SAR Image Colorization Using Multidomain Cycle-Consistency Generative Adversarial Network

Synthetic aperture radar (SAR) images are widely used for aerial and spatial image applications. However, Most of SAR images are usually grayscale images with no color information. Hence, the study of SAR image colorization is meaningful. At present, deep learning has become the mainstream method of SAR coloring, and with the most advanced pix2pix method, it achieves satisfactory results. However, such an approach is limited to the corresponding paired data, which may be difficult to get. We then notice that the cycle-consistency loss can remove this constraint to some extent. In this letter, we present a novel method to colorize the SAR image using a multidomain cycle-consistency generative adversarial network (MC-GAN). The proposed method improves the performance of coloring SAR images from two aspects: first, we propose a mask vector for images of every particular terrain combined with cycle-consistency loss, which does not need the paired SAR-optical images to train the model. Second, we define the multidomain classification loss, which can together get the correct output image with the color we hope it to be. We examined the proposed method on the newly SEN1-2 data set compared with the pix2pix and CycleGAN methods, which demonstrates the effectiveness of our proposed method.