Deep Learning Inversion of Ocean Wave Spectrum from SAR Satellite Observations

Abstract

The monitoring of waves at the ocean surface is critical for both operational needs (e.g., maritime traffic) and scientific studies (e.g., air-sea interactions). Synthetic aperture radar (SAR) Satellites provide one of the only remote sensing observations to retrieve ocean wave information on a global scale. However state-of-the-art SAR processing schemes often lead to poor inversion performance due to overly-simplistic assumptions. Here we leverage deep learning schemes to address these shortcomings. We state the targeted measurement of the ocean wave spectrum at sea surface as a neural mapping from SAR satellite observations. We exploit supervised deep learning schemes trained from a large-scale collocation dataset between real SAR observations and Wavewatch III model data. Our results emphasize for the first time how deep learning schemes can outperform the state-of-the-art analytical SAR-based inversion with an improvement in terms of mean square error greater than 65%. We analyse and discuss further the key features of the trained neural processing.