Converting SAR Images to Optical Images Using CycleGAN

This paper explores the application of CycleGAN, a generative adversarial network, to perform unpaired image-to-image translation between Synthetic Aperture Radar (SAR) images and optical images. This research leverages an unpaired dataset of SAR and optical images, training the CycleGAN model to generate realistic optical images from previously unseen SAR data. The implications of this work are significant for remote sensing applications, where optical images may be more advantageous for specific tasks. The study demonstrates the potential of unpaired data in addressing the challenge of SAR to optical image translation.