| 基于主元分析和稀疏表示的SAR图像目标识别 |
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| 引用本文： | 刘中杰,庄丽葵,曹云峰,丁萌.基于主元分析和稀疏表示的SAR图像目标识别[J].系统工程与电子技术,2013,35(2):282-286. |
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| 作者姓名： | 刘中杰 庄丽葵 曹云峰 丁萌 |
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| 作者单位： | 1. 南京航空航天大学自动化学院， 江苏 南京 210016；;
2. 南京航空航天大学高新技术研究院，江苏 南京 210016；;
3. 南京航空航天大学民航学院， 江苏 南京 210016 |
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| 基金项目： | 国家自然科学基金(61203170);航空科学基金(20110752005);江苏省普通高校研究生科研创新计划(CXLX12_0160)资助课题 |
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| 摘 要： | 现有的合成孔径雷达图像目标识别方法通常包括图像预处理、特征提取和识别算法3部分。但是，预处理算法的自适应性很难得到保证。提出了一种基于主元分析和稀疏表示的目标识别算法。首先，阐述了稀疏表示和重构的基本理论；其次，提出了基于主元分析和稀疏表示的合成孔径雷达图像目标识别算法；最后，选取MSTAR数据库中的5类合成孔径雷达目标图像进行仿真。结果表明，在没有预处理的情况下，该算法仍能有效地识别目标，与主元分析和三阶近邻的识别算法相比，具有较高的识别率和鲁棒性。
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| 关 键 词： | 目标识别 稀疏表示 主元分析 合成孔径雷达图像 |
Target recognition of SAR images using principal component analysis and sparse representation |
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| LIU Zhong-jie,ZHUANG Li-kui,CAO Yun-feng,DING Meng.Target recognition of SAR images using principal component analysis and sparse representation[J].System Engineering and Electronics,2013,35(2):282-286. |
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| Authors: | LIU Zhong-jie ZHUANG Li-kui CAO Yun-feng DING Meng |
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| Institution: | 1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; ;
2. Academy of Frontier Science, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; ;
3. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China |
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| Abstract: | With the existing target recognition algorithms of synthetic aperture radar (SAR) images, image preprocessing, feature extraction and recognition algorithm are usually carried out. The adaptability of the preprocessing algorithm is difficult to be guaranteed. A target recognition algorithm using principal component analysis (PCA) and sparse representation is proposed. Firstly, the basic theory of sparse representation and reconstruction is presented. Secondly, an SAR image target recognition algorithm is presented using PCA and sparse representation. Finally, an experiment with five kinds of SAR target images in the MSTAR database is given. The simulation results show that this algorithm can still recognize the target effectively without preprocessing. Compared with the PCA and the third order nearest neighbor algorithm, the proposed algorithm has a higher recognition rate and robustness. |
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