Corpus ID: 207758819

Conditional Denoising of Remote Sensing Imagery Using Cycle-Consistent Deep Generative Models

@article{Zotov2019ConditionalDO,
  title={Conditional Denoising of Remote Sensing Imagery Using Cycle-Consistent Deep Generative Models},
  author={Michael Zotov and Jevgenij Gamper},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.14567}
}
  • Michael Zotov, Jevgenij Gamper
  • Published 2019
  • Computer Science, Engineering
  • ArXiv
  • The potential of using remote sensing imagery for environmental modelling and for providing real time support to humanitarian operations such as hurricane relief efforts is well established. These applications are substantially affected by missing data due to non-structural noise such as clouds, shadows and other atmospheric effects. In this work we probe the potential of applying a cycle-consistent latent variable deep generative model (DGM) for denoising cloudy Sentinel-2 observations… CONTINUE READING

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