Abstract
Synthetic aperture radar (SAR) is a powerful imaging sensor capable of operating in any weather and at any time, despite its image interpretation poses challenges. Translating SAR images to optical (S2O) can serve as a beneficial supplement for target detection, image alignment, and image fusion. The existing S2O translation employs solely unsupervised or supervised techniques. This research presents a semi-supervised S2O generating approach that can obtain domain-level and pixel-level mapping relationships. A dual-branch cycle consistency loss has been developed to independently constrain supervised and unsupervised modules. To improve global and sparsity feature extraction, the generator combines sliding dilated convolution with multi-respective parallel branching. Using just 1,100 pairs of training sets, extensive experiments have demonstrated that the suggested strategy outperforms baseline methods in both subjective and objective standards, where Fréchet inception distance (FID) is optimized by 23.41%, 49.25%, 48.57%, 16.73%, and 42.32% on the five land classes.