Aiming at the difficulty of acquiring SAR image data, a multi-scale generative adversarial network based on single-image training is proposed, and it is applied to the augmentation of SAR aircraft images.Because the original generative adversarial network sets a single-scale convolution kernel, only the feature distribution of the image under the fixed receptive field is obtained.Therefore, integrating multi-scale group convolution into the adversarial network can mine the distribution features of the image from different scales, and increase the detailed information of generated image.The result is that 400 new SAR aircraft image samples are obtained by training, and the augmented data set is verified by Faster R-CNN and image quality assessment indexes.The experimental results show that the quality assessment indexes of generated images meet the requirements of image detection.Using Faster R-CNN algorithm combined with data augmentation of generative adversarial network, the average detection accuracy is improved from 73.5% to 77.6%.