DiffFuSR: Super-Resolution of all Sentinel-2 Multispectral Bands using Diffusion Models
This work addresses the problem of enhancing low-resolution multispectral satellite imagery for remote sensing applications, offering a modular solution with significant performance gains over existing methods.
The paper tackles super-resolving all 12 spectral bands of Sentinel-2 imagery to 2.5 meters by proposing DiffFuSR, a two-stage pipeline using diffusion models and fusion networks, which outperforms state-of-the-art baselines on the OpenSR benchmark in metrics like reflectance fidelity and spectral consistency.
This paper presents DiffFuSR, a modular pipeline for super-resolving all 12 spectral bands of Sentinel-2 Level-2A imagery to a unified ground sampling distance (GSD) of 2.5 meters. The pipeline comprises two stages: (i) a diffusion-based super-resolution (SR) model trained on high-resolution RGB imagery from the NAIP and WorldStrat datasets, harmonized to simulate Sentinel-2 characteristics; and (ii) a learned fusion network that upscales the remaining multispectral bands using the super-resolved RGB image as a spatial prior. We introduce a robust degradation model and contrastive degradation encoder to support blind SR. Extensive evaluations of the proposed SR pipeline on the OpenSR benchmark demonstrate that the proposed method outperforms current SOTA baselines in terms of reflectance fidelity, spectral consistency, spatial alignment, and hallucination suppression. Furthermore, the fusion network significantly outperforms classical pansharpening approaches, enabling accurate enhancement of Sentinel-2's 20 m and 60 m bands. This study underscores the power of harmonized learning with generative priors and fusion strategies to create a modular framework for Sentinel-2 SR. Our code and models can be found at https://github.com/NorskRegnesentral/DiffFuSR.