Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (21): 199-203.DOI: 10.3778/j.issn.1002-8331.1909-0249

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Research on Super-Resolution Reconstruction of Remote Sensing Image Based on Improved Conditional Generative Adversarial Networks

JIANG Wenjie, LUO Xiaoshu, DAI Qinxuan   

  1. College of Electronic Engineering, Guangxi Normal University, Guilin, Guangxi 541004, China
  • Online:2020-11-01 Published:2020-11-03



  1. 广西师范大学 电子工程学院,广西 桂林 541004


Image resolution is an important indicator to measure the quality of remote sensing images. It is limited by imaging equipment and transmission conditions. The resolution of traditional remote sensing images is difficult to guarantee. To solve the above problems, a super-resolution reconstruction of remote sensing image based on conditional generation confrontation network is proposed. In order to speed up the convergence of the model, the combination of content loss and anti-loss is used as the objective function in the generator network. In addition, in order to improve the stability of network training, a gradient penalty function is introduced in the discriminator network to limit the discriminator gradient. The experimental results show that the improved model has significant improvement in subjective visual effects and objective evaluation indicators compared with SRCNN, FSRCNN and SRGAN models.

Key words: remote sensing image, super-resolution, conditional generative adversarial networks, gradient penalty



关键词: 遥感图像, 超分辨率, 条件生成对抗网络, 梯度惩罚