A full-resolution training framework for Sentinel-2 image fusion
This addresses the need for high-resolution satellite imagery in remote sensing applications, but it appears incremental as it builds on existing fusion and unsupervised learning techniques.
The authors tackled the problem of super-resolution for Sentinel-2 satellite images by developing an unsupervised training framework that fuses 10-m and 20-m bands without resolution downgrade, showing promising results compared to supervised methods in preliminary experiments.
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.