To recreate high-resolution, more detailed remote sensing images from existing low-resolution photos, this technique is known as remote sensing image superresolution reconstruction, and it has numerous uses. As an important research hotspot of neural networks, generative adversarial network (GAN) has made outstanding progress for image superresolution reconstruction. It solves the computational complexity and low reconstructed image quality of standard superresolution reconstruction algorithms. This research offers a superresolution reconstruction strategy with a self-attention generative adversarial network to improve the quality of reconstructed superresolution remote sensing images. The self-attention strategy as well as residual module is utilized to build a generator in this model that transforms low-resolution remote sensing images into superresolution ones. It aims to determine the discrepancy between a reconstructed picture and a true picture by using a deep convolutional network as a discriminator. For the purpose of enhancing the accuracy, content loss is used. This is done to obtain accurate detail reconstruction. According to the findings of the experiments, this approach is capable of regenerating higher-quality images.