Hybrid GAN and Fourier Transformation for SAR Ocean Pattern Image Augmentation

Synthetic Aperture Radar (SAR) images provide valuable information for ocean observation and analysis, aiding the understanding of oceanic processes and their role in climate change. This study presents a Generative Adversarial Networks (GANs)-based approach for generating realistic and diverse ocean-pattern SAR images. The proposed methodology combines a style-based generator network with an adversarial discrimina-tor network to learn and reproduce complex patterns present in SAR images. To avoid discriminator overfitting due to insufficient training data, an adaptive discriminator augmentation (ADA) mechanism has been exploited. However, the generated images lacked physical properties in the frequency domain. To address this issue, we modified the loss functions by adding the mean square error of the generated and real images in the frequency domain, improving the spectrum of the generated images.