Generative Adversarial Network with Residual Dense Generator for Remote Sensing Image Super Resolution

Abstract

Improving image resolution, especially spatial resolution, has been one of the most important concerns on remote sensing research communities. An efficient solution for improving spatial resolution is by using algorithm, known as super-resolution (SR). The super-resolution technique that received special attention recently is super-resolution based on deep learning. In this paper, we propose deep learning approach based on generative adversarial network (GAN) for remote sensing images super resolution. We used residual dense network (RDN) as generator network. Generally, deep learning with residual dense network (RDN) gives high performance on classical (objective) evaluation metrics meanwhile generative adversarial network (GAN) based approach shows a high perceptual quality. Experiment results show that combination of residual dense network generator with generative adversarial network training is found to be effective. Our proposed method outperforms the baseline method in terms of objective and perceptual quality evaluation metrics.