The use of Deep Learning (DL) in Earth observation technology has become essential. Even though DL models do remarkably well in classifying high-resolution satellite images and extracting semantic information, they frequently need a lot of training data, which can be costly and time-consuming to obtain. With an emphasis on ocean Synthetic Aperture Radar (SAR) image analysis, this research investigates the use of synthetically generated data using Generative Adversarial Networks (GANs) for data augmentation. Relying on GAN-based generated images for remote sensing applications requires a thorough assessment of the quality and authenticity of the generated images, as well as validation of the model’s performance on real-world data. We assess the diversity and reliability of GAN-generated images by training a classification network on these images and evaluating their performance on real-world data. By comparing the classification accuracy in different experimental setups, we approximate the precision and recall for GANs performance.