A Demand-Driven SAR Target Sample Generation Method for Imbalanced Data Learning

Changjie Cao, Zongyong Cui, Liying Wang, Jielei Wang, Zongjie Cao, Jianyu Yang

Abstract: Since there are differences in the natural frequency of various synthetic aperture radar (SAR) target samples in reality, the problem of imbalanced data on the automatic target recognition (ATR) model has gradually appeared in recent years. The problem makes the classification boundary learned by the ATR model often fuzzy or even wrong. In this article, an SAR target sample generation method was proposed, called demand-driven generative adversarial nets (DDGANs), which provided an effective way to implement imbalanced data learning. When the imbalanced data exacerbated the deterioration of the minority category target samples distribution, the proposed method generated samples to alleviate this negative impact. The proposed method innovatively used two convolutional neural networks to form the discriminator of DDGAN. Among them, a convolutional neural network was used to determine whether the generated sample is real or fake. Moreover, another convolutional neural network can simultaneously dig out the generation demands of different categories of target samples when recognizing the generated samples. The generation demands enabled DDGAN to allocate different generation capabilities to different target samples on demand, thereby alleviating the negative impact of data imbalance. At the same time, DDGAN can autonomously learn the generation demands from imbalanced training sets. Several experimental results based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset showed the advantages of DDGAN. Compared with existing imbalanced learning algorithms, the proposed method had obvious superiority in recognition performance and data generation efficiency.