  Recent advances have shown how deep neural networks can be extremely
effective at super-resolving remote sensing imagery, starting from a
multitemporal collection of low-resolution images. However, existing models
have neglected the issue of temporal permutation, whereby the temporal ordering
of the input images does not carry any relevant information for the
super-resolution task and causes such models to be inefficient with the, often
scarce, ground truth data that available for training. Thus, models ought not
to learn feature extractors that rely on temporal ordering. In this paper, we
show how building a model that is fully invariant to temporal permutation
significantly improves performance and data efficiency. Moreover, we study how
to quantify the uncertainty of the super-resolved image so that the final user
is informed on the local quality of the product. We show how uncertainty
correlates with temporal variation in the series, and how quantifying it
further improves model performance. Experiments on the Proba-V challenge
dataset show significant improvements over the state of the art without the
need for self-ensembling, as well as improved data efficiency, reaching the
performance of the challenge winner with just 25% of the training data.
