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Remote Sens. 2017, 9(11), 1104; doi:10.3390/rs9111104

Airborne LiDAR Data Filtering Based on Geodesic Transformations of Mathematical Morphology

1
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
2
School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China
3
GIS Technology Section, Department OTB, Faculty of Architecture and the Built Environment, Delft University of Technology, 2628 BL Delft, The Netherlands
4
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
5
Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China
*
Authors to whom correspondence should be addressed.
Received: 1 August 2017 / Revised: 23 October 2017 / Accepted: 26 October 2017 / Published: 29 October 2017
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Abstract

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. View Full-Text
Keywords: LiDAR; filtering; mathematical morphology; geodesic transformation LiDAR; filtering; mathematical morphology; geodesic transformation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Li, Y.; Yong, B.; van Oosterom, P.; Lemmens, M.; Wu, H.; Ren, L.; Zheng, M.; Zhou, J. Airborne LiDAR Data Filtering Based on Geodesic Transformations of Mathematical Morphology. Remote Sens. 2017, 9, 1104.

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