Institute of Engineering, Guangzhou College of Technology and Business, Gangzhou 510850, China.
Ideological and Political Theory Teaching Department, South China Business College Guangdong University of Foreign Studies, Gangzhou 510545, China.
Comput Intell Neurosci. 2021 Nov 26;2021:2392642. doi: 10.1155/2021/2392642. eCollection 2021.
For the problem of synthetic aperture radar (SAR) image target recognition, a method via combination of multilevel deep features is proposed. The residual network (ResNet) is used to learn the multilevel deep features of SAR images. Based on the similarity measure, the multilevel deep features are clustered and several feature sets are obtained. Then, each feature set is characterized and classified by the joint sparse representation (JSR), and the corresponding output result is obtained. Finally, the results of different feature sets are combined using the weighted fusion to obtain the target recognition results. The proposed method in this paper can effectively combine the advantages of ResNet and JSR in feature extraction and classification and improve the overall recognition performance. Experiments and analysis are carried out on the MSTAR dataset with rich samples. The results show that the proposed method can achieve superior performance for 10 types of target samples under the standard operating condition (SOC), noise interference, and occlusion conditions, which verifies its effectiveness.
针对合成孔径雷达（SAR）图像目标识别问题，提出了一种基于多级深度特征融合的方法。该方法利用残差网络（ResNet）学习 SAR 图像的多级深度特征，基于相似性度量对多级深度特征进行聚类，得到多个特征集，然后利用联合稀疏表示（JSR）对每个特征集进行特征描述和分类，得到相应的输出结果，最后采用加权融合将不同特征集的结果进行融合，得到目标识别结果。本文提出的方法可以有效地结合 ResNet 和 JSR 在特征提取和分类方面的优势，提高整体识别性能。在具有丰富样本的 MSTAR 数据集上进行了实验和分析。结果表明，该方法在标准工作条件（SOC）、噪声干扰和遮挡条件下，能够对 10 类目标样本实现优越的性能，验证了其有效性。
