![]() in November 2016, which can generate high-quality images by combing generator of U-Net and discriminator of PatchGAN. In particular, image-to-image translation network proposed by Isola et al. However, considering that this method is unsupervised learning, the resulting image lacks marking information.įor the generated data lacking tag information with unsupervised GAN, people have carried out GAN's semi-supervised and supervised research in recent years. Generated images are of higher quality and have a higher degree of similarity to real SAR images. In, more realistic SAR images are generated based on DCGANS. GAN has relatively few applications in SAR image generation. To increase the stability, convolutional networks and batch normalisation are introduced to deep convolutional GANs (DCGANs) is proposed. However, the original GAN training process is unstable. Through the training of the generator and discriminator, the image generated by the final generator can successfully deceive the discriminator. Generative adversarial network (GAN) is composed of generator and discriminator. ![]() In recent years, in the field of deep learning, Goodfellow proposed a deep generation model for generating confrontation networks and then generated data similar to the original data distribution by fitting the data distribution. However, considering that there is an error in the model of the target and there is an approximation to the process of the target RCS, there is a certain deviation between the SAR image obtained by this method and the real SAR image. The cost of acquiring SAR images through electromagnetic simulation is low, and all angles of SAR images can be obtained through simulation. However, the real SAR image acquisition process is limited by the radar observation angle, and there is a lack of target image acquisition angle. The images provide more information and detection accuracy has improved. In the field of deep learning, continuous frame images are used as training set ratios. In the traditional field, multi-angle fusion can overcome the influence of unfavourable factors such as occlusion, top-bottom inversion, and shadow in a single-angle SAR observation, and achieve better target reconstruction. Therefore, it is of great significance to achieve multi-angle fusion of the target. Different from the optical image, the SAR image reflects the scattering characteristics of the target, and the imaging results of the same target at different angles differ greatly. In recent years, with the development of SAR, SAR has played an increasingly important role in the military and civil fields. Synthetic aperture radar (SAR) enables full-time, all-weather, high-resolution imaging. ![]()
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