Target discrimination method for SAR images via convolutional neural network with semi-supervised learning and minimum feature divergence constraint

ABSTRACT

Target discrimination is an important part of the synthetic aperture radar automatic target recognition (SAR ATR). Nowadays, convolutional neural network (CNN) has been used in SAR ATR successfully. However, training CNN requires large amounts of labelled data and collection of the labelled SAR data is expensive and time demanding. It may yield overfitting when directly applying CNN to the SAR target discrimination with the limited labelled SAR data. To tackle this problem, we design a semi-supervised SAR target discrimination framework consisting of the classification network and the reconstruction network. In addition to the labelled SAR data, the unlabelled SAR data are also used to help the whole network better extract generalized feature for classification. Moreover, to make the learned feature more discriminative, the feature constraint based on Kullback-Leibler (KL) divergence is introduced to minimize the distribution divergence between the training and test data feature representations. Experimental results on the miniSAR data show the effectiveness of the proposed method.

Additional information

Funding

This work was supported by the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) [No.B18039]; the National Natural Science Foundation of China [61671354]; the Shaanxi Innovation Team Project.; the National Science Fund for Distinguished Young Scholars of China [61525105].