And Ranging) strip adjustment is process to improve geo-referencing of the ALS (Airborne Laser Scanner) strips that leads to seamless LiDAR data. Multiple strips are required to collect data over the large areas, thus the strips are overlapped in order to ensure data continuity. The LSA (LiDAR Strip Adjustment) consists of identifying corresponding features and minimizing discrepancies in the overlapping strips. The corresponding features are utilized as control features to estimate transformation parameters. This paper applied SURF (Speeded Up Robust Feature) to identify corresponding features. To improve determination of the corresponding feature, false matching points were removed by applying three schemes: (1) minimizing distance of the SURF feature vectors, (2) selecting reliable matching feature with high cross-correlation, and (3) reflecting geometric characteristics of the matching pattern. In the strip adjustment procedure, corresponding points having large residuals were removed iteratively that could achieve improvement of accuracy of the LSA eventually. Only a few iterations were required to reach reasonably high accuracy. The experiments with simulated and real data show that the proposed method is practical and effective to airborne LSA. At least 80 % accuracy improvement was achieved in terms of RMSE (Root Mean Square Error) after applying the proposed schemes.

Keywords

References

  1. Bang, K., Habib, A., and Kersting, A. (2010), Estimation of biases in LiDAR system calibration parameters using overlapping strips, Canadian Journal of Remote Sensing , Vol. 36, No. 2, pp. 335-354.
  2. Bay, H., Ess, A., Tuytelaars, T., and Van Gool, A. (2008), Speeded-Up Robust Features (SURF), Computer Vision and Image Understanding , Vol. 110, No. 3, pp. 346-359. https://doi.org/10.1016/j.cviu.2007.09.014
  3. Besl, P. and McKay, N. (1992), A method for registration of 3D shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence , Vol. 14, No. 2, pp. 239-256. https://doi.org/10.1109/34.121791
  4. Filin, S. (2003), Analysis and implementation of a laser strip adjustment model, International Archives of Photogrammetry and Remote Sensing , Vol. 34, Part3/W13, pp. 65-70.
  5. Filin, S. and Vosselman, G. (2004), Adjustment of airborne laser altimetry strips, International Archives of Photogrammetry and Remote Sensing , Vol. 34, Part3/W13, pp. 285-289.
  6. Glira, P., Pfeifer, N., Briese, C., and Ressl, C. (2015), Rigorous strip adjustment of airborne laser scanning data based on the ICP algorithm, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 28 September-3 October, La Grande Motte, France, Vol. II-3, Part. W5, pp. 73-80.
  7. Glira, P., Pfeifer, N., and Mandlburger, G. (2016), Rigorous strip adjustment of UAV-based laser scanning data including time-dependent correction of trajectory errors, Photogrammetric Engineering and Remote Sensing , Vol. 21, No. 12, pp. 945-954.
  8. Gruen, A. and Akca, D. (2005), Least squares 3D surface matching, ISPRS Journal of Photogrammetry and Remote Sensing , Vol. 59, No. 3, pp. 151-174. https://doi.org/10.1016/j.isprsjprs.2005.02.006
  9. Habib, A., Bang, K., Kersting, A., and Chow, J. (2010), Alternative methodologies for LiDAR system calibration, Remote Sensing , Vol. 2, No. 3, pp.874-907. https://doi.org/10.3390/rs2030874
  10. Habib, A., Bang K., Kersting, A., and Lee, D.C. (2009), Error budget of LiDAR systems and quality control of the derived data, Photogrammetric Engineering and Remote Sensing , Vol. 75, No. 9, pp. 1093-1108. https://doi.org/10.14358/PERS.75.9.1093
  11. Habib, A., Kersting, A., Ruifanga, Z., Al-Durgham, M., Kim, C., and Lee, D.C. (2008), LiDAR strip adjustment using conjugate linear features in overlapping strips, Proceedings of International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences , 3-11 July, Beijing, China, Vol. 37, Part B1, pp. 385-390.
  12. Han, D., Yu, K., and Kim, Y. (2012), Georegistration of airborne LiDAR data using a digital topographic map, Journal of the Korean Society of Surveying Geodesy Photogrammetry and Cartography , Vol. 30, No. 3, pp. 323-332. (in Korean with English abstract) https://doi.org/10.7848/ksgpc.2012.30.3.323
  13. Haralick, R. and Shapiro, L. (1992), Computer and Robot Vision: Volume 1, Addison-Wesley, Reading, MA, New Jersey, Hoboken.
  14. Hassaballah, M., Abdelmgeid, A., and Alshazly, H. (2016), Image features detection, description and matching, In: Awad, A. and Hassaballah, M. (eds.), Image Feature Detections and Descriptors, Springer, Switzerland, pp. 11-45.
  15. Kersting, A. and Habib, A. (2012), A comparative analysis between rigorous and approximate approaches for LiDAR system calibration, Journal of the Korean Society of Surveying Geodesy Photogrammetry and Cartography , Vol. 30, No. 6-2, pp. 593-605. https://doi.org/10.7848/ksgpc.2012.30.6-2.593
  16. Lee, D., Yoo, E., Yom, J.H., and Lee, D.C. (2014), Strip adjustment of airborne laser scanner data using areabased surface matching, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 32, No. 6, pp. 625-635. https://doi.org/10.7848/ksgpc.2014.32.6.625
  17. Lowe, D. (2004), Distinctive image features from scaleinvariant key points, International Journal of Computer Vision , Vol. 60, No. 2, pp. 91-110. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  18. Maas, H. (2000), Least squares matching with airborne laser scanning data in a TIN structure, International Archives of Photogrammetry and Remote Sensing , Vol. 33, Part. B3, pp. 548-555.
  19. Mahalanobis, P. (1936), On the generalised distance in statistics, Proceedings of the National Institute of Sciences of India, Vol. 2, No. 1, pp. 49-55.
  20. Pfeifer, N., Elberink, S., and Filin, S. (2005), Automatic tie elements detection for laser scanner strip adjustment, International Archives of Photogrammetry and Remote Sensing , Vol. 36, Part 3/W3, pp. 1682-1750.
  21. Schenk, T. (1999), Digital Photogrammetry, Volume 1: Background, Fundamentals, Automatic Orientation Procedures , TerraScience, Laurelville, OH.
  22. Schenk, T. (2001), Modeling and Analyzing Systematic Errors of Airborne Laser Scanners, Technical Notes in Photogrammetry, No. 19, Department of Civil and Environmental Engineering and Geodetic Science, The 369 Ohio State University, Columbus, OH.
  23. Toth, C. (2009), Strip adjustment and registration, In: Shan, J. and Toth C. (eds.), Topographic Laser Ranging and Scanning: Principles and Processing, CRC Press, Boca Raton, FL, pp. 235-268.
  24. Zhang, Y., Xiong, X., and Hu, X (2013), Rigorous LiDAR strip adjustment with triangulated aerial imagery, Remote Sensing and Spatial Information Sciences , 11-13 November, Antalya, Turkey, Vol. II-5/W2, pp. 361-366.