A performance analysis of convolutional neural network models in SAR target recognition

In recent years, the deep learning method represented by Convolutional Neural Network (CNN) has made great progress in the field of image recognition. In this paper, the representative convolution neural network models such as AlexNet, VGGNet, GoogLeNet, ResNet, DenseNet, SENet and so on are applied to SAR image target recognition. According to the accuracy, parameter quantity, training time and other indicators, the performance of different CNN models are analyzed and compared on MSTAR data set, the superiority of CNN model in SAR image target recognition is verified, and it also provides a reference for the follow-up work in this field.