Wave Celerity Estimation using Unsupervised Image Registration from Video Imagery

Jinah Kim, Jaeil Kim, Sungwon Shin

http://doi.org/10.5626/JOK.2019.46.12.1296

In this paper, we propose an image registration method based on unsupervised learning to estimate wave celerity by tracking wave movements using a large amount of video imagery. It is difficult to estimate the wave celerity accurately using physics-based modeling in the coastal region, owing to the limitations of in-situ measurement and the high nonlinearity of wave phenomena itself as well as high complexity from nonlinear interactions. In order to estimate wave celerity, the proposed method learns the nonlinear wave behavior from the video imagery. Autoencoder is applied to separate hydrodynamics scenes from environmental factors, such as daylights. The displacement vector of propagating waves is computed by non-linear spatio-temporal image registration. The wave celerity is estimated by accumulating the displacement vectors along time. In this paper, we compare the wave celerity measurement with conventional image processing methods and actual measurement using sensors for accuracy evaluation.