International Journal of Digital Earth (Dec 2023)

Integrating topographic features and patch matching into point cloud restoration for terrain modelling

  • Jun Chen,
  • Liyang Xiong,
  • Guoan Tang,
  • Guanghui Hu,
  • Hong Wei,
  • Fei Zhao,
  • Lei Zhou

DOI
https://doi.org/10.1080/17538947.2023.2277797
Journal volume & issue
Vol. 16, no. 2
pp. 4573 – 4596

Abstract

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ABSTRACTPoint clouds are widely used in Earth surface research but usually exhibit gaps of missing data. Previous point cloud restoration methods used in terrain modelling have not fully considered complex terrain characteristics, which can be summarised as the controlling role of topographic features in shaping terrain surfaces and the inherent similarities observed among these surfaces. This work introduces a novel method that integrates Topographic Features and Patch Matching (TFPM) into point cloud restoration processes for terrain modelling. The method mainly contains three steps. First, identifying gap boundary points. Second, topographic feature points are extracted and subsequently interpolated into the identified gaps. Third, searching other parts of the raw point cloud for patches resembling the gaps, and the identified patches are used as templates to restore the point cloud. The proposed method is benchmarked against three state-of-the-art point cloud restoration methods. The experimental results demonstrate that the TFPM method consistently exhibits superior accuracy in terrain modelling and analysis, as evidenced by low values of the root mean square error, average elevation difference, and average slope difference. This work endeavours to incorporate topographic features into point cloud restoration processes and can benefit future research related to terrain modelling and analysis.

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