单演信号随机加权融合的SAR图像目标识别方法

Randomly weighting of monogenic signal for target recognition of SAR images

提出基于单演信号决策层随机加权融合的合成孔径雷达(SAR)图像目标识别方法。采用稀疏表示分类(SRC)分别对SAR图像分解得到的多层次、多成分单演信号表示实施决策。对于误差矢量,通过随机权值矩阵的方式进行融合。该矩阵中包含大量随机权值,根据融合后的结果可以获得不同类别误差统计结果,定义决策变量反映不同类别相关性。最后,按照最小误差进行类别决策。在MSTAR数据集上进行广泛实验并与多类现有方法进行对比,结果表明提出方法可有效提升SAR目标识别整体性能。

This article proposed a synthetic aperture radar(SAR)target recognition method by decision level fusion of monogenic signal using random weighting.The sparse representation-based classification(SRC)was employed to classify the multi-scale and multi-component monogenic representations.For erro vectors,the random weight matrix was designed to perform the fusion,which includes a large volume of random weight vectors.The statistics of the fused reconstruction errors were analyzed to form the decision values,which reflect the correlations between the test sample and different classes.Finally,the target label was decided by comparison of the decision values.Extensive experiments were conducted on the MSTAR dataset to evaluate the proposed method,which was compared with some existing SAR target recognition methods.The results showed that the proposed method could effectively improve the overall performance.