To prevent the over fitting phenomenon of the convolutional neural network( CNN ) under the condition of insufficient labeled data, and aim at the SAR target recognition under noisy condition, a novel target recognition method is proposed. First, the data augmentation method is used to augment the data set to improve the generalization ability of the model. Second, the feature extraction is carried out by zero phase component analysis( ZCA ), and a set of feature sets is used to pre-train the convolutional neural network. In order to optimize the network structure and prevent the over-fitting phenomenon, the rectified linear unit( ReLU ), Dropout, regularization, unit convolution kernel and other sparse technology are used. Experiments demonstrate that the new algorithm is effective for target recognition, which has a high recognition capability for targets and their deformation sub-classes, and is robust to noise.
