In the large-scale target detection of remote sensing images based on deep learning, it is difficult to obtain some ground objects, and show poor performance in training results. Therefore, the morphological building index and the generative adversarial network were used for sample augmentation to reduce the problem of model overfitting caused by insufficient detection samples. By extracting the morphological building index related to the texture structure information, superimposing it with the original sample, the texture and spatial characteristics of the building could be strengthened. The existing samples were used to train the generative adversarial network to augment some targets categories. After compositing them with the samples enhanced by the morphological building index, the original sample set was expanded. Compared with the augmentation strategies of flipping, cropping, and changing the color, the detection accuracy of this method got 2% ~ 5% improvement on YLOLOv5, EfficientDet and other models. Experiments have proved that the sample augmentation method combining building index and generative adversarial network can significantly improve the detection accuracy of small-sample remote sensing image targets of special interest categories such as power stations.