Large-scale high-quality, standardized, measurable and accurate data is the key to promote the progress of the algorithm in the radar remote sensing. Data scaling is a widespread technology that increases the size of a labeled training set dataset through specific data transformations. Synthetic Aperture Radar (SAR) image simulators based on computer-aided mapping models play an important role in SAR applications such as automatic target recognition and image interpretation, but the accuracy of this simulator is due to geometric errors and simplification of electromagnetic calculations. In order to achieve a SAR image datasets with the known target and azimuth angles, we can generate the desired image directly from a known image database. We can realize the augmentation of SAR image data set through linear synthesis and Generative Adversarial Networks, which can generate SAR images for the specified azimuth.