Semi-Supervised SAR ATR Framework with Transductive Auxiliary
  Segmentation
Chenwei Wang Xiaoyu Liu Yulin Huang Siyi Luo Jifang Pei Jianyu Yang Deqing Mao
Convolutional neural networks (CNNs) have achieved high performance in
synthetic aperture radar (SAR) automatic target recognition (ATR). However, the
performance of CNNs depends heavily on a large amount of training data. The
insufficiency of labeled training SAR images limits the recognition performance
and even invalidates some ATR methods. Furthermore, under few labeled training
data, many existing CNNs are even ineffective. To address these challenges, we
propose a Semi-supervised SAR ATR Framework with transductive Auxiliary
Segmentation (SFAS). The proposed framework focuses on exploiting the
transductive generalization on available unlabeled samples with an auxiliary
loss serving as a regularizer. Through auxiliary segmentation of unlabeled SAR
samples and information residue loss (IRL) in training, the framework can
employ the proposed training loop process and gradually exploit the information
compilation of recognition and segmentation to construct a helpful inductive
bias and achieve high performance. Experiments conducted on the MSTAR dataset
have shown the effectiveness of our proposed SFAS for few-shot learning. The
recognition performance of 94.18\% can be achieved under 20 training samples in
each class with simultaneous accurate segmentation results. Facing variances of
EOCs, the recognition ratios are higher than 88.00\% when 10 training samples
each class.