| 申 伟,石 平.单演信号随机加权融合的 SAR 图像目标识别方法[J].电子测量与仪器学报,2020,34(9):181-187 |
| 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 |
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| DOI： |
| 中文关键词: 合成孔径雷达 目标识别 稀疏表示 随机权值 决策变量 |
| 英文关键词:synthetic aperture radar target recognition sparse representation-based classification random weights decision value |
| 基金项目:河南省高等学校重点科研项目计划(19A510023)资助 |
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| 中文摘要: |
| 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。 |
| 英文摘要: |
| 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 multicomponent 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. |
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