Remote Sensing Image Super-Resolution Reconstruction based on Generative Adversarial Network

Abstract

The super-resolution reconstruction algorithm based on generative adversarial network (GAN) can generate realistic texture in the super-resolution process of a single remote sensing image. In order to further improve the visual quality of the reconstructed image, this paper will improve the generation network, discrimination network, and perceptual loss of the generated confrontation network. Firstly, the batch normalization layer is removed and dense connections are used in the residual blocks, which effectively improves the performance of the generated network. Then, we use the relative discriminant network to learn more detailed texture. Finally, we obtain the perception loss before the activation function to maintain the consistency of brightness. In addition, transfer learning is used to solve the problem of insufficient remote sensing data. The experimental results show that the proposed algorithm has superiority in the super-resolution reconstruction of remote sensing images and can obtain better subjective visual effects