In recent years, deep learning has brought significant progress for the problem of synthetic aperture radar (SAR) target classification. However, SAR image characteristics are highly sensitive to the change of imaging conditions. The inconsistency of imaging parameters (especially the depression angle) leads to the distribution shift between the training and test data and severely deteriorates the classification performance. To address this problem, in this paper we propose an unsupervised domain adaptation method based on selective pseudo-labelling for SAR target classification. Our method directly trains a deep model using the data from the target domain by generating pseudo-labels in the target domain. The key idea is to iteratively select valuable samples from the target domain and optimize the classifier. In each iteration, the breaking ties (BT) criterion is adopted to select the best samples with the highest scores of relative confidence. Besides, to avoid error accumulation in the iterative process, class confusion regularization is used to improve the accuracy of pseudo-labelling. Our method is compared with state-of-the-art methods, including supervised classification and unsupervised domain adaptation methods, over the moving and stationary target acquisition and recognition (MSTAR) dataset. The experimental results demonstrate that the proposed method can achieve better classification performance, especially when the difference of depression angles of the source and target domain images is large. Besides, our method also shows its superiority under limited-sample conditions.

近年来，深度学习为合成孔径雷达（SAR）目标分类问题带来了重大进展。然而，SAR图像特征对成像条件的变化高度敏感。成像参数（尤其是俯角）的不一致会导致训练数据和测试数据之间的分布偏移，严重影响分类性能。为了解决这个问题，在本文中，我们提出了一种基于选择性伪标签的无监督域自适应方法，用于 SAR 目标分类。我们的方法通过在目标域中生成伪标签，直接使用来自目标域的数据训练深度模型。关键思想是从目标域中迭代地选择有价值的样本并优化分类器。在每次迭代中，采用打破关系（BT）标准来选择具有最高相对置信度分数的最佳样本。此外，为了避免迭代过程中的误差累积，使用类混淆正则化来提高伪标注的准确性。我们的方法在移动和静止目标获取和识别 (MSTAR) 数据集上与最先进的方法进行了比较，包括监督分类和无监督域适应方法。实验结果表明，该方法可以取得更好的分类性能，尤其是在源域图像和目标域图像的俯角差异较大的情况下。此外，我们的方法在有限样本条件下也显示出其优越性。