Airborne LiDAR data filtering based on geodesic transformations of mathematical morphology

Airborne LiDAR data filtering based on geodesic transformations of mathematical morphology

Li, Y. (TU Delft OLD Department of GIS Technology; Hohai University)
Yong, Bin (Hohai University)
van Oosterom, P.J.M. (TU Delft OLD Department of GIS Technology)
Lemmens, M.J.P.M. (TU Delft OLD Department of GIS Technology)
Wu, Huayi (Wuhan University; Collaborative Innovation Center of Geospatial Technology)
Ren, Liliang (Hohai University)
Zheng, Mingxue (Collaborative Innovation Center of Geospatial Technology; Wuhan University)
Zhou, Jiajun (Hohai University)

2017

The capability of acquiring accurate and dense three-dimensional geospatial information that covers large survey areas rapidly enables airborne light detection and ranging (LiDAR) has become a powerful technology in numerous fields of geospatial applications and analysis. LiDAR data filtering is the first and essential step for digital elevation model generation, land cover classification, and object reconstruction. The morphological filtering approaches have the advantages of simple concepts and easy implementation, which are able to filter non-ground points effectively. However, the filtering quality of morphological approaches is sensitive to the structuring elements that are the key factors for the filtering success of mathematical operations. Aiming to deal with the dependence on the selection of structuring elements, this paper proposes a novel filter of LiDAR point clouds based on geodesic transformations of mathematical morphology. In comparison to traditional morphological transformations, the geodesic transformations only use the elementary structuring element and converge after a finite number of iterations. Therefore, this algorithm makes it unnecessary to select different window sizes or determine the maximum window size, which can enhance the robustness and automation for unknown environments. Experimental results indicate that the new filtering method has promising and competitive performance for diverse landscapes, which can effectively preserve terrain details and filter non-ground points in various complicated environments

LiDAR
filtering
mathematical morphology
geodesic transformation
OA-Fund TU Delft

http://resolver.tudelft.nl/uuid:46544734-d063-4b49-9e07-869b8ed46d01

https://doi.org/10.3390/rs9111104

2072-4292

Remote Sensing, 9 (11)

Institutional Repository

journal article

© 2017 Y. Li, Bin Yong, P.J.M. van Oosterom, M.J.P.M. Lemmens, Huayi Wu, Liliang Ren, Mingxue Zheng, Jiajun Zhou