DDM-CGAN: a modified conditional generative adversarial network for SAR target image generation

In recent years, Generative Adversarial Network (GAN) have shown great potential and achieved excellent performance on the task of generating optical images. However, when GAN is applied to Synthetic Aperture Radar (SAR) images, the differences in imaging mechanisms between SAR images and optical images make GAN susceptible to training instability and model collapse problems during training. In this paper, we propose a new end-to-end model called Dual Discriminator Modified Conditional Generative Adversarial Network (DDM-CGAN) to address these issues. First, two discriminators are designed to play an adversarial game against the generator in DDM-CGAN. One discriminator favors samples from real data, while the other rewards high scores for generated samples. Essentially, we designed a novel objective function by utilizing the dual discriminator to combine the respective advantages of alternative cost function and original cost function of the standard GAN. We theoretically prove that this objective function can optimize DDM-CGAN towards minimizing the Kullback-Leibler Divergence, thus avoiding the problem of non-convergence during model training. Second, we propose a modified gradient penalty, which makes the model training more stable. In addition, the introduction of a discriminative auxiliary classifier provides the generator with information about the target distribution, thus improving the diversity of the generated images. We perform comprehensive qualitative and quantitative experiments with the Gaofen-3 SAR image dataset. Our proposed DDM-CGAN is compared with the state-of-the-art SAR image generation methods. Experimental results demonstrate that the SAR images generated by DDM-CGAN achieves optimal results in terms of the similarity, statistical characteristics, and diversity.