A Novel Semi-Supervised Convolutional Neural Network Method for Synthetic Aperture Radar Image Recognition
journal contributionposted on 14.03.2019, 09:50 by Z Yue, F Gao, Q Xiong, J Wang, T Huang, E Yang, H Zhou
Background / introduction: SAR image automatic target recognition technology (SAR-ATR) is one of the research hotspots in the field of image cognitive learning. Inspired by the human cognitive process, experts have designed convolutional neural networks (CNN) based methods and successfully applied the methods to SAR-ATR. However, the performance of CNNs significantly deteriorates when the labelled samples are insufficient. Methods: To effectively utilize the unlabelled samples, a semi-supervised CNN method is proposed in this paper. First, CNN is used to extract the features of the samples, and subsequently the class probabilities of the unlabelled samples are computed using the softmax function. To improve the effectiveness of the unlabelled samples, we remove possible noise performing thresholding on the class probabilities. Afterwards, based on the remaining class probabilities, the information contained in the unlabelled samples is integrated with the scatter matrices of the standard linear discriminant analysis (LDA) method. The loss function of CNN consists of a supervised component and an unsupervised component, where the supervised component is created using the cross-entropy function and the unsupervised component is created using the scatter matrices. The class probabilities are utilized to control the impact of the unlabelled samples in the training process, and the reliability of the unlabelled samples is further improved. Results: We choose ten types of targets from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The experimental results show that the recognition accuracy of our method is significantly higher than that of the supervised CNN method. Conclusions: It proves that our method can effectively improve the SAR-ATR accuracy despite the deficiency of the labelled samples.