Synthetic aperture radar (SAR) image recognition is an important stage of SAR image interpretation. The standard convolutional neural network (CNN) has been successfully applied in the SAR image recognition due to its powerful feature extraction capability. Nevertheless, the CNN requires numerous labeled samples for satisfactory recognition performance, while the performance of the CNN decreases greatly with insufficient labeled samples. Aiming at improving the SAR image recognition accuracy with a small number of labeled samples, a new few-shot learning method is proposed in this paper. We first utilize the attention prototypical network (APN) to calculate the average features of the support images from each category, which are adopted as the prototypes. Afterwards, the feature extraction is performed on the query images using the attention convolutional neural network (ACNN). Finally, the feature matching classifier (FMC) is adopted for calculating the similarity scores between the feature maps and the prototypes. We embed the attention model SENet to the APN, ACNN, and FMC, which effectively enhances the expression of the prototypes and the feature maps. Besides, the loss function of our method consists of cross-entropy and prototype-separability losses. In the training process, this loss function increases the separability of different prototypes, which contributes to higher recognition accuracy. We perform experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) and the Vehicle and Aircraft (VA) datasets. It has been proved that our method is superior to the related state-of-the-art few-shot image recognition methods.

合成孔径雷达（SAR）图像识别是SAR图像解译的一个重要阶段。标准卷积神经网络（CNN）由于其强大的特征提取能力，已成功应用于SAR图像识别。尽管如此，CNN 需要大量标记样本才能获得令人满意的识别性能，而 CNN 的性能会因标记样本不足而大幅下降。针对在标记样本数量较少的情况下提高SAR图像识别精度的问题，本文提出了一种新的小样本学习方法。我们首先利用注意力原型网络（APN）计算每个类别的支持图像的平均特征，并将其作为原型。然后，使用注意力卷积神经网络 (ACNN) 对查询图像执行特征提取。最后，采用特征匹配分类器（FMC）计算特征图和原型之间的相似度得分。我们将注意力模型 SENet 嵌入到 APN、ACNN 和 FMC 中，这有效地增强了原型和特征图的表达。此外，我们方法的损失函数包括交叉熵和原型可分离性损失。在训练过程中，这个损失函数增加了不同原型的可分离性，有助于提高识别准确率。我们对移动和静止目标采集与识别 (MSTAR) 以及车辆和飞机 (VA) 数据集进行了实验。