Application of the LSTM Models for Baltic Sea Wave Spectra Estimation

Abstract

This article proposes to apply long-short-term memory (LSTM) deep learning models to transform Sentinel-1 A/B interferometric wide (IW) swath image data into the wave density spectrum. Although spectral wave estimation methods for synthetic aperture radar data have been developed, similar approaches for coastal areas have not received enough attention. Partially, this is caused by the lack of high-resolution wave-mode data, as well as the nature of wind waves that have more complicated backscattering mechanisms compared to the swell waves for which the aforementioned methods were developed. The application of the LSTM model has allowed the transformation of the Sentinel-1 A/B IW one-dimensional image spectrum into wave density spectra. The best results in the test dataset led to the mean Pearson's correlation coefficient 0.85 for the comparison of spectra and spectra. The result was achieved with the LSTM model using $VV$ and $VH$ polarization spectra fed into the model independently. Experiments with LSTM neural networks that classify images into wave spectra with the Baltic Sea dataset demonstrated promising results in cases where empirical methods were previously considered.

Keywords

• Coastal sea
• long-short-term memory (LSTM)
• remote sensing
• synthetic aperture radar (SAR)
• wave density spectra