MCWESRGAN: Improving Enhanced Super-Resolution Generative Adversarial Network for Satellite Images

With the dynamic technological development, we are witnessing a major progress in solutions that allow for the observation of Earth's surface. Small satellites have a significant drawback. Due to their limitations, the installed optic systems are not perfect. As a result, the quality of the obtained images is lower, including lower resolution, although the satellites move on the Low Earth Orbit. In the case of images lacking a high-resolution counterpart, the spatial resolution of the imagery can be improved using single-image super-resolution algorithms. In this article, we present an SISR solution based on a new network called MCWESRGAN, which is a modification of the popular ESRGAN network. We propose a novel strategy that introduces a multi-column discriminator model. The generator model is trained using Wasserstein loss. The introduced modifications enable a tenfold reduction in the training time of the network. The proposed algorithm is verified using images obtained from space, aerial imagery, and the Dataset for Object deTection in Aerial Images (DOTA) database. A set of evaluation methods for super-resolution (SR) images is proposed to verify the results. These evaluation methods indicate areas that are poorly estimated by the algorithm. Furthermore, as part of the conducted experiments, an absolute assessment method for interpretational potential based on the power spectral density of the image (PSD) is proposed, allowing for determining the magnitude of interpretational improvement after applying resolution enhancement algorithms. The conducted research demonstrates that we achieve better qualitative and quantitative results than classical ESRGAN methods and other state-of-the-art (SOTA) approaches.