An unpaired SAR-to-optical image translation method based on Schrödinger bridge network and multi-scale feature fusion.

SAR-to-optical (S2O) translation is able to covert SAR into optical images, which help the interpreter to extract information efficiently. In the absence of strictly matched datasets, it is difficult for existing methods to complete training on unpaired data with a minimum amount of data. By employing the recent Schrödinger bridge-based transformation framework, a multiscale axial residual module (MARM) based on the concept of multi-scale feature fusion has been proposed in this paper. To enable efficient translation of SAR to optical images, the generator and discriminator of the model have been designed. Extensive experiments on the SEN1-2 dataset conducted, and the results show the superiority of the proposed method in terms of the generation quality. Compared with the classical CycleGAN, the proposed method can improve the FID metrics by 42.05%.