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L1BSR: Exploiting Detector Overlap for Self-Supervised Single-Image Super-Resolution of Sentinel-2 L1B Imagery. (arXiv:2304.06871v1 [cs.CV])
cs.CV updates on arXiv.org arxiv.org
High-resolution satellite imagery is a key element for many Earth monitoring
applications. Satellites such as Sentinel-2 feature characteristics that are
favorable for super-resolution algorithms such as aliasing and
band-misalignment. Unfortunately the lack of reliable high-resolution (HR)
ground truth limits the application of deep learning methods to this task. In
this work we propose L1BSR, a deep learning-based method for single-image
super-resolution and band alignment of Sentinel-2 L1B 10m bands. The method is
trained with self-supervision directly on real L1B data …
algorithms alignment application applications arxiv data deep learning earth feature image monitoring satellite satellites sentinel work