Super Resolution Approach for the Satellite Data Based on the Generative Adversarial Networks

Abstract

In the past few years, medium and high-resolution data became freely available for downloading. It provides great opportunity for researchers not to select between solving the task with high-resolution data on small territory or on global scale, but with low-resolution satellite images. Due to high spectral and spatial resolution of the data, Sentinel-1 and Sentinel-2 are very popular sources of information. Nevertheless, in practice if we would like to receive final product in 10 m resolution we should use bands with 10 m resolution. Sentinel-2 has four such bands, but also has other bands, especially red-edge 20 m resolution bands that are useful for vegetation analysis and often are omitted due to lower resolution. Thus, in this study we propose methodology for enhancing resolution (super-resolution) of the existing low-resolution images to higher resolution images. The main idea is to use advanced methods of deep learning - Generative Adversarial Networks (GAN) and train it to increase the resolution for the satellite images. Experimental results for the Sentinel-2 data showed that this approach is efficient and could be used for creating high resolution products.