  Spaceborne synthetic aperture radar can provide meters scale images of the
ocean surface roughness day or night in nearly all weather conditions. This
makes it a unique asset for many geophysical applications. Sentinel 1 SAR wave
mode vignettes have made possible to capture many important oceanic and
atmospheric phenomena since 2014. However, considering the amount of data
provided, expanding applications requires a strategy to automatically process
and extract geophysical parameters. In this study, we propose to apply
subaperture decomposition as a preprocessing stage for SAR deep learning
models. Our data centring approach surpassed the baseline by 0.7, obtaining
state of the art on the TenGeoPSARwv data set. In addition, we empirically
showed that subaperture decomposition could bring additional information over
the original vignette, by rising the number of clusters for an unsupervised
segmentation method. Overall, we encourage the development of data centring
approaches, showing that, data preprocessing could bring significant
performance improvements over existing deep learning models.
