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A fast and robust interpolation filter for airborne lidar point clouds

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  • Chuanfa Chen
  • Yanyan Li
  • Na Zhao
  • Jinyun Guo
  • Guolin Liu

Abstract

A fast and robust interpolation filter based on finite difference TPS has been proposed in this paper. The proposed method employs discrete cosine transform to efficiently solve the linear system of TPS equations in case of gridded data, and by a pre-defined weight function with respect to simulation residuals to reduce the effect of outliers and misclassified non-ground points on the accuracy of reference ground surface construction. Fifteen groups of benchmark datasets, provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) commission, were employed to compare the performance of the proposed method with that of the multi-resolution hierarchical classification method (MHC). Results indicate that with respect to kappa coefficient and total error, the proposed method is averagely more accurate than MHC. Specifically, the proposed method is 1.03 and 1.32 times as accurate as MHC in terms of kappa coefficient and total errors. More importantly, the proposed method is averagely more than 8 times faster than MHC. In comparison with some recently developed methods, the proposed algorithm also achieves a good performance.

Suggested Citation

  • Chuanfa Chen & Yanyan Li & Na Zhao & Jinyun Guo & Guolin Liu, 2017. "A fast and robust interpolation filter for airborne lidar point clouds," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-20, May.
    • Handle: RePEc:plo:pone00:0176954
    DOI: 10.1371/journal.pone.0176954
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    References listed on IDEAS

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    1. Garcia, Damien, 2010. "Robust smoothing of gridded data in one and higher dimensions with missing values," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1167-1178, April.
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