| 基于SVM/LDA的SAR图像目标鉴别方法 |
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| 作者姓名： | 赵凤军 高东生 贾亚飞 |
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| 作者单位： | 1. 中国科学院电子学研究所, 北京 100190;2. 沈阳军区司令部第二部, 沈阳 110805;3. 中国科学院研究生院, 北京 100049 |
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| 基金项目： | 国家863计划项目(2009AA7050613)资助 |
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| 摘 要： | 提出一种基于主成分分析和支持向量机与线性判别分析结合算法的合成孔径雷达(synthetic aperture radar,SAR)图像目标鉴别方法. 利用主成分分析算法对SAR图像向量进行降维并提取其全局特征,对降维后的全局特征采用最小类内散度支持向量机算法进行变换,并对变换结果训练生成最佳分类器,进行分类完成目标鉴别. 实验结果表明该方法可以获得较高的分类正确率.
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| 关 键 词： | 合成孔径雷达 主成分分析 线性判别分析 支持向量机 鉴别 |
| 收稿时间： | 2010-12-30 |
| 修稿时间： | 2011-06-03 |
Synthetic aperture radar images target discrimination based on combined SVM and LDA |
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| Authors: | ZHAO Feng-Jun GAO Dong-Sheng JIA Ya-Fei |
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| Institution: | 1. Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China;2. Secondly Bureau of Shenyang Headquarters Troop, Shenyang 110805, China;3. Graduate University, Chinese Academy of Sciences, Beijing 100049, China |
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| Abstract: | We propose a method for synthetic aperture radar images target discrimination based on the principal component analysis and an approach combining support vector machine (SVM) and linear discriminant analysis(LDA). Dimensionality of the image vector is reduced and the global features are extracted by using principal component analysis. The global features are transformed and the results are used to generate classifiers which complete target discrimination. The results show the high performance of the proposed method. |
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| Keywords: | synthetic aperture radar (SAR) principal component analysis (PCA) linear discriminant analysis (LDA) support vector machine (SVM) discrimination |
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