ASYMMETRIC TRAINING OF GENERATIVE ADVERSARIAL NETWORK FOR HIGH FIDELITY SAR IMAGE GENERATION

In practical application, the research of synthetic aperture radar (SAR) target recognition has fallen into a bottleneck due to the lack of samples. Data argumentation methods based on generative adversarial networks (GAN) have received widespread attention in solving this type of few-shot sample problem. However, the generated images suffer from various shortcomings, such as lack of diversity, low signal-to-noise ratio, blur, etc. In this article, the VAE-WGANGP is proposed, which combines GAN and variational autoencoder (VAE) to alleviate these shortcomings. The innovations of this paper are as follows: firstly, the generator of GAN is replaced with VAE, which constructs an asymmetric network ensuring the stability of GAN training; secondly, the asymmetric loss function is composed of four parts, including reconstruction loss, divergence loss, adversarial loss, and gradient penalty. In this way, the problem of gradient explosion or gradient disappearance is alleviated. The experimental results with the MSTAR dataset show that the images generated by the proposed model outperform the advanced technology with many similar deep features and achieve significant improvement in the target recognition accuracy rate.