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Due to all-weather and all-time work characteristics, synthetic aperture radar (SAR) images have been widely used in remote sensing. There is great difficulty in understanding SAR images because they are quite different from optical images in imaging mechanism, geometric characteristics, and radiation characteristics. It can greatly improve the readability of SAR images if we can translate them into optical image styles. In this paper, we propose a variational generative adversarial network for SAR images to optical images translation (S2O-VGAN). To demonstrate the validity of the proposed model, a new large-scale dataset called SARGB is proposed. Experimental results based on the proposed dataset with several evaluations show the superiority of the proposed model over the existing methods on SAR-optical image translation.

SAR-to-optical image translation by a variational generative adversarial network