Lightweight Two-Stream Convolutional Neural Network for SAR Target Recognition

Abstract

This letter proposes a lightweight two-stream convolutional neural network (CNN) for synthetic aperture radar (SAR) target recognition. Specifically, the two-stream CNN first extracts low-level features by three alternating convolution layers and max-pooling layers. Then two streams are followed to extract local and global features. One stream uses global maximum pooling to extract local features with the greatest response; the other uses large-stride convolution kernels to extract global features. Finally, the two streams are combined for target recognition. Therefore, the two-stream CNN can learn rich multilevel features to achieve high recognition accuracy for SAR target recognition. Moreover, compared to other popular CNNs, the two-stream CNN is very lightweight. The experimental results on the moving and stationary target acquisition and recognition (MSTAR) data set demonstrate that the proposed method not only can improve the recognition accuracy but also reduce the number of parameters of the model dramatically.