A SAR-to-Optical Image Translation Method Based on Conditional Generation Adversarial Network (cGAN)

Abstract

Synthetic Aperture Radar (SAR) remote sensing images with all-day and all-weather advantages are attracting ever-increasing attention in various areas. However, the interpretation of SAR images is quite challenging and not adapted to the non-expert people. To improve the interpretation of SAR images, a SAR-to-optical image translation method based on a modified conditional generation adversarial network (cGAN) is proposed. In the proposed method, the characteristics of the SAR and optical images are comprehensively considered. To generate better results, a modified strongly constrained cGAN, with the generator and the discriminator networks, based on structure similarity index measure (SSIM) and L1 norm was constructed. The generator network aims to generate an artificial optical image by SAR, and the discriminator network aims to force the generated image to be close to the real optical image. The proposed method was verified and compared with several state-of-the-art methods, and the experimental results show the superiority of the proposed method.

Keywords

• SAR-to-optical translation
• conditional generation adversarial network (cGAN)
• deep learning
• synthetic aperture radar (SAR) images
• optical images