Abstract: In order to improve the quality of super-resolution reconstruction of remote sensing images，a flow-based remote sensing image reconstruction algorithm was proposed.First，the improved RRDB architecture was introduced on the basis of the Glow model for low-resolution image feature extraction，and more layers and connections were built to improve the stability of training.Then，a pure data-driven flow model was used to train the parameters of the distribution，and the method of maximizing the negative log-likelihood was optimized to obtain the loss function of the algorithm.Experiments show that the model can quickly reach a stable convergence state during the network training process and has a strong generalization ability.The reconstructed image quality was compared with SRCNN，SRGAN，ESRGAN.After testing，it is found that the proposed algorithm is far superior to the SRCNN algorithm，and that it also has obvious advantages compared with other algorithms.The reconstructed image not only has improved indicators，for example，PSRN and SSIM increase by 15% and 40% respectively compared with SRCNN,but also has better clarity and richer high-frequency details.

Key words: super-resolution reconstruction, Flow, Glow, RRDB, negative log-likelihood