Automatic target recognition (ATR) is an important use case for synthetic aperture radar (SAR) image interpretation. Recent years have seen significant advancements in SAR ATR technology based on semi-supervised learning. However, existing semi-supervised SAR ATR algorithms show low recognition accuracy in the case of class imbalance. This work offers a non-balanced semi-supervised SAR target recognition approach using dynamic energy scores and adaptive loss. First, an energy score-based method is developed to dynamically select unlabeled samples near to the training distribution as pseudo-labels during training, assuring pseudo-label reliability in long-tailed distribution circumstances. Secondly, loss functions suitable for class imbalances are proposed, including adaptive margin perception loss and adaptive hard triplet loss, the former offsets inter-class confusion of classifiers, alleviating the imbalance issue inherent in pseudo-label generation. The latter effectively tackles the model's preference for the majority class by focusing on complex difficult samples during training. Experimental results on extremely imbalanced SAR datasets demonstrate that the proposed method performs well under the dual constraints of scarce labels and data imbalance, effectively overcoming the model bias caused by data imbalance and achieving high-precision target recognition.

翻译：合成孔径雷达（SAR）图像解译中，自动目标识别（ATR）是一项重要应用。近年来，基于半监督学习的SAR ATR技术取得了显著进展。然而，现有半监督SAR ATR算法在类别不平衡情况下识别精度较低。本研究提出一种利用动态能量分数与自适应损失的非平衡半监督SAR目标识别方法。首先，提出一种基于能量分数的方法，在训练过程中动态选择靠近训练分布的未标记样本作为伪标签，确保长尾分布场景下伪标签的可靠性。其次，提出了适用于类别不平衡的损失函数，包括自适应间隔感知损失与自适应困难三元组损失：前者通过抵消分类器的类间混淆，缓解伪标签生成中固有的不平衡问题；后者通过在训练中聚焦于复杂困难样本，有效应对模型对多数类的偏好。在极端不平衡SAR数据集上的实验结果表明，所提方法在标签稀缺与数据不平衡的双重约束下表现优异，能有效克服数据不平衡导致的模型偏差，实现高精度目标识别。