  Earth observing satellites carrying multi-spectral sensors are widely used to
monitor the physical and biological states of the atmosphere, land, and oceans.
These satellites have different vantage points above the earth and different
spectral imaging bands resulting in inconsistent imagery from one to another.
This presents challenges in building downstream applications. What if we could
generate synthetic bands for existing satellites from the union of all domains?
We tackle the problem of generating synthetic spectral imagery for
multispectral sensors as an unsupervised image-to-image translation problem
with partial labels and introduce a novel shared spectral reconstruction loss.
Simulated experiments performed by dropping one or more spectral bands show
that cross-domain reconstruction outperforms measurements obtained from a
second vantage point. On a downstream cloud detection task, we show that
generating synthetic bands with our model improves segmentation performance
beyond our baseline. Our proposed approach enables synchronization of
multispectral data and provides a basis for more homogeneous remote sensing
datasets.
