基于二维随机投影特征典型相关分析融合的SAR ATR方法

SAR ATR method based on canonical correlations analysis of features extracted by 2D random projection

合成孔径雷达(Synthetic aperture radar,SAR)自动目标识别(Automatic target recognition,ATR)是现代战场情报侦察、精确打击的重要支撑技术。为提升SAR ATR整体性能,提出基于二维投影特征多重集典型相关分析(Multiset canonical correlations analysis,MCCA)的方法。首先,采用若干二维随机投影矩阵对SAR图像进行特征提取,获得多层次特征描述。考虑到这些结果之间的相关性和可能存在的冗余及干扰,进一步通过MCCA对它们进行融合处理,获取单一特征矢量。基于稀疏表示分类器(Sparse representation-based classification,SRC)对融合特征矢量进行处理,判决目标类别。实验基于MSTAR数据集开展,对方法性能进行检验确认,结果能够验证其有效性。

Synthetic aperture radar(SAR)automatic target recognition(ATR)is an important support technology for modern battlefield intelligence reconnaissance and precision strikes.In order to improve the overall performance of SAR ATR,a method based on multiset canonical correlations analysis(MCCA)of twodimensional(2D)projection features is proposed.First,a series of 2D random projection matrices are used to extract features from SAR images to obtain multi-level feature descriptions.Considering the correlation between these results and the possible redundancy and interference,they are further fused through MCCA to obtain a single feature vector.The sparse representation-based classification(SRC)is used to process the fusion feature vector to determine the target class.The experiment is carried out based on the MSTAR dataset to fully test the proposed methods.The experimental results verify its effectiveness.