Remote sensing image super-resolution using cascade generative adversarial nets.

Image super-resolution (SR) is a widely used and cost-effective technology in remote sensing image processing. Deep learning-based SR methods have shown promising performance, but they are prone to losing texture details. Instead, generative adversarial nets (GAN)-based methods can generate more visually acceptable results. However, GAN-based SR methods are suffering from scene variance and uncontrollable performance of discriminators as well as unstable training. Besides, both these two methods cannot yield arbitrary high-time SR images. To solve these issues, we propose a novel SR method for remote sensing images using C ascade G enerative A dversarial N ets (CGAN) with introduction of content fidelity and scene constraint, which can achieve arbitrary high-time high-quality SR image. More specifically, the scene-constraint item is incorporated to constrain generated feature for avoiding the risk of scene change. Then, content fidelity is introduced to stabilize the training and avoid gradient vanishing. Besides, an edge enhancement module is designed to preserve edge detail and suppress the noise. CGAN with these three components has achieved higher quality SR results than other recent state-of-the-art methods. Compared with these methods, our proposed method outperformed average increments of 7.3% SSIM, 7.3% FSIM and 6.0% MSIM on WHU-RS19 and NWPU-RESISC45 datasets. In addition, the evaluation of GAN-train and GAN-test gained average increments of 6.3% and 4.5% on the WHU-RS19 and AID datasets, respectively. [ABSTRACT FROM AUTHOR]