To solve the problem that the detection accuracy of remote sensing image targets is affected by convolution neural network overfitting under the condition of small samples, a data augmentation method based on generative adversarial networks is proposed. The discrimination model is used to provide local and global decisions for the generation model to improve the quality of the image generated by the generative model. The new samples are obtained by fusing the generated target and the training set image, and the new samples do not need to be labeled manually. Experimental results show that: the accuracy of detection and recognition is improved after adding the generated data to the original data; this method can be superimposed with the data augmentation method based on image affine transformation to further improve the effect of data augmentation.