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.

翻译：本文提出了一种无监督框架，用于训练深度学习模型实现哨兵二号10米与20米波段融合的超分辨率重建。该方案避免了监督学习中生成训练数据所需的降分辨率处理过程。另一方面，我们提出了一个考虑网络预测结果与待融合输入分量之间循环一致性的适当损失函数。尽管采用无监督方式，初步实验表明，该方案在与监督方法的对比中展现了良好表现。此外，通过所提损失函数的构造，训练得到的网络可归入多分辨率分析方法类别。