FG-GAN: A Fine-Grained Generative Adversarial Network for Unsupervised SAR-to-Optical Image Translation

Abstract

Synthetic aperture radar (SAR) and optical sensing are two important means of Earth observation. SAR can be used for all-day and all-weather Earth observation, but it has the disadvantages of speckle noise and geometric distortion, which are not conducive to human eye recognition. Optical image conforms to the characteristics of human visual observation, but it is easily affected by climate and time. Therefore, to integrate the advantages of the two, researchers have carried out extensive work on SAR-to-optical (S2O) image translation. Most of the existing methods for S2O image translation are supervised and need paired training samples, limiting its large-scale application in remote sensing field. Thus, we give priority to an unsupervised S2O image translation method. Meanwhile, we find that the images generated by unsupervised methods suffer from significant detail deficiencies. To solve this problem, we propose a fine-grained generative adversarial network (FG-GAN) introducing three strategies to enhance the detailed information in generated optical images. First, we design an unbalanced generator (UBG) with complex encoder networks and relatively simple decoder networks. The complex encoder extracts abundant feature information, while the decoder obtains key details by filtering these features. Second, to match the learning ability of the generator, we present a multiscale discriminator (MSD) to enhance the discriminant ability of the network. Third, we propose a comprehensive normalization group (CNG) to promote the physical representation consistency of SAR and optical images. Extensive experiments have been conducted, and the results show that our method is superior to the state-of-the-art (SOTA) methods on both subjective and objective evaluation indicators. Moreover, our FG-GAN has a significant improvement on classification accuracy, indicating its potential in facilitating the performance of practical remote sensing tasks.