ABSTRACT Synthetic aperture radar (SAR) automatic target recognition (ATR) based on convolutional neural network (CNN) is a research hotspot in recent years. However, CNN is data-driven, and severe overfitting occurs when training data is scarce. To solve this problem, we first introduce a non-greedy CNN network. But when a CNN structure with a non-greedy classifier is used to handle the SAR ATR in the case of scarce training data, the feature extraction capability of the network degrades. To balance the feature extraction and anti-overfitting capabilities of the network, a semi-greedy network called transfer learning with convolutional auto-encoders (CAE) and hinge loss CNN (HL-CNN), namely CAE-HL-CNN, is proposed in this paper. First, the CAE-HL-CNN introduces a non-greedy network which uses a hinge loss classifier in the CNN structure to enhance the network’s generalization performance. It retains the hierarchical feature extraction structure of CNN and has the same anti-overfitting capability as support vector machine. Then, by combining CAE with the HL-CNN through transfer learning, the CAE-HL-CNN extracts a complete feature representation to compensate for the degradation in feature extraction capability in a greedy way. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that in the case of scarce training data, the proposed network can improve the recognition performance of CNN, which achieves higher classification accuracy and performs more equably on each category, and it extracts sparser feature maps than the compared methods.

中文翻译：

---

用于有限训练数据的SAR目标识别的半贪婪神经网络CAE-HL-CNN

摘要 基于卷积神经网络（CNN）的合成孔径雷达（SAR）自动目标识别（ATR）是近年来的研究热点。然而，CNN 是数据驱动的，当训练数据稀缺时会出现严重的过拟合。为了解决这个问题，我们首先引入一个非贪婪的CNN网络。但是当在训练数据稀少的情况下使用具有非贪婪分类器的 CNN 结构来处理 SAR ATR 时，网络的特征提取能力会下降。为了平衡网络的特征提取和抗过拟合能力，提出了一种称为具有卷积自动编码器（CAE）和铰链损失CNN（HL-CNN）的迁移学习的半贪婪网络，即CAE-HL-CNN，在这篇报告。第一的，CAE-HL-CNN 引入了一种非贪婪网络，它在 CNN 结构中使用铰链损失分类器来增强网络的泛化性能。它保留了CNN的分层特征提取结构，并具有与支持向量机相同的抗过拟合能力。然后，通过迁移学习将 CAE 与 HL-CNN 相结合，CAE-HL-CNN 提取完整的特征表示，以贪婪的方式补偿特征提取能力的下降。在移动和静止目标获取与识别（MSTAR）数据集上的实验表明，在训练数据稀少的情况下，所提出的网络可以提高 CNN 的识别性能，从而实现更高的分类准确率，并且在每个类别上的表现更加均衡，