Increasing the Spatial Resolution of Panchromatic Satellite Images Based on Generative Neural Networks

Generative adversarial neural networks are used to increase the resolution of satellite images of a certain class without additional data. The quality of the obtained high-resolution images is assessed by the signal-to-noise ratio and the measure of structural similarity. Based on the known loss functions used in generative adversarial neural networks, a function specific to the problem being solved is obtained. Training and testing is carried out on the example of images of a railway infrastructure, covering about 78 km of railways.