  Landsat-8 (NASA) and Sentinel-2 (ESA) are two prominent multi-spectral
imaging satellite projects that provide publicly available data. The
multi-spectral imaging sensors of the satellites capture images of the earth's
surface in the visible and infrared region of the electromagnetic spectrum.
Since the majority of the earth's surface is constantly covered with clouds,
which are not transparent at these wavelengths, many images do not provide much
information. To increase the temporal availability of cloud-free images of a
certain area, one can combine the observations from multiple sources. However,
the sensors of satellites might differ in their properties, making the images
incompatible. This work provides a first glance at the possibility of using a
transformer-based model to reduce the spectral and spatial differences between
observations from both satellite projects. We compare the results to a model
based on a fully convolutional UNet architecture. Somewhat surprisingly, we
find that, while deep models outperform classical approaches, the UNet
significantly outperforms the transformer in our experiments.
