Guided Unsupervised Learning by Subaperture Decomposition for Ocean SAR Image Retrieval

@article{Ristea2022GuidedUL,
  title={Guided Unsupervised Learning by Subaperture Decomposition for Ocean SAR Image Retrieval},
  author={Nicolae-Cuatualin Ristea and Andrei Anghel and Mihai Datcu and Bertrand Chapron},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.15034}
}
—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… 

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