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In contrast to the well-investigated field of SAR-to-Optical translation,this study explores the lesser-investigated domain of Optical-to-SARtranslation, a challenging field due to the ill-posed nature of thistranslation. The complexity arises as a single optical data can have multipleSAR representations based on the SAR viewing geometry. We propose a novelapproach, termed SAR Temporal Shifting, which inputs an optical data from thedesired timestamp along with a SAR data from a different temporal point butwith a consistent viewing geometry as the expected SAR data, both complementedwith a change map of optical data during the intervening period. This modelmodifies the SAR data based on the changes observed in optical data to generatethe SAR data for the desired timestamp. Our model, a dual conditionalGenerative Adversarial Network (GAN), named Temporal Shifting GAN (TSGAN),incorporates a siamese encoder in both the Generator and the Discriminator. Toprevent the model from overfitting on the input SAR data, we employed a changeweighted loss function. Our approach surpasses traditional translation methodsby eliminating the GAN's fiction phenomenon, particularly in unchanged regions,resulting in higher SSIM and PSNR in these areas. Additionally, modificationsto the Pix2Pix architecture and the inclusion of attention mechanisms haveenhanced the model's performance on all regions of the data. This researchpaves the way for leveraging legacy optical datasets, the most abundant andlongstanding source of Earth imagery data, extending their use to SAR domainsand temporal analyses. To foster further research, we provide the code,datasets used in our study, and a framework for generating paired SAR-Opticaldatasets for new regions of interest. These resources are available ongithub.com/moienr/TemporalGAN

TSGAN: An Optical-to-SAR Dual Conditional GAN for Optical based SAR Temporal Shifting