Target Recognition of SAR Image Based on CN-GAN and CNN in Complex Environment

In recent years, with the rapid development of deep learning, the research of radar image automatic target recognition (ATR) has made great progress. However, because of the complex environments and special imaging principles, Synthetic Aperture Radar (SAR) image still have the problems of sample scarcity and strong speckle noise, which affects the target recognition performance. To solve the above problems, we proposed a target recognition method of SAR image based on Constrained Naive Generative Adversarial Networks (CN-GAN) and Convolutional Neural Network (CNN). Combining Least Squares Generative Adversarial Networks (LSGAN) and Image-to-Image Translation (Pix2Pix), CN-GAN can overcome these problems of low Signal-to-Clutter-Noise Ratio (SCNR), model instability and the excessive freedom degree of the output, which are produced by conventional naive GAN. Besides, we adopted a shallow network structure design in CNN, which can effectively improve the generalization ability of the model and avoid the problem of model overfitting. The experimental results in this paper demonstrate that CN-GAN has achieved the data generation and data enhancement, the SCNR of generated data is higher than the origin data set and data sets gained by other forms of GANs, the recognition performance based on the extended data set is better than the origin data set, and the recognition rate of data set enhanced by CN-GAN is higher than that of other common data enhancement methods.