Aiming at occlusing cloud in remote sensing images of electric power facilities,a de-cloud model based on generative adversarial network(GAN)is proposed.Taking cGAN as the main structure,the model was designed with the encoder adaptive filling convolution,the recurrent neural network module based on soft attention,which is used to to solve the problem of network local optimum by adding global dependencies to all feature nodes.By extracting the key information through spatial information transformation,the cloud removal and reconstruction quality is improve.The experimental results demonstrated that the method has a better effect on cloud occlusion removal in remote sensing images.Compared with other GAN networks,the SSIM and PSNR of the reconstructed images reach 0.983 and 32.899, which are improved by 23.93%and 8.86%,respectively.The method not only provides a basis for remote sensing image-based power facility identification,but also provides a reference for deep learning remote sensing image processing applications,