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1 March 2016 Synthetic Imagery for the Automated Detection of Rip Currents
Sebastian Pitman, Shari L. Gallop, Ivan D. Haigh, Sasan Mahmoodi, Gerd Masselink, Roshanka Ranasinghe
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Pitman, S.J.; Gallop, S.L.; Haigh, I.D.; Mahmoodi, S.; Masselink, G., and Ranasinghe, R., 2016. Synthetic imagery for the automated detection of rip currents. In: Vila-Concejo, A.; Bruce, E.; Kennedy, D.M., and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 912–916. Coconut Creek (Florida), ISSN 0749-0208.

Rip currents are a major hazard on beaches worldwide. Although in-situ measurements of rips can be made in the field, it is generally safer and more cost effective to employ remote sensing methods, such as coastal video imaging systems. However, there is no universal, fully-automated method capable of detecting rips in imagery. In this paper we discuss the benefits of image manipulation, such as filtering, prior to rip detection attempts. Furthermore, we present a new approach to detect rip channels that utilizes synthetic imagery. The creation of a synthetic image involves the partitioning of the ‘parent’ image into key areas, such as sand bars, channels, shoreline and offshore. Then, pixels in each partition are replaced with the respective dominant color trends observed in the parent image. Using synthetic imagery increased the accuracy of rip detection from 81% to 92%. Synthetics reduce ‘noise’ inherent in surfzone imagery and is another step towards an automated approach for rip current detection.

©Coastal Education and Research Foundation, Inc. 2016
Sebastian Pitman, Shari L. Gallop, Ivan D. Haigh, Sasan Mahmoodi, Gerd Masselink, and Roshanka Ranasinghe "Synthetic Imagery for the Automated Detection of Rip Currents," Journal of Coastal Research 75(sp1), 912-916, (1 March 2016).
Received: 15 October 2015; Accepted: 15 January 2016; Published: 1 March 2016
coastal imaging
image filtering
remote sensing
rip channel
synthetic imagery
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