Adaptation of Diffusion Models for Remote Sensing Imagery

The present contribution focuses on applying Denoising Diffusion Probabilistic Models to Remote Sensing image classification, generation and super-resolution. Diffusion models are enhanced, including an attention block embedded in a UNet architecture is used to generate images to complement the EuroSAT data set. Furthermore, models with the same architecture super-resolve sentinel-2 optical images. The results indicate that diffusion models with attention can provide a promising methodological path for applications such as estimating the NDVI images most probably associated with given electro-optical and SAR acquisitions thereby overcoming limitations in observability due to cloud cover or solar illumination.