Comparison of filtering algorithms used for DTM production from airborne lidar data: a case study in Bergama, Turkey

Suleymanoglu, Baris; Soycan, Metin

doi:10.15292/geodetski-vestnik.2019.03.395-414

Cited by 9 publications

(8 citation statements)

References 31 publications

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“…The analyzed filters are summarized in Table 2. To our knowledge PMF, SBF, SMRF, CSF, SegBF, and BMHF were not assessed in any of the previous studies [22][23][24][25][26][27].…”

Section: Evaluated Filtersmentioning

confidence: 99%

“…These and numerous other existing filters are based on algorithms that can be divided into five categories [26,[45][46][47]:…”

Section: Evaluated Filtersmentioning

confidence: 99%

“…The new points are inserted into the triangulation. The process is repeated on the thus densified network until (i) there are no more points in a triangle, (ii) a certain density of ground points is reached, or (iii) when all acceptable points are closer to the surface than a threshold value [26,46,47].…”

Section: Evaluated Filtersmentioning

confidence: 99%

“…Points that have negative residual values are weighted more heavily and are considered ground points. There are many extensions and variants of the surface-based filters [26,46].…”

Section: Evaluated Filtersmentioning

confidence: 99%

“…In the past several studies have compared relevant filters implemented in free software [22][23][24][25][26][27]. However, none of them include newer filters, none of them use a standardized comparison method by Sithole and Vosselman [20], and none of them is archaeology-specific.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Filters for Archaeology-Specific Ground Extraction from Airborne LiDAR Point Clouds

Štular

Lozić

2020

Remote Sensing

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Identifying bare-earth or ground returns within point cloud data is a crucially important process for archaeologists who use airborne LiDAR data, yet there has thus far been very little comparative assessment of the available archaeology-specific methods and their usefulness for archaeological applications. This article aims to provide an archaeology-specific comparison of filters for ground extraction from airborne LiDAR point clouds. The qualitative and quantitative comparison of the data from four archaeological sites from Austria, Slovenia, and Spain should also be relevant to other disciplines that use visualized airborne LiDAR data. We have compared nine filters implemented in free or low-cost off-the-shelf software, six of which are evaluated in this way for the first time. The results of the qualitative and quantitative comparison are not directly analogous, and no filter is outstanding compared to the others. However, the results are directly transferable to real-world problem-solving: Which filter works best for a given combination of data density, landscape type, and type of archaeological features? In general, progressive TIN (software: lasground_new) and a hybrid (software: Global Mapper) commercial filter are consistently among the best, followed by an open source slope-based one (software: Whitebox GAT). The ability of the free multiscale curvature classification filter (software: MCC-LIDAR) to remove vegetation is also commendable. Notably, our findings show that filters based on an older generation of algorithms consistently outperform newer filtering techniques. This is a reminder of the indirect path from publishing an algorithm to filter implementation in software.

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“…The analyzed filters are summarized in Table 2. To our knowledge PMF, SBF, SMRF, CSF, SegBF, and BMHF were not assessed in any of the previous studies [22][23][24][25][26][27].…”

Section: Evaluated Filtersmentioning

confidence: 99%

“…These and numerous other existing filters are based on algorithms that can be divided into five categories [26,[45][46][47]:…”

Section: Evaluated Filtersmentioning

confidence: 99%

Section: Evaluated Filtersmentioning

confidence: 99%

“…Points that have negative residual values are weighted more heavily and are considered ground points. There are many extensions and variants of the surface-based filters [26,46].…”

Section: Evaluated Filtersmentioning

confidence: 99%