Whitecaps are formed at the ocean surface when breaking waves entrain sufficient quantities of air to produce an optically distinct signal at the water surface. Whitecaps drive energy transfer from the surface wave field to the upper ocean and the entrained air drives key bubble-mediated processes such as air-sea gas exchange and aerosol production flux. By exploiting the broadband scattering of light by the surface whitecaps, this study develops an algorithm for automated whitecap detection and tracking (AWDAT) from fixed image systems, in order to detect and track individual whitecaps. AWDAT introduces new image processing and computer vision techniques that handle temporal developments of whitecaps and complex behaviors of whitecap foam patches such as splitting and merging. We teach a learning-based model to filter tracked whitecaps based on the AWDAT-measured foam area, breaking speed, and direction time series. The algorithm is tested on three different image datasets to assess its performance with different camera systems in different geographic locations—the Adriatic Sea (AS), Black Sea (BS), and Yellow Sea (YS). Applications of AWDAT are demonstrated by aggregating whitecap statistics from geometric, kinematic, and dynamic measurements of individual breaking waves, which are then evaluated within the volume-time-integral (VTI) method and Phillips (1985)$\Lambda (c)$spectral framework.

An image processing observational technique for the stereoscopic reconstruction of the waveform of oceanic sea states is developed. The technique incorporates the enforcement of any given statistical wave law modeling the quasi-Gaussianity of oceanic waves observed in nature. The problem is posed in a variational optimization framework, where the desired waveform is obtained as the minimizer of a cost functional that combines image observations, smoothness priors and a weak statistical constraint. The minimizer is obtained by combining gradient descent and multigrid methods on the necessary optimality equations of the cost functional. Robust photometric error criteria and a spatial intensity compensation model are also developed to improve the performance of the presented image matching strategy. The weak statistical constraint is thoroughly evaluated in combination with other elements presented to reconstruct and enforce constraints on experimental stereo data, demonstrating the improvement in the estimation of the observed ocean surface.

We develop a novel remote sensing technique for the observation of waves on the ocean surface. Our method infers the 3-D waveform and radiance of oceanic sea states via a variational stereo imagery formulation. In this setting, the shape and radiance of the wave surface are given by minimizers of a composite energy functional that combines a photometric matching term along with regularization terms involving the smoothness of the unknowns. The desired ocean surface shape and radiance are the solution of a system of coupled partial differential equations derived from the optimality conditions of the energy functional. The proposed method is naturally extended to study the spatiotemporal dynamics of ocean waves and applied to three sets of stereo video data. Statistical and spectral analysis are carried out. Our results provide evidence that the observed omnidirectional wavenumber spectrumS(k) decays ask-2.5is in agreement with Zakharov's theory (1999). Furthermore, the 3-D spectrum of the reconstructed wave surface is exploited to estimate wave dispersion and currents.