Research on Super-resolution Reconstruction Algorithm of Remote Sensing Image Based on Generative Adversarial Networks

Due to natural conditions and hardware manufacturing processes, the resolution of remote sensing images is generally low. Obtaining high-definition remote sensing images by simply improving hardware and manufacturing processes is not only costly and technically challenging but also cannot be deployed on a large scale. Aiming at the limitations of the traditional methods, this paper studies the image super-resolution reconstruction method for improving the generated anti-network. Firstly, the generator network is optimized, and an RRDB (Residual-in-Residual Dense without BN (Batch Normalization) is used. Block) module; secondly, the related idea of relativistic GAN (relativistic generative adversarial network) is introduced, the relative value of the discriminator is not the absolute value; finally, the sensation loss is improved, and the feature is used before the function is activated. The test results show that the proposed algorithm is better than SRGAN (Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network), SRCNN (Super-Resolution Convolutional Neural Network) and FSRCNN (Fast Super-Resolution Convolutional Neural Network). The clarity of the reconstructed image is improved, and the reconstructed image quality is significantly improved.