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The Sentinel-2 satellites deliver 13 band multi-spectral imagery with bands having 10 m, 20 m or 60 m spatial resolution. The low-resolution bands can be upsampled to match the high resolution bands to extract valuable information at higher spatial resolution. This paper presents a Wasserstein Generative Adversarial Network (WGAN) based approach named as DSen2-WGAN to super-resolve the low-resolution (i.e., 20 m and 60 m) bands of Sentinel-2 images to a spatial resolution of 10 m. A proposed generator is trained in an adversarial manner using the min-max game to super-resolve the low-resolution bands with the guidance of available high-resolution bands in an image. The performance evaluated using metrics such as Signal Reconstruction Error (SRE) and Root Mean Squared Error (RMSE) shows the effectiveness of the proposed approach as compared to the state-of-the-art method, DSen2 as the DSen2-WGAN reduced RMSE by 14.68% and 7%, while SRE improved by almost 4% and 1.6% for 6and 2super-resolution. Lastly, for further evaluation, we have used trained DSen2-WGAN model to super-resolve the bands of EuroSAT dataset, a satellite image classification dataset based on Sentinel-2 images. The per band classification accuracy of low-resolution bands shows significant improvement after super-resolution using our proposed approach.

Super-resolution of Sentinel-2 images using Wasserstein GAN