SAR-to-Optical Image Translating Through Generate-Validate Adversarial Networks

Synthetic aperture radar (SAR) has the advantages of high resolution in all-weather and all-day. However, SAR images are hard to be understood, due to their unique imaging mechanism. The SAR to optical image translation can assist in interpreting and has become a topic of growing interest in the field of remote sensing. In this letter, a SAR to optical image translation network is proposed, called generate-validate adversarial networks (GVANs). More specifically, there are two Pix2Pix networks form the cyclic structure. The validate module is employed to increase the training process and improve the edge retention ability. In order to improve multidomain images adaptability, the embedded layer is proposed. Additionally, the dilation convolution layer is employed in the generator, which is more suitable for the characteristics of SAR images. The proposed method has experimented on the SEN1-2 dataset. The result demonstrates the superiority of the proposed method over state-of-the-art methods.