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基于自注意力特征融合的半监督生成对抗网络用于SAR目标识别
引用本文:应自炉,王发官,翟懿奎,王文琪.基于自注意力特征融合的半监督生成对抗网络用于SAR目标识别[J].信号处理,2022,38(2):258-267.
作者姓名:应自炉  王发官  翟懿奎  王文琪
作者单位:五邑大学智能制造学部,广东 江门 529020
基金项目:广东省普通高校人工智能重点领域专项2018KZDXM073广东省科技厅国际科技合作项目2021A0505030080广东省基础与应用基础研究基金项目2019A1515010716
摘    要:与具有大量标注数据的光学图像相比,合成孔径雷达(Synthetic Aperture Radar,SAR)图像缺乏足够的标记样本,限制了监督学习的SAR目标识别算法的性能.而无监督识别方法又难以满足实际需求,因此本文提出了基于自注意力特征融合的半监督生成对抗网路.首先,在构建生成器和判别器时引入自注意力层,克服卷积算子...

关 键 词:合成孔径雷达  生成对抗网络  半监督  自注意力  特征融合
收稿时间:2021-06-01

Semi-supervised Generative Adversarial Network Based on Self-attention Feature Fusion for SAR Target Recognition
Affiliation:School of Intelligent Manufacturing,Wuyi University,Jiangmen,Guangdong 529020,China
Abstract:Compared with optical images with a large number of labeled data, synthetic aperture radar (SAR) images lacked sufficient labeled samples, which limited the performance of supervised learning SAR target recognition algorithm. However, the unsupervised recognition methods were difficult to meet the practical needs. This paper proposed a semi-supervised generative adversarial network based on self-attention feature fusion,alleviating the challenge of annotated data lacking. Firstly, the self-attention layer was introduced in the construction of generator and discriminator to overcome the problem that convolution did not have long range extraction capability. Secondly, the discriminator utilized multi-level feature fusion to capture the key information of SAR image. Finally, spectral normalization was applied in the training process to improve the convergence stability. In order to verify the effectiveness of the proposed method, experiments were carried out on Moving and Stationary Target Acquisition and Recognition (MSTAR) data sets. Experiment show that the proposed method can learn valuable information from unlabeled samples, effectively solve the problem of insufficient labeling. 
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