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.

翻译：卷积神经网络（CNN）在合成孔径雷达（SAR）自动目标识别（ATR）中取得了高性能。然而，CNN的性能高度依赖于大量训练数据。标记训练SAR图像的不足限制了识别性能，甚至使某些ATR方法失效。此外，在标记训练数据稀少的情况下，许多现有CNN甚至无法有效工作。针对这些挑战，我们提出了一种带有直推式辅助分割的半监督SAR ATR框架（SFAS）。该框架专注于通过辅助损失作为正则化器，利用未标记样本的直推式泛化能力。通过对未标记SAR样本的辅助分割以及训练中的信息残差损失（IRL），该框架能够利用所提出的训练循环过程，逐步整合识别与分割的信息编译，构建有益的归纳偏置，实现高性能。在MSTAR数据集上进行的实验表明，我们提出的SFAS在小样本学习中的有效性。每类20个训练样本下可达到94.18%的识别性能，同时获得精确的分割结果。面对EOC变化，每类10个训练样本时识别率仍高于88.00%。