Sensitivity analysis of parameters and contrasting performance of ground filtering algorithms with UAV photogrammetry-based and LiDAR point clouds

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F20%3A82146" target="_blank" >RIV/60460709:41330/20:82146 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21110/20:00341988
Result on the web
<a href="https://www.tandfonline.com/doi/full/10.1080/17538947.2020.1791267" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/17538947.2020.1791267</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/17538947.2020.1791267" target="_blank" >10.1080/17538947.2020.1791267</a>

Result language
angličtina
Original language name
Sensitivity analysis of parameters and contrasting performance of ground filtering algorithms with UAV photogrammetry-based and LiDAR point clouds
Original language description
Most ground filtering algorithms are primarily designed for airborne LiDAR point cloud processing and their successful use in identifying ground points from photogrammetric point clouds remains questionable. We compared six ground filtering algorithms implemented in Metashape, ArcGIS, CloudCompare, LAStools, and PDAL. We used UAV photogrammetry-based (acquired under leaf-off conditions) and airborne LiDAR (leaf-on) point clouds of the same area to: (i) compare accuracy of generated DTMs (ii) evaluate the effect of vegetation density and terrain slope on filtering accuracy and (iii) assess which algorithm parameters have the greatest effect on the filtering accuracy. Our results show that the performance of filtering algorithms was affected by the point cloud type, terrain slope and vegetation cover. The results were generally better for LiDAR (RMSE 0,13-0,19 m) than for photogrammetric (RMSE 0,19-0,23 m) point clouds. The behavior in varying vegetation and terrain conditions was consistent for LiDAR
Czech name
—
Czech description
—

Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10508 - Physical geography

Project
<a href="/en/project/GJ17-17156Y" target="_blank" >GJ17-17156Y: Fusion of LiDAR and UAV borne multispectral data to assess physiographic diversity of post-mining sites</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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