Progressive Filtering of Airborne LiDAR Point Clouds Using Graph Cuts

He, Y, Zhang, C and Simpson Fraser, C 2018, 'Progressive Filtering of Airborne LiDAR Point Clouds Using Graph Cuts', IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 8, pp. 2933-2944.


Document type: Journal Article
Collection: Journal Articles

Title Progressive Filtering of Airborne LiDAR Point Clouds Using Graph Cuts
Author(s) He, Y
Zhang, C
Simpson Fraser, C
Year 2018
Journal name IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume number 11
Issue number 8
Start page 2933
End page 2944
Total pages 12
Publisher IEEE
Abstract The development of robust and accurate filtering approaches for automated extraction of digital terrain models (DTMs) from airborne Light Detection and Ranging (LiDAR) data continues to be a challenge. The problem is due to the nature of LiDAR point clouds, the complexity of scene components, and the intrinsic structure of the terrain itself. This paper proposes a novel approach for filtering LiDAR point clouds, which exploits the spatial structure of the terrain and the spatial coherence among the LiDAR points. Terrain points are progressively detected through energy minimization using graph cuts. The energy function and graph model encode both pointwise closeness and pairwise smoothness. The DTM is then extracted through progressive filtering via the graph cuts. The performance of the proposed method is investigated using two datasets with different point densities, terrain complexity, and land covers. The results show that the filter can effectively remove nonterrain points, leading to an accurately extracted DTM. The filter is also compared with other methods reported in the literature, the comparison demonstrating that the proposed method exhibits advantages in terms of performance.
Subject Physical Geography and Environmental Geoscience not elsewhere classified
Keyword(s) Airborne LiDAR
digital terrain models (DTM)
filtering
graph cuts
point cloud
DOI - identifier 10.1109/JSTARS.2018.2839738
Copyright notice © 2018 IEEE
ISSN 1939-1404
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 1 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 0 times in Scopus Article
Altmetric details:
Access Statistics: 10 Abstract Views  -  Detailed Statistics
Created: Thu, 31 Jan 2019, 11:26:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us