| 图像分块匹配的SAR目标识别方法 |
| |
| 引用本文： | 马丹丹.图像分块匹配的SAR目标识别方法[J].红外与激光工程,2021,50(10):20210120-1-20210120-8. |
| |
| 作者姓名： | 马丹丹 |
| |
| 作者单位： | 佳木斯大学 信息电子技术学院，黑龙江 佳木斯 154000 |
| |
| 基金项目： | 佳木斯大学科研项目（L2012-084） |
| |
| 摘 要： | 提出基于分块匹配的合成孔径雷达（Synthetic Aperture Radar，SAR）目标识别方法。对待识别SAR图像进行4分块处理，每个分块描述目标的局部区域。对于每个分块，基于单演信号构造特征矢量，描述其时频分布以及局部细节信息。单演信号从幅度、相位以及局部方位3个层次对图像进行分解，可有效描述图像的局部变化情况，对于扩展操作条件下的目标变化分析具有重要的参考意义。对于构造得到的4个特征矢量，分别采用稀疏表示分类（Sparse Representation-based Classification，SRC）分别进行分类，获得相应的重构误差矢量。在此基础上，按照线性加权融合的基本思想，通过构造随机权值矩阵进行分析。对于不同权值矢量下获得的结果，经统计分析构造有效的决策变量，通过比较不同训练类别的结果，判定测试样本的类别。所提方法在特征提取和分类决策过程中充分考虑SAR图像获取条件的不确定，通过统计分析获得最优决策结果。实验在MSTAR数据集上设置和开展，包含了1类标准操作条件和3类扩展操作条件。通过与现有几类方法对比，有效证明了所提方法的有效性。
|
| 关 键 词： | 合成孔径雷达 目标识别 图像分块 单演信号 随机权值 |
| 收稿时间： | 2021-04-10 |
SAR target recognition based on image blocking and matching |
| |
| Affiliation: | College of Information Science & Electronic Technique, Jiamusi University, Jiamusi 154000, China |
| |
| Abstract: | A synthetic aperture radar (SAR) target recognition method based on image blocking and matching was proposed. The tested SAR image was blocked into four patches, which described the local regions of the target, respectively. For each SAR image patch, the monogenic signal was employed to construct a feature vector, which described its time-frequency distribution and local details. The monogenic signal decomposed the input image from amplitude, phase, and local orientation. Therefore, it could reflect the local variations in the image so providing more reference information for the analysis of target changes under the extended operating conditions. For the 4 feature vectors, the sparse representation-based classification (SRC) was used for classification and produce the corresponding reconstruction error vectors. Accordingly, based on the linear weighting fusion, the random weight matrix was constructed for analysis. For the results from different weight vectors, an effective decision variable was defined based on statistical analysis. By comparison of the decision values of different classes, the target label of the test sample could be decided. The proposed method made sufficient analysis of the uncertainties about the operating conditions during SAR image measurement, an optimal decision was made based on statistical analysis. Experiments were set up and conducted on the MSTAR dataset including one standard operating condition and three extended operating conditions. Compared with several present methods, the results confirmed the validity of the proposed method. |
| |
| Keywords: | |
|
| 点击此处可从《红外与激光工程》浏览原始摘要信息 |
|
点击此处可从《红外与激光工程》下载全文 |
|
