Diffusion Models with Cross-Modal Data for Super-Resolution of Sentinel-2 To 2.5 Meter Resolution

Abstract

Diffusion models have obtained photo-realistic results on various super-resolution tasks. However, existing approaches typically require the availability of high-resolution and paired training data, which often is not readily available in many remote sensing scenarios. To enhance multi-spectral Sentinel 2 (S2) satellite images – at a ground sampling distance (GSD) ranging from 10m to 60m – without requiring high-resolution or paired training data, we therefore propose and evaluate a novel set of approaches to leverage traditional pansharpening within a diffusion model context to simulate the required training data. We extensively compare the proposed methods and demonstrate that by utilizing unpaired Spot-6/7 data, we are able to produce photo-realistic S2 images at a resolution of 2.5m.