An Adversarial Training Framework for Sentinel-2 Image Super-Resolution

In this work is presented a new adversarial training framework for deep learning neural networks for super-resolution of Sentinel 2 images, exploiting the data fusion techniques on 10 and 20 meters bands. The proposed scheme is fully convolutional and tries to answer the need for generalization in scale, producing realistic and detailed accurate images. Furthermore, the presence of a mathcal{L}-{1} loss limits the instability of GAN training, limiting possible problems of spectral dis-tortion. In our preliminary experiments, the GAN training scheme has shown comparable results in comparison with the baseline approach.