| 赵海发.基于 SAR 图像分块匹配的目标识别方法[J].电子测量与仪器学报,2020,34(10):171-177 |
| 基于 SAR 图像分块匹配的目标识别方法 |
| SAR target recognition based on image blocking and matching |
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| DOI： |
| 中文关键词: 合成孔径雷达 目标识别 图像分块 单演信号 稀疏表示分类 随机权值 |
| 英文关键词:synthetic aperture radar target recognition image blocking monogenic signal sparse representation-based classification random weight |
| 基金项目:国家自然科学基金(51375145)资助项目 |
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| 中文摘要: |
| 提出基于分块匹配的合成孔径雷达(SAR)目标识别方法。 对待识别 SAR 图像进行 4 分块处理,分别进行分析和匹配。
对于每个 SAR 图像分块,采用单演信号描述其时频分布以及局部细节信息,进而构造特征矢量。 采用稀疏表示分类( SRC)分
别对 4 个分块的单演特征矢量进行分类,获得相应的重构误差矢量。 对于 4 个重构误差矢量,利用多组随机权值矢量对它们进
行加权并对所有的结果进行统计分析。 最后,基于统计特征构造决策变量用于测试样本的类别确认。 利用 MSTAR 数据集进行
实验,结果表明了方法的有效性。 |
| 英文摘要: |
| This paper proposes a synthetic aperture radar (SAR) target recognition method based on image blocking and matching. The
test SAR image is blocked into four patches, which are analyzed and matched separately. For each SAR image patch, the monogenic
signal is employed to describe its time-frequency distribution and local details thus to construct the feature vector. The sparse
representation-based classification (SRC) is used to classify the four monogenic feature vectors and produce the reconstruction error
vectors. For the four reconstruction error vectors, a rich set of random weight vectors are used to fuse them and all the results are
analyzed in a statistical way. Finally, the decision value is designed to determine the target label. The proposed method is tested on the
MSTAR dataset. The results confirm the validity of the proposed method. |
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