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Open AccessArticle

Comparing Filtering Techniques for Removing Vegetation from UAV-Based Photogrammetric Point Clouds

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Satelligence BV, Maliebaan 22, 3581 CP Utrecht, The Netherlands
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Information Technology Group, Wageningen University, Hollandseweg 1, 6706 KN Wageningen, The Netherlands
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Team Soil, Water and Land Use, Wageningen Environmental Research, PO Box 47, 6700 AA Wageningen, The Netherlands
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School of Engineering, Faculty of Engineering and Built Environment, The University of Newcastle, University Drive, Callaghan, NSW 2308, Australia
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Author to whom correspondence should be addressed.
Drones 2019, 3(3), 61; https://doi.org/10.3390/drones3030061
Received: 29 June 2019 / Revised: 26 July 2019 / Accepted: 26 July 2019 / Published: 30 July 2019
Digital Elevation Models (DEMs) are 3D representations of the Earth’s surface and have numerous applications in geomorphology, hydrology and ecology. Structure-from-Motion (SfM) photogrammetry using photographs obtained by unmanned aerial vehicles (UAVs) have been increasingly used for obtaining high resolution DEMs. These DEMs are interpolated from point clouds representing entire landscapes, including points of terrain, vegetation and infrastructure. Up to date, there has not been any study clearly comparing different algorithms for filtering of vegetation. The objective in this study was, therefore, to assess the performance of various vegetation filter algorithms for SfM-obtained point clouds. The comparison was done for a Mediterranean area in Murcia, Spain with heterogeneous vegetation cover. The filter methods that were compared were: color-based filtering using an excessive greenness vegetation index (VI), Triangulated Irregular Networks (TIN) densification from LAStools, the standard method in Agisoft Photoscan (PS), iterative surface lowering (ISL), and a combination of iterative surface lowering and the VI method (ISL_VI). Results showed that for bare areas there was little to no difference between the filtering methods, which is to be expected because there is little to no vegetation present to filter. For areas with shrubs and trees, the ISL_VI and TIN method performed best. These results show that different filtering techniques have various degrees of success in different use cases. A default filter in commercial software such as Photoscan may not always be the best way to remove unwanted vegetation from a point cloud, but instead alternative methods such as a TIN densification algorithm should be used to obtain a vegetation-less Digital Terrain Model (DTM). View Full-Text
Keywords: UAV; fixed-wings; low-altitude aerial photography; DTM; vegetation filtering; TIN densification; sparse vegetation UAV; fixed-wings; low-altitude aerial photography; DTM; vegetation filtering; TIN densification; sparse vegetation
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Anders, N.; Valente, J.; Masselink, R.; Keesstra, S. Comparing Filtering Techniques for Removing Vegetation from UAV-Based Photogrammetric Point Clouds. Drones 2019, 3, 61.

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