IESRGAN: Enhanced U-Net Structured Generative Adversarial Network for Remote Sensing Image Super-Resolution Reconstruction

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2023-07, Vol.15 (14), p.3490
• Main Authors: Yue, Xiaohan, Liu, Danfeng, Wang, Liguo, Benediktsson, Jón Atli, Meng, Linghong, Deng, Lei
• Format: Article
• Language: English
• Subjects: Algorithms; Deep learning; Discriminators; Generative adversarial networks; Image enhancement; Image processing; Image quality; Image reconstruction; Image resolution; Methods; Neural networks; Performance evaluation; Remote sensing; remote sensing images; Satellite imagery; Satellite technology; super-resolution reconstruction; Technology application
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15143490

With the continuous development of modern remote sensing satellite technology, high-resolution (HR) remote sensing image data have gradually become widely used. However, due to the vastness of areas that need to be monitored and the difficulty in obtaining HR images, most monitoring projects still rely on low-resolution (LR) data for the regions being monitored. The emergence of remote sensing image super-resolution (SR) reconstruction technology effectively compensates for the lack of original HR images. This paper proposes an Improved Enhanced Super-Resolution Generative Adversarial Network (IESRGAN) based on an enhanced U-Net structure for a 4× scale detail reconstruction of LR images using NaSC-TG2 remote sensing images. In this method, in-depth research has been performed and consequent improvements have been made to the generator and discriminator within the GAN network. Specifically, before introducing Residual-in-Residual Dense Blocks (RRDB), in the proposed method, input images are subjected to reflective padding to enhance edge information. Meanwhile, a U-Net structure is adopted for the discriminator, incorporating spectral normalization to focus on semantic and structural changes between real and fake images, thereby improving generated image quality and GAN performance. To evaluate the effectiveness and generalization ability of our proposed model, experiments were conducted on multiple real-world remote sensing image datasets. Experimental results demonstrate that IESRGAN exhibits strong generalization capabilities while delivering outstanding performance in terms of PSNR, SSIM, and LPIPS image evaluation metrics.