It is difficult to obtain the infrared image of the aircraft target under air-to-ground background, which may leads to over-fitting or other problems of the algorithms for infrared aircraft target detection.To solve the problems, a data augmentation method for small-sample infrared aircraft targets is proposed based on the related methods of feature fitting and inter-channel attention mechanism of Generative Adversarial Network (GAN).Firstly, a special pyramidal multi-scale GAN structure is used to learn the feature information of a single image at different scales.Secondly, for the small infrared aircraft images, the structure of the network generator is improved, an inter-channel attention module is added to the generator to enhance the representation of small sensory fields and enrich the details of the generated images.Finally, the learning rate of the small-scale stage of the pyramid is scaled in the training phase of the network, to avoid the distortion of the generated images due to the overlarge learning rate.Several common target detection algorithms are simulated and evaluated, and the results validate the effectiveness and superiority of the proposed method by comparing it with the traditional data augmentation methods.