Generating simulated SAR images using Generative Adversarial Network

Synthetic aperture radar (SAR) is a microwave imaging equipment based on the principle of synthetic aperture, which has all kinds of characteristics such as all-time, all-weather, high resolution and wide breadth. It also has high research value and applied foreground in the area of military and civilian. In particular, worldwide, a great deal of researches on SAR target classification and identification based Deep Learning are ongoing, and the obtained results are highly effective. However, it is well known that Deep Learning requires a large amount of data, and it is costly and inaccessible to acquire SAR samples through field experiment, so image simulation research for expanding SAR dataset is essential. In this paper, we concentrated on generating highly realistic SAR simulated images for several equipment models using Generative Adversarial Network (GAN) without construction of terrain scene model and RCS material mapping. Then we tested the SAR simulated images on a specialized SAR classification model pretrained on MSTAR dataset. The results showed that simulated targets could be identified and classified accurately, demonstrating the high similarity of SAR simulated images with real samples. Our work could provide a greater variety of available SAR images for target classification and identification study.