Exploring the Potential of Unsupervised Image Synthesis for SAR-Optical Image Matching

We consider SAR-optical image matching problems, where correspondences are acquired from a pair of SAR and optical images. Recent methods for such a problem typically simplify the SAR-optical image matching to the SAR-SAR or optical-optical image matchings using supervised-image-synthesis methods. However, training supervised-image-synthesis needs plenty of aligned SAR-optical image pairs while gathering sufficient amounts of aligned multi-modal image pairs is challenging in remote sensing. In this work, we investigate the applicability of unsupervised-image-synthesis for SAR-optical image matching such that the unaligned SAR-optical images could be used. To this end, we apply feature matching loss to a well known unsupervised-image-synthesis method, i.e., CycleGAN, to enforce the feature matching consistency. Moreover, we develop a shared-matching-strategy to improve the results of SAR-optical image matching further. Qualitative comparisons against CycleGAN, StarGAN, and DualGAN demonstrate the superiority of our approach. Quantitative results show that, compared with CycleGAN, StarGAN, and DualGAN, our method obtains at least 2.6 times more qualified SAR-optical matchings.