SAR Target Recognition Based on Inception and Fully Convolutional Neural Network Combining Amplitude Domain Multiplicative Filtering Method

Abstract

The research of Synthetic aperture radar (SAR) target recognition plays a significant role in military and civilian fields. However, for small sample SAR target recognition, there are some problems that need to be solved urgently, including low recognition accuracy, slow training convergence rate, and serious overfitting. Aiming at the above problems, we propose a recognition method based on Inception and Fully Convolutional Neural Network (IFCNN) combined with Amplitude Domain Multiplicative Filtering (ADMF) image processing. To improve the recognition accuracy and convergence rate, the ADMF method is utilized to construct the pretraining set, and the initial parameters of the network are optimized by pretraining. In addition, this paper builds the IFCNN model by introducing the Inception structure and the mixed progressive convolution layer into the FCNN. The full convolution structure of FCNN is effective to alleviate the problem of network overfitting. The Inception structure can enhance the sparsity of features and improve the network classification ability. Meanwhile, the mixed progressive convolution layers can accelerate training. Based on the MSTAR dataset, the experimental results show that the method proposed achieves an average precision of 88.95% and the training convergence rate is significantly improved in small sample scenarios.