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Study on Sampling Rule and Simplification of LiDAR Point Cloud Based on Terrain Complexity

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Abstract

It is difficult to obtain digital elevation model (DEM) in the mountainous regions. As an emerging technology, Light Detection and Ranging (LiDAR) is an enabling technology. However, the amount of points obtained by LiDAR is huge. When processing LiDAR point cloud, huge data will lead to a rapid decline in data processing speed, so it is necessary to thin LiDAR point cloud. In this paper, a new terrain sampling rule had been built based on the integrated terrain complexity, and then based on the rule a LiDAR point cloud simplification method, which was referred as to TCthin, had been proposed. The TCthin method was evaluated by experiments in which XUthin and Lasthin were selected as the TCthin’s comparative methods. The TCthin’s simplification degree was estimated by the simplification rate value, and the TCthin’s simplification quality was evaluated by Root Mean Square Deviation. The experimental results show that the TCthin method can thin LiDAR point cloud effectively and improve the simplification quality, and at 5 m, 10 m, 30 m scale levels, the TCthin method has a good applicability in the areas with different terrain complexity. This study has theoretical and practical value in sampling theory, thinning LiDAR point cloud, building high-precision DEM and so on.

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Acknowledgements

This research is supported by the Mapping Geographic Information Public Service Industry Research and Special Funds under Grant No. 201512028); the Central universities fundamental research funds under Grant No. 2682014CX017. The International Water Management Institute (IWMI) and the OpenTopography Facility of San Diego Supercomputer Center provide the remote sensing data.

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Authors and Affiliations

  1. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 611756, Sichuan, China

    Qian-ning Zhang, Ze-chun Huang & Zhu Xu

  2. State-province Joint Engineering Laboratory of Spatial Information Technology for High-speed Railway Safety, Southwest Jiaotong University, Chengdu, 610031, Sichuan, China

    Ze-chun Huang & Zhu Xu

  3. School of Architecture and Engineering, Xinjiang University, Ürümqi, 830046, Xinjiang Uygur Autonomous Region, China

    Hai-bin Shang

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  1. Qian-ning Zhang
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  2. Ze-chun Huang
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  3. Zhu Xu
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  4. Hai-bin Shang
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Correspondence to Ze-chun Huang.

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Zhang, Qn., Huang, Zc., Xu, Z. et al. Study on Sampling Rule and Simplification of LiDAR Point Cloud Based on Terrain Complexity. J Indian Soc Remote Sens 46, 1773–1784 (2018). https://doi.org/10.1007/s12524-018-0831-x

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  • DOI: https://doi.org/10.1007/s12524-018-0831-x

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