The scattering signatures of a synthetic aperture radar (SAR) target image will be highly sensitive to different azimuth angles/poses, which aggravates the demand for training samples in learning-based SAR image automatic target recognition (ATR) algorithms, and makes SAR ATR a more challenging task. This paper develops a novel rotation awareness-based learning framework termed RotANet for SAR ATR under the condition of limited training samples. First, we propose an encoding scheme to characterize the rotational pattern of pose variations among intra-class targets. These targets will constitute several ordered sequences with different rotational patterns via permutations. By further exploiting the intrinsic relation constraints among these sequences as the supervision, we develop a novel self-supervised task which makes RotANet learn to predict the rotational pattern of a baseline sequence and then autonomously generalize this ability to the others without external supervision. Therefore, this task essentially contains a learning and self-validation process to achieve human-like rotation awareness, and it serves as a task-induced prior to regularize the learned feature domain of RotANet in conjunction with an individual target recognition task to improve the generalization ability of the features. Extensive experiments on moving and stationary target acquisition and recognition benchmark database demonstrate the effectiveness of our proposed framework. Compared with other state-of-the-art SAR ATR algorithms, RotANet will remarkably improve the recognition accuracy especially in the case of very limited training samples without performing any other data augmentation strategy.

中文翻译：

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基于旋转感知的有限训练样本SAR目标识别自监督学习


合成孔径雷达（SAR）目标图像的散射特征将对不同的方位角/姿态高度敏感，这加剧了基于学习的SAR图像自动目标识别（ATR）算法对训练样本的需求，并使SAR ATR成为更具挑战性的任务。本文针对训练样本有限的情况，开发了一种新颖的基于旋转感知的学习框架 RotANet，用于 SAR ATR。首先，我们提出了一种编码方案来表征类内目标之间姿态变化的旋转模式。这些目标将通过排列构成具有不同旋转模式的多个有序序列。通过进一步利用这些序列之间的内在关系约束作为监督，我们开发了一种新颖的自监督任务，使 RotANet 学会预测基线序列的旋转模式，然后在没有外部监督的情况下自主地将这种能力推广到其他序列。因此，该任务本质上包含一个学习和自我验证的过程，以实现类人的旋转意识，并且它作为任务诱导的先验，结合单个目标识别任务对 RotANet 学习到的特征域进行正则化，以提高泛化能力的功能的能力。对移动和静止目标获取和识别基准数据库的大量实验证明了我们提出的框架的有效性。与其他最先进的 SAR ATR 算法相比，RotANet 将显着提高识别精度，尤其是在训练样本非常有限且不执行任何其他数据增强策略的情况下。