Remote sensing image superresolution reconstruction based on Generative adversarial networks has some problems, such as unstable training, redundant parameters and unclear texture details. This paper presents a super resolution reconstruction algorithm of remote sensing image based on edge detection. Firstly, the improved Canny edge detection operator is introduced into the generator network for lowresolution image feature extraction. Bilateral filtering and 3×3 neighborhood gradient are used to detect image edge information in the Canny operator edge extraction process, so that the network can better express highfrequency features. Secondly, in order to reduce the network parameters and improve the stability of network training, the redundant BN layer in the discriminator network is removed, and the Wasserstein distance is defined as adversarial loss to solve the gradient disappearance phenomenon in generating adversarial network training. On the NWPU RESISC45 dataset, Compared with WDSR and CARN, the peak signaltonoise ratio and structural similarity of the proposed method are improved by 1.22 dB,0.114 and 0.32 dB,0.013, respectively. Moreover, compared with other SR algorithms such as WDSR and CARN, the reconstructed images are improved in texture details and subjective visual effects.