SAR ATR based on shunt convolutional neural network

In view of the low recognition accuracy of synthetic aperture radar(SAR)in the field of image target recognition, this paper designs a target recognition method for extracting SAR image features by shunt convolutional neural network. Firstly, the improved ELU activation function is used to replace the conventional ReLU activation function, and a deep learning model combined with the quadratic cost function is established. Secondly, this paper uses the optimization algorithm combining root mean square prop (RMSProp) and Nesterov momentum to perform the iterative updating task of cost function parameters, and uses Nesterov to introduce momentum to change the gradient to improve the updating method from two aspects to effectively improve the convergence speed and accuracy of the network. Experiments on the MSTAR data set produced by DARPA and AFRL show that the proposed algorithm is effective in target recognition since that it can extract the information contained in various targets in SAR images sufficiently, and has good recognition performance.