Zero-Shot Sentinel-2 Sharpening Using a Symmetric Skipped Connection Convolutional Neural Network

Sentinel-2 (S2) satellite constellations can provide multispectral images of 10 m, 20 m, and 60 m resolution for visible, near-infrared (NIR) and short-wave infrared (SWIR) in the electromagnetic spectrum. In this paper, we present a sharpening method based on a symmetric skipped connection convolutional neural network, called SSC-CNN, to sharpen 20 m bands using 10 m bands. The main advantage of SSC-CNN architecture is that it brings the features of the input branch to the output, thus improving convergence without using too many deep layers. The proposed method uses the reduced-scale combination of 10 m bands and 20 m bands, and the observed 20 m bands as the training pairs. The experimental results using two Sentinel-2 datasets show that our method outperforms competitive methods in quantitative metrics and visualization.