SYNTHETIC MINORITY CLASS DATA BY GENERATIVE ADVERSARIAL NETWORK FOR IMBALANCED SAR TARGET RECOGNITION

The deep convolutional neural networks (CNNs) have achieved the state of art performance in synthetic aperture radar (SAR) automatic target recognition (ATR). However, these networks often provide sub-optimal recognition results in the case of imbalanced SAR data distribution. In this paper, a synthetic minority class data method for improving imbalanced SAR target recognition using the generative adversarial network (GAN) is proposed. The minority class SAR data is first over-sampled by optimized data augmentation policies from automatic search method, which enlarge the training set for GAN. The progressive growing of GANs (PGGAN) is then trained on these data and generates high quality and diverse minority class SAR data to alleviate imbalanced data distribution. Experimental results on the designed imbalanced distributed Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset indicate that our method can effectively improve the recognition accuracy of minority class by approximately 11.68%.