From picture to porosity of river bed material using Structure-from-Motion with Multi-View-Stereo

From picture to porosity of river bed material using Structure-from-Motion with Multi-View-Stereo Common methods for in-situ determination of porosity of river bed material are time- and effort-consuming. Although mathematical predictors can be used for estimation, they do not adequately represent porosities. The objective of this study was to assess a new approach for the determination of porosity of frozen sediment samples. The method is based on volume determination by applying Structure-from-Motion with Multi View Stereo (SfM-MVS) to estimate a 3D volumetric model based on overlapping imagery. The method was applied on artificial sediment mixtures as well as field samples. In addition, the commonly used water replacement method was applied to determine porosities in comparison with the SfM-MVS method. We examined a range of porosities from 0.16 to 0.46 that are representative of the wide range of porosities found in rivers. SfM-MVS performed well in determining volumes of the sediment samples. A very good correlation (r = 0.998, p < 0.0001) was observed between the SfM-MVS and the water replacement method. Results further show that the water replacement method underestimated total sample volumes. A comparison with several mathematical predictors showed that for non-uniform samples the calculated porosity based on the standard deviation performed better than porosities based on the median grain size. None of the predictors were effective at estimating the porosity of the field samples. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Geomorphology Elsevier

From picture to porosity of river bed material using Structure-from-Motion with Multi-View-Stereo

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier B.V.
ISSN
0169-555X
eISSN
1872-695X
D.O.I.
10.1016/j.geomorph.2018.01.014
Publisher site
See Article on Publisher Site

Abstract

Common methods for in-situ determination of porosity of river bed material are time- and effort-consuming. Although mathematical predictors can be used for estimation, they do not adequately represent porosities. The objective of this study was to assess a new approach for the determination of porosity of frozen sediment samples. The method is based on volume determination by applying Structure-from-Motion with Multi View Stereo (SfM-MVS) to estimate a 3D volumetric model based on overlapping imagery. The method was applied on artificial sediment mixtures as well as field samples. In addition, the commonly used water replacement method was applied to determine porosities in comparison with the SfM-MVS method. We examined a range of porosities from 0.16 to 0.46 that are representative of the wide range of porosities found in rivers. SfM-MVS performed well in determining volumes of the sediment samples. A very good correlation (r = 0.998, p < 0.0001) was observed between the SfM-MVS and the water replacement method. Results further show that the water replacement method underestimated total sample volumes. A comparison with several mathematical predictors showed that for non-uniform samples the calculated porosity based on the standard deviation performed better than porosities based on the median grain size. None of the predictors were effective at estimating the porosity of the field samples.

Journal

GeomorphologyElsevier

Published: Apr 1, 2018

References

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