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On the use of the Radon transform to estimate longshore currents from video imagery
Author(s) -
Stanislas Larnier,
Rafaël Almar,
Rodrigo Cienfuegos,
Antoine Lejay
Publication year - 2014
Publication title -
journal of coastal research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.247
H-Index - 90
eISSN - 1551-5036
pISSN - 0749-0208
DOI - 10.2112/si70-005.1
Subject(s) - longshore drift , radon , radon transform , geology , meteorology , oceanography , geography , sediment , sediment transport , geomorphology , computer science , physics , quantum mechanics , artificial intelligence
International audienceIn nearshore applications, the estimation of longshore currents is of primary importance since it controls the alongshore sediment transport and coastal evolution. Direct estimation of longshore currents using in-situ instruments is difficult and costly, especially under highly energetic wave climates. Low cost remote sensing systems based on video observations constitute a promising alternative when the drifting sea foam left after the passage of breaking waves is visible. In this paper we describe a method based on longshore timestacks that necessitates less data than approaches using the full video frames. Our approach uses the Radon transform applied on the time series derived from timestacks to produce an estimate of the longshore component of nearshore surface currents detected from the foam signature in video images. The Radon transform can be used to separate the wave crests from the drifting foam part. The identification of the longshore drift is enhanced with both a temporal and a spatial filters. The corresponding sinogram from the Radon transform is computed in order to find the angle of the alongshore drifting that is further converted into the longshore component of the surface currents. For the estimation of longshore currents, our approach is first tested using synthetic timestack examples created using anisotropic Gaussian random current fields. Comparisons between estimates derived by our algorithm and manual operator detection from videos are performed showing good agreement. These videos come from a field campaign conducted in the Mataquito River mouth area in the Maule region (Chile). Field test comparisons were also made against in situ current meter from the 2008 Truc Vert experiment in Aquitaine (France). It was taking place during an energetic event

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