Gan-based ocean pattern sar image augmentation

Synthetic Aperture Radar (SAR) image generation using Generative Adversarial Networks (GANs) has gained significant attention in recent years. In addition, the ocean plays a crucial role in regulating Earth's climate system. SAR images provide valuable information for ocean observation and analysis, aiding in the understanding of oceanic processes and their role in climate change. This study presents a GAN-based approach for generating realistic and diverse ocean pattern SAR images. The proposed methodology combines a style-based generator network with an adversarial discriminator network to learn and reproduce the complex and unique patterns present in SAR images. In order to avoid discriminator overfitting, which frequently occurs as a result of insufficient training data, an adaptive discriminator augmentation (ADA) mechanism has been exploited. By training the GAN with ADA, the generator learns to capture the spatial and statistical properties of oceanic phenomena in restricted data regimes. The experimental results demonstrate the effectiveness and potential of the proposed GAN-based approach for ocean pattern SAR image generation, opening new avenues for advancing ocean observation and analysis in the context of climate change mitigation.