A semi-greedy neural network CAE-HL-CNN for SAR target recognition with limited training data

Abstract

ABSTRACT Synthetic aperture radar (SAR) automatic target recognition (ATR) based on convolutional neural network (CNN) is a research hotspot in recent years. However, CNN is data-driven, and severe overfitting occurs when training data is scarce. To solve this problem, we first introduce a non-greedy CNN network. But when a CNN structure with a non-greedy classifier is used to handle the SAR ATR in the case of scarce training data, the feature extraction capability of the network degrades. To balance the feature extraction and anti-overfitting capabilities of the network, a semi-greedy network called transfer learning with convolutional auto-encoders (CAE) and hinge loss CNN (HL-CNN), namely CAE-HL-CNN, is proposed in this paper. First, the CAE-HL-CNN introduces a non-greedy network which uses a hinge loss classifier in the CNN structure to enhance the network’s generalization performance. It retains the hierarchical feature extraction structure of CNN and has the same anti-overfitting capability as support vector machine. Then, by combining CAE with the HL-CNN through transfer learning, the CAE-HL-CNN extracts a complete feature representation to compensate for the degradation in feature extraction capability in a greedy way. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that in the case of scarce training data, the proposed network can improve the recognition performance of CNN, which achieves higher classification accuracy and performs more equably on each category, and it extracts sparser feature maps than the compared methods.