A generation countermeasure network (GAN) remote-sensing image super-resolution reconstruction algorithm, integrating a multiscale receptive field module is proposed to obtain remote-sensing reconstructed images containing more high-frequency perceptual information and texture details. The GAN algorithm should also be able to solve the problems of training super-resolution reconstruction algorithms and missing reconstructed image details. First, a multiscale convolution cascade is used to enhance the global feature acquisition, remove the normalization layer from the generated countermeasure network, improve network training efficiency, remove artifacts, and reduce computational complexity. Then, the multiscale receptive field and dense residual module are used as the detail feature extraction modules to improve the quality of network reconstruction and obtain more detailed texture information. Finally, the Charbonnier and total variation loss functions are combined to improve the stability of network training and accelerate convergence. Consequently, experimental results show that the average detection outcomes of the proposed algorithm on the Kaggle, WHU-RS19, and AID datasets are higher than those of the super-resolution GAN in terms of peak signal-to-noise ratio, structural similarity, and feature similarity, respectively, by about 1.65 dB, 0.040 (5.2%), and 0.010 (1.1%).