  Satellite images are often contaminated by clouds. Cloud removal has received
much attention due to the wide range of satellite image applications. As the
clouds thicken, the process of removing the clouds becomes more challenging. In
such cases, using auxiliary images such as near-infrared or synthetic aperture
radar (SAR) for reconstructing is common. In this study, we attempt to solve
the problem using two generative adversarial networks (GANs). The first
translates SAR images into optical images, and the second removes clouds using
the translated images of prior GAN. Also, we propose dilated residual inception
blocks (DRIBs) instead of vanilla U-net in the generator networks and use
structural similarity index measure (SSIM) in addition to the L1 Loss function.
Reducing the number of downsamplings and expanding receptive fields by dilated
convolutions increase the quality of output images. We used the SEN1-2 dataset
to train and test both GANs, and we made cloudy images by adding synthetic
clouds to optical images. The restored images are evaluated with PSNR and SSIM.
We compare the proposed method with state-of-the-art deep learning models and
achieve more accurate results in both SAR-to-optical translation and cloud
removal parts.
