A simulation of the synthetic aperture radar image based on improved CycleGAN

The cross-modal data of targets is of great significance to the improvement of the performance of cross-modal detection and multi-modal fusion algorithms based on deep neural networks. Due to the particularity of SAR images, the cost of obtaining paired data is very high, and most of the existing SAR image generation algorithms focus on improving image diversity and small-scale scene generation, and rarely involve image pairing conversion for specific scenes. In this paper, the improved cycle consistency against network CycleGAN is used to achieve the simulation of SAR images of SAR image targets and scenes, and the least square loss is used to improve the network, which improves the network performance and improves the imaging quality. The simulation experiment of SAR image is carried out. The results show that the method produced in this paper has the best fineness and stability, and achieves better simulation results.