Archival photogrammetric analysis of river–floodplain systems using Structure from Motion (SfM) methods

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Version: Author's accepted manuscript
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Serval ID
serval:BIB_C160B2684F27
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Archival photogrammetric analysis of river–floodplain systems using Structure from Motion (SfM) methods
Journal
Earth Surface Processes and Landforms
Author(s)
Bakker M., Lane S.N.
ISSN
0197-9337
ISSN-L
1096-9837
Publication state
Published
Issued date
2017
Peer-reviewed
Oui
Volume
42
Pages
1274-1286
Language
english
Notes
A formatted version is also available at : https://rdcu.be/biYFC
Abstract
In this study we evaluate the extent to which accurate topographic data can be obtained by applying Structure from Motion (SfM) photogrammetric methods to archival imagery. While SfM has proven valuable in photogrammetric applications using specially acquired imagery (e.g. from unmanned aerial vehicles), it also has the potential to improve the precision of topographic data and the ease with which can be produced from historical imagery. We evaluate the application of SfM to a relatively extreme case, one of low relative relief: a braided river–floodplain system. We compared the bundle adjustments of SfM and classical photogrammetric methods, applied to eight dates. The SfM approach resulted in data quality similar to the classical approach, although the lens parameter values (e.g. focal length) recovered in the SfM process were not necessarily the same as their calibrated equivalents. Analysis showed that image texture and image overlap/configuration were critical drivers in the tie-point generation which impacted bundle adjustment quality. Working with archival imagery also illustrated the general need for the thorough understanding and careful application of (commercial) SfM software packages. As with classical methods, the propagation of (random) error in the estimation of lens and exterior orientation parameters using SfM methods may lead to inherent systematic error in the derived point clouds. We have shown that linear errors may be accounted for by point cloud registration based on a reference dataset, which is vital for the further application in quantitative morphological analyses when using archival imagery. Copyright © 2016 John Wiley & Sons, Ltd.
Keywords
archival aerial imagery, classical photogrammetry, Structure from Motion (SfM) methods, systematic error minimisation, morphological interpretation
Open Access
Yes
Create date
25/06/2017 13:38
Last modification date
30/10/2023 9:53
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