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Satellite images are often contaminated by clouds. Cloud removal has receivedmuch attention due to the wide range of satellite image applications. As theclouds thicken, the process of removing the clouds becomes more challenging. Insuch cases, using auxiliary images such as near-infrared or synthetic apertureradar (SAR) for reconstructing is common. In this study, we attempt to solvethe problem using two generative adversarial networks (GANs). The firsttranslates SAR images into optical images, and the second removes clouds usingthe translated images of prior GAN. Also, we propose dilated residual inceptionblocks (DRIBs) instead of vanilla U-net in the generator networks and usestructural similarity index measure (SSIM) in addition to the L1 Loss function.Reducing the number of downsamplings and expanding receptive fields by dilatedconvolutions increase the quality of output images. We used the SEN1-2 datasetto train and test both GANs, and we made cloudy images by adding syntheticclouds 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 andachieve more accurate results in both SAR-to-optical translation and cloudremoval parts.

Cloud removal in remote sensing images using generative adversarial networks and SAR-to-optical image translation