Sentinel-2 Image Generation via Stylegan3 Model

Satellite imagery is crucial for remote sensing, allowing observation of Earth from various platforms with diverse payloads. These images have been extensively collected and utilized in civil and military applications. While relied upon for analysis and decision-making, satellite images are susceptible to manipulation. Advancements in image generation have led to sophisticated techniques, such as Generative Adversarial Neural Networks (GANs), capable of creating realistic synthetic images. However, there is limited research on applying GANs to satellite imagery. This study focuses on training the state of the art StyleGAN3 model on multispectral Sentinel-2 data to generate synthetic data.