  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 by leveraging
overlapping areas in L1B images produced by adjacent CMOS detectors, thus not
requiring HR ground truth. Our self-supervised loss is designed to enforce the
super-resolved output image to have all the bands correctly aligned. This is
achieved via a novel cross-spectral registration network (CSR) which computes
an optical flow between images of different spectral bands. The CSR network is
also trained with self-supervision using an Anchor-Consistency loss, which we
also introduce in this work. We demonstrate the performance of the proposed
approach on synthetic and real L1B data, where we show that it obtains
comparable results to supervised methods.
