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We used the SEN1-2 dataset to train and test both GANs, and we made cloudy images by adding synthetic clouds to optical images.
In remote sensing images, the existence of the thin cloud is an inevitable and ubiquitous phenomenon that crucially reduces the quality of imageries and limits the scenarios of application.
This work has been accepted by IEEE TGRS for publication.
In this paper, a novel thick cloud removal method for remote sensing images based on temporal smoothness and sparsity-regularized tensor optimization (TSSTO) is proposed.
Over the years, millions of such inspection sheets have been recorded and the data within these sheets has remained inaccessible.
In this paper, we present the optical image simulation from a synthetic aperture radar (SAR) data using deep learning based methods.
The networks are trained to output images that are close to the ground truth using the images synthesized with clouds over the ground truth as inputs.