A Directional Generation Algorithm for SAR Image Based on Azimuth-Guided Statistical Generative Adversarial Network

The high cost of acquiring synthetic aperture radar (SAR) images results in the problem of insufficient data, which limits the performance of deep learning-based automatic target recognition (ATR) models. To solve this problem, a directional generation algorithm for SAR image based on azimuth-guided statistical generative adversarial network (AGSGAN) is proposed in this paper. The proposed algorithm can not only generate images with similar statistical characteristics as the real SAR images, but also control the azimuth of the generated images. Considering that the statistical characteristics of SAR images are different at different azimuth, the proposed algorithm partitions the azimuth intervals of SAR image into adaptive azimuth intervals, and the statistical characteristics of images within each adaptive azimuth interval should be similar. Then, the proposed algorithm obtains the statistical characteristics of real images by using the $G^{0}$ distribution to fit the statistical distribution of images within each adaptive azimuth interval. Finally, the random noise sampled from the fitted $G^{0}$ distribution and the serial number of adaptive azimuth interval are inputted into AGSGAN. The images that are within a specified adaptive azimuth interval and have statistical characteristics similar to the real images are generated by AGSGAN. Experimental results show that the images generated by the proposed algorithm are more realistic in statistical characteristics, and can effectively improve the recognition accuracy of the deep learning-based SAR automatic target recognition model.