A full-resolution training framework for Sentinel-2 image fusion
Matteo Ciotola Mario Ragosta Giovanni Poggi Giuseppe Scarpa
This work presents a new unsupervised framework for training deep learning
models for super-resolution of Sentinel-2 images by fusion of its 10-m and 20-m
bands. The proposed scheme avoids the resolution downgrade process needed to
generate training data in the supervised case. On the other hand, a proper loss
that accounts for cycle-consistency between the network prediction and the
input components to be fused is proposed. Despite its unsupervised nature, in
our preliminary experiments the proposed scheme has shown promising results in
comparison to the supervised approach. Besides, by construction of the proposed
loss, the resulting trained network can be ascribed to the class of
multi-resolution analysis methods.