The deep neural networks (DNNs) have freed the synthetic aperture radar automatic target recognition (SAR ATR) from expertise-based feature designing and demonstrated superiority over conventional solutions. There has been shown the unique deficiency of ground vehicle benchmarks in shapes of strong background correlation results in DNNs overfitting the clutter and being non-robust to unfamiliar surroundings. However, the gap between fixed background model training and varying background application remains underexplored. Inspired by contrastive learning, this letter proposes a solution called Contrastive Feature Alignment (CFA) aiming to learn invariant representation for robust recognition. The proposed method contributes a mixed clutter variants generation strategy and a new inference branch equipped with channel-weighted mean square error (CWMSE) loss for invariant representation learning. In specific, the generation strategy is delicately designed to better attract clutter-sensitive deviation in feature space. The CWMSE loss is further devised to better contrast this deviation and align the deep features activated by the original images and corresponding clutter variants. The proposed CFA combines both classification and CWMSE losses to train the model jointly, which allows for the progressive learning of invariant target representation. Extensive evaluations on the MSTAR dataset and six DNN models prove the effectiveness of our proposal. The results demonstrated that the CFA-trained models are capable of recognizing targets among unfamiliar surroundings that are not included in the dataset, and are robust to varying signal-to-clutter ratios.

翻译：深度神经网络(DNNs)使合成孔径雷达自动目标识别(SAR ATR)摆脱了基于专家经验的特征设计，并展现出优于传统方法的性能。研究表明，地面车辆基准数据集在形态上存在背景强相关性的独特缺陷，导致DNNs过度拟合杂波特征，对陌生环境缺乏鲁棒性。然而，固定背景模型训练与变背景应用之间的鸿沟仍未得到充分探索。受对比学习启发，本文提出一种名为对比特征对齐(CFA)的解决方案，旨在学习不变表征以实现鲁棒识别。该方法贡献了混合杂波变体生成策略和新推理分支，并配备通道加权均方误差(CWMSE)损失进行不变表征学习。具体而言，该生成策略经过精心设计，能更好地吸引特征空间中杂波敏感偏差。CWMSE损失进一步被设计用于更有效地对比这种偏差，并将原始图像与对应杂波变体激活的深度特征进行对齐。提出的CFA将分类损失与CWMSE损失联合训练模型，从而实现目标不变表征的渐进式学习。在MSTAR数据集和六个DNN模型上的大量评估证明了本方法的有效性。结果表明，经CFA训练的模型能够识别数据集未包含的陌生环境中的目标，并对不同信杂比具有鲁棒性。