A remote sensing image super-resolution model using SRGAN

With the increasing application of remote sensing images, people have higher requirements for images’ quality. Therefore, improving remote sensing images’ resolution is of great significance. With the development of machine learning, neural networks (especially SRGAN) have significant advantage in single image super-resolution research. The SRGAN model was used to enhance remote sensing images’ resolution in the study. SRGAN’s generator used residual blocks, which improved SRGAN’s performance. SRGAN outperformed traditional algorithms in subjective visual perception and objective evaluation metrics such as PSNR and SSIM. Compared to SRCNN, SRGAN improved PSNR by 1.809 dB and SSIM by 0.015. In addition, the super-resolution image quality was significantly improved, which fully demonstrated the effectiveness of the proposed method.