Data augmentation is essential for improving deep learning performance with limited data. This thesis examines whether class-conditional Denoising Diffusion Probabilistic Models (DDPMs) can enhance satellite image classification on the EuroSAT dataset. Using a U-Net-based DDPM, we generated synthetic images for ten land cover classes and evaluated ResNet-18 with different real-to-synthetic ratios. Results show that geometric transformations consistently outperform synthetic data, which often degrades performance, especially at higher proportions. However, hybrid approaches improved specific classes, such as AnnualCrop (+2.65 points). Overall, geometric augmentation remains most effective, though class-dependent synthetic strategies show potential for targeted enhancement.
