Significant wave height estimation using SVR algorithms and shadowing information from simulated and real measured X-band radar images of the sea surface

• We estimate significant wave height from X-band radar images using support vector regression (SVR).
• We use simulation-based data to train the SVR.
• We compare the performance of the SVR with that of neural networks.
• Finally we test the SVR trained over simulated-based data in real data at the North Sea.

In this paper we propose to apply the Support Vector Regression (SVR) methodology to significant wave height estimation using the shadowing effect, that is visible on the X-band marine radar images of the sea surface due to the presence of high waves. One of the main problems of using sea clutter images is that, for a given sea state conditions, the shadowing effect depends on the radar antenna installation features, such as the angle of incidence. On the other hand, for a given radar antenna location, the shadowing properties depend on the different sea state parameters, like wave periods, and wave lengths. Thus, in this paper we show that SVR can be successfully trained from simulation-based data. We propose a simulation process for X-band marine radar images derived from simulated wave elevation fields using the stochastic wave theory. We show the performance of the SVR in simulation data and how SVR outperforms alternative algorithms such as neural networks. Finally, we show that the simulation process is reliable by applying the SVR methodology trained in the simulation-based data to real measured data, obtaining good prediction results in wave height, which indicates the goodness of our proposal.