  Spaceborne synthetic aperture radar (SAR) can provide accurate images of the
ocean surface roughness day-or-night in nearly all weather conditions, being an
unique asset for many geophysical applications. Considering the huge amount of
data daily acquired by satellites, automated techniques for physical features
extraction are needed. Even if supervised deep learning methods attain
state-of-the-art results, they require great amount of labeled data, which are
difficult and excessively expensive to acquire for ocean SAR imagery. To this
end, we use the subaperture decomposition (SD) algorithm to enhance the
unsupervised learning retrieval on the ocean surface, empowering ocean
researchers to search into large ocean databases. We empirically prove that SD
improve the retrieval precision with over 20% for an unsupervised transformer
auto-encoder network. Moreover, we show that SD brings important performance
boost when Doppler centroid images are used as input data, leading the way to
new unsupervised physics guided retrieval algorithms.
