Abstract
Deep neural networks, especially convolutional neural networks are recently applied to synthetic aperture radar target recognition and achieved state-of-the-art results. Large amount of labeled data are needed during training period of deep neural network. However, labeling enough synthetic aperture radar data on novel classes is not feasible. In this paper, a new framework is presented by introducing triplet loss function to train a deep neural network with few labeled data. The proposed few-shot learning method is verified using Moving and Stationary Target Acquisition and Recognition data set. The results show that the proposed network has good recognition performance on limited labeled data.
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