In this paper, we introduce a new generative adversarial network (GAN) with dual image-color discriminators, to predict Artificial-SAR colorized images from SAR ones (Sentinel-1). Based on the conventional architecture of GANs, we employ an additional color discriminator that evaluates the differences in brightness, contrast, and major colors between images, while the image discriminator compares texture and content. To achieve the required level of colorization in the generation process, we employ non-adversarial color loss dedicated for color comparison, unlike conventional approaches that use only L 1 loss. Moreover, to overcome the vanishing gradient problem in deep architecture, and ensure the flow of low-level information inside network layers, we add residual connections to our generator that follows the general shape of U-Net. The performance of the proposed model was evaluated quantitatively as well as qualitatively with the SEN1-2 dataset. Results show that the proposed model generates realistic colorized images with fewer artifacts compared to the state-of-the-art approaches. This model helps to maintain color steadiness as well as visual recognizability at less textured large continuous regions, such as plantation and water areas, when it's difficult to be distinguished in SAR images.