Conditional Denoising of Remote Sensing Imagery Using Cycle-Consistent Deep Generative Models
This addresses data quality issues for environmental modelling and humanitarian operations like hurricane relief, but it is incremental as it adapts existing methods to a specific domain.
The paper tackled the problem of missing data in remote sensing imagery due to clouds and atmospheric effects by applying a cycle-consistent deep generative model for conditional denoising, demonstrating its potential with an adapted Fréchet Distance metric for evaluation.
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 Fréchet 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.