A novel semantic-aided feature generative adversarial network (SFGAN) is proposed in this letter and utilized in zero-shot learning (ZSL) for fast synthetic aperture radar (SAR) automatic target recognition (ATR). By exploiting shared semantic attributes of all classes, the SFGAN is capable of directly generating and recognizing samples with sufficient discriminability and reality from feature dimension and ultimately achieves the accurate and fast recognition of unseen targets. To enhance the quality and stability of feature space, the Wasserstein distance measurement based on the semantic attributes is applied in generative adversarial network (GAN). Meanwhile, in order to improve the discriminability of generated features, a classification subnet is integrated into the network. Additionally, Gaussian mixture model (GMM) and reconstruction loss are introduced in feature generation to align the unseen features more consistent with distribution of real data. In experiments on a new self-built SAR aircraft dataset, SFGAN achieves 70.14% accuracy on unseen targets and improves the training speed by more than two times, fully demonstrating the advancement and effectiveness of the proposed method.

中文翻译：

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具有语义属性的特征生成辅助零样本快速 SAR 目标识别

本文提出了一种新颖的语义辅助特征生成对抗网络（SFGAN），并将其用于快速合成孔径雷达（SAR）自动目标识别（ATR）的零样本学习（ZSL）。通过利用所有类的共享语义属性，SFGAN能够从特征维度直接生成和识别具有足够辨别力和真实性的样本，最终实现对不可见目标的准确快速识别。为了提高特征空间的质量和稳定性，基于语义属性的 Wasserstein 距离测量被应用于生成对抗网络（GAN）。同时，为了提高生成特征的可区分性，在网络中集成了分类子网。此外，在特征生成中引入了高斯混合模型（GMM）和重建损失，以使看不见的特征与真实数据的分布更加一致。在新型自建SAR飞机数据集上的实验中，SFGAN对未见过目标的准确率达到了70.14%，训练速度提高了两倍以上，充分证明了该方法的先进性和有效性。