Aiming at the problems of high complexity and poor feature extraction and presentation performance of ESRGAN model, a super-resolution reconstruction algorithm based on Light weight Generative Adversarial Network (LwGAN) is proposed. The Improved Residual Dense Block (IRDB) is used as the base block to construct the high order feature extraction part of the generated network, extract rich and diversified features, and establish the feature channel and long-distance location relationship. In addition to reducing the number of model parameters, the feature extraction and presentation performance of the model are improved. The experimental results on UC MERCED and NWPU-RESISC45 datasets show that compared with ESRGAN, LwGAN obtains larger peak signal-to-noise ratio and structural similarity, significantly improves the performance of super-resolution reconstruction of remote sensing images, and the visualization results show that the reconstructed images recover more texture detail information, while the number of model parameters is only about one-third of that of the original ESRGAN, which significantly improves the operation efficiency of the model and lays the foundation for subsequent analysis and processing of remote sensing images.