Remote sensing image land classification applications based on deep learning require massive data sets as training samples， and the image label data sets are often difficult to meet the training requirements due to the small number. Using existing samples to increase is an effective technical method. The traditional data augmentation technology only changes the color and sharpness of the image， and the amount of augmentation has a certain limit. In order to automate the augmentation of more diverse samples， a remote sensing image sample augmentation method based on Pix2pix network is designed in this paper. Pix2pix network generator is used to generate virtual images according to unmanned aerial vehicle and Google image tags， and the discriminator compares the virtual images with the real images. After generating adversarial training for many times， the sample pairs are output to achieve augmentation. The results show that the visual contrast similarity of the generated results is high and the average cosine similarity of the unmanned aerial vehicle image and Google image is 0.85 and 0.96， respectively， and the average histogram similarity is 0.50 and 0.61. It is an effective method for remote sensing image sample augmentation.