AI RESEARCH
Self-Supervised Super-Resolution for Sentinel-5P Hyperspectral Images
arXiv CS.CV
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ArXi:2604.17652v1 Announce Type: new Sentinel-5P (S5P) plays a critical role in atmospheric monitoring; however, its spatial resolution limits fine-scale analysis. Existing super-resolution (SR) approaches rely on supervised learning with synthetic low-resolution (LR) data, since true high-resolution (HR) data do not exist, limiting their applicability to real observations. We propose a self-supervised hyperspectral SR framework for S5P that enables