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An iterative coarse-to-fine sub-sampling method for density reduction of terrain point clouds

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An iterative coarse-to-fine sub-sampling method for density reduction of terrain point clouds. / Fan, L.; Atkinson, P.M.
In: Remote Sensing, Vol. 11, No. 8, 947, 19.04.2019.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Fan, L & Atkinson, PM 2019, 'An iterative coarse-to-fine sub-sampling method for density reduction of terrain point clouds', Remote Sensing, vol. 11, no. 8, 947. https://doi.org/10.3390/rs11080936

APA

Fan, L., & Atkinson, P. M. (2019). An iterative coarse-to-fine sub-sampling method for density reduction of terrain point clouds. Remote Sensing, 11(8), Article 947. https://doi.org/10.3390/rs11080936

Vancouver

Fan L, Atkinson PM. An iterative coarse-to-fine sub-sampling method for density reduction of terrain point clouds. Remote Sensing. 2019 Apr 19;11(8):947. doi: 10.3390/rs11080936

Author

Fan, L. ; Atkinson, P.M. / An iterative coarse-to-fine sub-sampling method for density reduction of terrain point clouds. In: Remote Sensing. 2019 ; Vol. 11, No. 8.

Bibtex

@article{07ebdf5f842e4520840c26cd425ea397,
title = "An iterative coarse-to-fine sub-sampling method for density reduction of terrain point clouds",
abstract = "Point clouds obtained from laser scanning techniques are now a standard type of spatial data for characterising terrain surfaces. Some have been shared as open data for free access. A problem with the use of these free point cloud data is that the data density may be more than necessary for a given application, leading to higher computational cost in subsequent data processing and visualisation. In such cases, to make the dense point clouds more manageable, their data density can be reduced. This research proposes a new coarse-to-fine sub-sampling method for reducing point cloud data density, which honours the local surface complexity of a terrain surface. The method proposed is tested using four point clouds representing terrain surfaces with distinct spatial characteristics. The effectiveness of the iterative coarse-to-fine method is evaluated and compared against several benchmarks in the form of typical sub-sampling methods available in open source software for point cloud processing. {\textcopyright} 2019 by the authors.",
keywords = "Interpolation, LiDAR, Point cloud, Sub-sampling, Iterative methods, Landforms, Open access, Open Data, Open source software, Open systems, Optical radar, Computational costs, Density reduction, Point cloud data, Spatial characteristics, Sub-sampling methods, Terrain surfaces, Data reduction",
author = "L. Fan and P.M. Atkinson",
year = "2019",
month = apr,
day = "19",
doi = "10.3390/rs11080936",
language = "English",
volume = "11",
journal = "Remote Sensing",
issn = "2072-4292",
publisher = "MDPI AG",
number = "8",

}

RIS

TY - JOUR

T1 - An iterative coarse-to-fine sub-sampling method for density reduction of terrain point clouds

AU - Fan, L.

AU - Atkinson, P.M.

PY - 2019/4/19

Y1 - 2019/4/19

N2 - Point clouds obtained from laser scanning techniques are now a standard type of spatial data for characterising terrain surfaces. Some have been shared as open data for free access. A problem with the use of these free point cloud data is that the data density may be more than necessary for a given application, leading to higher computational cost in subsequent data processing and visualisation. In such cases, to make the dense point clouds more manageable, their data density can be reduced. This research proposes a new coarse-to-fine sub-sampling method for reducing point cloud data density, which honours the local surface complexity of a terrain surface. The method proposed is tested using four point clouds representing terrain surfaces with distinct spatial characteristics. The effectiveness of the iterative coarse-to-fine method is evaluated and compared against several benchmarks in the form of typical sub-sampling methods available in open source software for point cloud processing. © 2019 by the authors.

AB - Point clouds obtained from laser scanning techniques are now a standard type of spatial data for characterising terrain surfaces. Some have been shared as open data for free access. A problem with the use of these free point cloud data is that the data density may be more than necessary for a given application, leading to higher computational cost in subsequent data processing and visualisation. In such cases, to make the dense point clouds more manageable, their data density can be reduced. This research proposes a new coarse-to-fine sub-sampling method for reducing point cloud data density, which honours the local surface complexity of a terrain surface. The method proposed is tested using four point clouds representing terrain surfaces with distinct spatial characteristics. The effectiveness of the iterative coarse-to-fine method is evaluated and compared against several benchmarks in the form of typical sub-sampling methods available in open source software for point cloud processing. © 2019 by the authors.

KW - Interpolation

KW - LiDAR

KW - Point cloud

KW - Sub-sampling

KW - Iterative methods

KW - Landforms

KW - Open access

KW - Open Data

KW - Open source software

KW - Open systems

KW - Optical radar

KW - Computational costs

KW - Density reduction

KW - Point cloud data

KW - Spatial characteristics

KW - Sub-sampling methods

KW - Terrain surfaces

KW - Data reduction

U2 - 10.3390/rs11080936

DO - 10.3390/rs11080936

M3 - Journal article

VL - 11

JO - Remote Sensing

JF - Remote Sensing

SN - 2072-4292

IS - 8

M1 - 947

ER -