Gan-Based Sar-To-Optical Image Translation With Region Information

In this paper, we propose a SAR-to-optical image translation method based on conditional generative adversarial networks (cGANs). Though cGANs have achieved great success in image translation, some problems remain in SAR-to-optical image translation. One of the problems is the colorization error owing to the lack of color information in SAR data. Since the colors of optical images are varied, while SAR images have no color information, the generator network is confused and fail to generate correctly colorized optical images. To prevent it, we introduce a region information to the image translation network. Specifically, the feature vector from the pre-trained classification network is fed to the generator and discriminator network. Experimental results with SEN1-2 dataset show the advantage of our proposed method over the baseline method that does not use any additional information.