In response to the issue that remote sensing images have lower resolution than traditional images and are affected by complex degradation processes, traditional generative adversarial networks can generate unrealistic features, leading to problems such as artifacts and a large number of false, sharp edges. This paper proposes an edge extraction and enhancement-based remote sensing image super-resolution network called EEEGAN. The network first employs the edge extraction algorithm TEED to extract image edges. It then designs a dual attention mechanism, TAM, to capture rich spatial and channel information of the image. Additionally, a basic block RRDJB is introduced to expand the model’s processing capabilities, and a downsampling network SPD is incorporated to further reduce detail loss. Based on the RSOD dataset, different data degradation treatments were applied according to degradation models. The results show that the proposed model in this paper has improved metrics under various degradation conditions compared to current mainstream image super-resolution models. The method presented in the paper shows a 0.034 increase in SSIM and a 1.329 8 dB increase in PSNR on samples with degradation condition I compared to the real enhanced image super-resolution generative adversarial network. After reconstruction, the visual effect of edge details in the images is better. Furthermore, good generalization effects were achieved on both the DIOR and HRSC2016 datasets.