Performance analysis of freeware filtering algorithms for determining ground surface from airborne laser scanning data

Julge, Kalev; Ellmann, Artu; Gruno, Anti

doi:10.1117/1.jrs.8.083573

Cited by 17 publications

(12 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

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

Štular

Lozić

2020

Remote Sensing

View full text Add to dashboard Cite

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.

show abstract

“…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%

“…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%

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

Štular

Lozić

2020

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Unmanned aerial vehicles that carry heavier payloads are mounted with better quality cameras. Cost-effective implementation of UAV surveying requires that field operations are planned to acquire sufficient spatial data efficiently, and that data processing can filter out non-ground points to provide an accurate "bare earth" model (Julge, Ellmann, & Gruno, 2014). There is evidence that the governmental road administration authorities have innovatively started to encourage the usage of UAV-based monitoring of roadwork.…”

Section: Introductionmentioning

confidence: 99%

Unmanned Aerial Vehicle Surveying For Monitoring Road Construction Earthworks

Julge

Ellmann

Köök

2019

BJRBE

Self Cite

View full text Add to dashboard Cite

Unmanned aerial vehicle photogrammetry is a surveying technique that enables generating point clouds, 3D surface models and orthophoto mosaics. These are based on photos captured with a camera placed on an unmanned aerial vehicle. Within the framework of this research, unmanned aerial vehicle photogrammetry surveys were carried out over a sand and gravel embankment with the aim of assessing the vertical accuracy of the derived surface models. Flight altitudes, ground control points and cameras were varied, and the impact of various factors on the results was monitored. In addition, the traditional real-time-kinematic Global Navigation Satellite System surveys were conducted for verifications. Surface models acquired by different methods were used to calculate volumes and compare the results with requirements set by Estonian Road Administration. It was found that with proper measuring techniques an accuracy of 5.7 cm for the heights were achieved.

show abstract

“…Given that this study aims to generate high-quality DTM data, the ground points obtained by filtering algorithms are interpolated into DTMs for evaluation. The elevation discrepancies between the generated and the reference DTMs are computed, and the RMSEs calculated from these discrepancies are used to quantify the accuracy of the generated DTM [56]. Apart from the calculation of RMSE for every single site, the overall accuracy values for all residential and mountainous sites respectively represented by RMSE r and RMSE m are calculated for each dataset.…”

Section: Evaluation and Comparisonmentioning

confidence: 99%

A Point Cloud Filtering Approach to Generating DTMs for Steep Mountainous Areas and Adjacent Residential Areas

et al. 2016

View full text Add to dashboard Cite

Digital terrain models (DTMs) are considered important basic geographic data. They are widely used in the fields of cartography, land utilization, urban planning, communications, and remote sensing. Digital photogrammetry mainly based on stereo image matching is a frequently applied technique to generate DTMs. Generally, the process of ground filtering should be applied to the point cloud derived from image matching to separate terrain and off-terrain points before DTM generation. However, many of the existing filtering methods perform unsatisfactorily for steep mountainous areas, particularly when residential neighborhoods exist in the proximity of the test areas. In this study, an improved automated filtering method based on progressive TIN (triangulated irregular networks) densification (PTD) is proposed to generate DTMs for steep mountainous areas and adjacent residential areas. Our main improvement on the classic method is the acquisition of seed points with better distribution and reliability to enhance its adaptability to different types of terrain. A rule-based method for detecting ridge points is first applied. The detected points are used as additional seed points. Subsequently, a locally optimized seed point selection method based on confidence interval estimation theory is applied to remove the erroneous points. The experiments on two sets of stereo-matched point clouds indicate that the proposed method performs well for both residential and mountainous areas. The total accuracy values in the form of root-mean-square errors of the generated DTMs by the proposed method are 0.963 and 1.007 m; respectively; which are better than the 1.286 and 1.309 m achieved by the classic PTD method.

show abstract

Performance analysis of freeware filtering algorithms for determining ground surface from airborne laser scanning data

Cited by 17 publications

References 8 publications

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

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

Unmanned Aerial Vehicle Surveying For Monitoring Road Construction Earthworks

A Point Cloud Filtering Approach to Generating DTMs for Steep Mountainous Areas and Adjacent Residential Areas

Contact Info

Product

Resources

About