Mind the Gap: Quantifying the Domain Gap in Cross-Sensor Diffusion Super-Resolution

• URL: http://arxiv.org/abs/2606.28039v1
• Date: Fri, 26 Jun 2026 12:43:42 GMT
• Title: Mind the Gap: Quantifying the Domain Gap in Cross-Sensor Diffusion Super-Resolution
• Abstract summary: Super-resolution satellite imagery can bridge the spatial resolution gap between freely available missions like Sentinel-2 and commercial systems like PlanetScope.<n>Because no sensor provides true paired low- and high-resolution observations, SR models are usually trained on synthetically degraded data.<n>We provide the first systematic study of how this synthetic-to-real mismatch affects the performance of modern diffusion-based SR models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Demand for high-resolution satellite imagery has increased interest in super-resolution (SR) to bridge the spatial resolution gap between freely available missions such as Sentinel-2 and commercial systems like PlanetScope. Because no sensor provides true paired low- and high-resolution observations, SR models are usually trained on synthetically degraded data, creating a domain gap on real cross-sensor imagery. In this work, we provide the first systematic study of how this synthetic-to-real mismatch affects the performance of modern diffusion-based SR models. Using a large, geometrically and temporally aligned dataset of Sentinel-2 and PlanetScope imagery, we evaluate five state-of-the-art diffusion architectures under controlled experimental settings. We also introduce LPIPS-Sat, a domain-adapted perceptual metric based on Sentinel-2 self-supervised features. Our results show two persistent challenges: synthetically trained models degrade sharply on real pairs, while models trained on real cross-sensor data exhibit optimisation difficulties and struggle to adapt to the physical and radiometric diversity. These findings highlight a key limitation of current SR and motivate methods that disentangle super-resolution from domain adaptation.

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