Semi-Supervised SAR ATR via Conditional Generative Adversarial Network with Multi-Discriminator

Convolutional neural networks (CNN) show superior potential in synthetic aperture radar automatic target recognition (SAR ATR). However, due to the difficulty of obtaining SAR images and the scarcity of labeled SAR images, supervised learning has poor performance in this area and is not widely applicable. To address this problem, a semi-supervised conditional generative adversarial network with a multi-discriminator (SCGAN-MD) is proposed in this paper. In our method, a conditional generative adversarial network (CGAN) is adopted with two discriminators for training the generated images and predicting the labels for unlabeled samples. Compared with other semi-supervised learning-based methods, our proposed method has more accurate image generation capability and can achieve improved recognition accuracy of SAR ATR. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database indicate that the proposed method can effectively improve the recognition accuracy and robustness of the network with a small number of labeled samples.