摘要
提出了一种基于多层次单演信号特征的合成孔径雷达(SAR)图像目标识别方法。为了充分利用多层次单演信号特征的鉴别力,采用多重集典型相关分析(MCCA)分别对各个层次上的局部幅度、局部相位以及局部方位进行融合。融合得到的特征矢量包含了不同层次之间各类特征的内在相关性。在分类阶段,采用联合稀疏表示(JSR)对3类特征融合得到的特征矢量进行联合决策,进一步发掘不同特征之间的内在相关性。最后,根据联合稀疏表示输出的重构误差判定目标类别。基于MSTAR数据集对提出方法进行了性能测试,结果证明了其有效性。
A Synthetic Aperture Radar(SAR)target recognition method based on the multi-scale monogenic features is proposed.To fully exploit the discrimination capability of the multi-scale monogenic features the Multiset Canonical Correlation Analysis(MCCA)is used to fuse the different types of monogenic features from different scales including local amplitude local phase and local orientation which results in a feature vector containing the internal correlations of each kind of feature.In the classification stage the Joint Sparse Representation(JSR)is employed to classify the feature vector fused by the three kinds of features and to further exploit the internal correlations of different types of features.Finally the target type is decided according to the reconstruction errors from JSR.Experiments are conducted on the Moving and Stationary Target Acquisition and Recognition(MSTAR)dataset to evaluate the performance of the proposed method and the results prove the validity of the proposed method.
作者
王源源
WANG Yuan-yuan(Chengdu College University of Electronic Science and Technology of China Chengdu 611731,China)
出处
《电光与控制》
CSCD
北大核心
2019年第10期7-11,29,共6页
Electronics Optics & Control
基金
四川省高校计算机基础教育研究会“教育教学改革研究”(2015-09)
关键词
合成孔径雷达
目标识别
单演信号
多重集典型相关分析
联合稀疏表示
Synthetic Aperture Radar(SAR)
target recognition
monogenic signal
Multiset Canonical Correlation Analysis(MCCA)
Joint Sparse Representation(JSR)
