Data Augmentation Method of SAR Image Dataset Based on Wasserstein Generative Adversarial Networks

The published Synthetic Aperture Radar (SAR) samples are not abundant enough, which is not conducive to the application of deep learning methods in the field of SAR automatic target recognition. Generative Adversarial Nets (GANs) is one of the most effective ways to generate image samples. In this manuscript, the gradient penalty method was used on the traditional DCGAN to solve the mode collapse problem. The improved model was used for data augmentation of the MSTAR dataset. All the experimental results show that the method was an effective way to generate SAR images with high quality.