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- W4288087310 abstract "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 conditioned on the information in cloud penetrating bands. We adapt the recently proposed Fr'{e}chet Distance metric to remote sensing images for evaluating performance of the generator, demonstrate the potential of DGMs for conditional denoising, and discuss future directions as well as the limitations of DGMs in Earth science and humanitarian applications." @default.
- W4288087310 created "2022-07-28" @default.
- W4288087310 creator A5038337718 @default.
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- W4288087310 date "2019-10-31" @default.
- W4288087310 modified "2023-10-16" @default.
- W4288087310 title "Conditional Denoising of Remote Sensing Imagery Using Cycle-Consistent Deep Generative Models" @default.
- W4288087310 doi "https://doi.org/10.48550/arxiv.1910.14567" @default.
- W4288087310 hasPublicationYear "2019" @default.
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