期刊文献+

一种有效的SAR自动目标识别方法 被引量:3

AN EFFICIENT SAR AUTOMATIC TARGET RECOGNITION APPROACH
原文传递
导出
摘要 本文给出了一种基于最优线性变换和支持矢量机(Support Vector Machine,SVM)的合成孔径雷达(Synthetic Aperture Radar,SAR)自动目标识别方法。该方法首先对目标图像做线性变换使目标的类间距加大,类内距减小,然后利用SVM分类器实现SAR目标的自动分类。实验结果表明该方法具有良好的识别率和推广性能。 In this paper, an efficient SAR ATR (Synthetic Aperture Radar Automatic Target Recognition) approach based on optimal linear transform and SVM is proposed. First, linear transform of SAR images is performed to maximize the distance between classes and to minimize the distance within classes, then SVM is used to implement target classification. Experimental results illustrate the performance of the proposed approach.
出处 《模式识别与人工智能》 EI CSCD 北大核心 2003年第2期208-212,共5页 Pattern Recognition and Artificial Intelligence
基金 国家自然科学基金(No.69902009) 中国科学院自动化研究所模式识别国家重点实验室开放课题资助项目
关键词 合成孔径雷达 自动目标识别 信号处理 模板匹配 线性变换 支持矢量机 Synthetic Aperture Radar, Automatic Target Recognition, Linear Transform, Support Vector Machine, Template Matching
  • 相关文献

参考文献8

  • 1Ross T,Worrell S,Velten V,Mossing J,Bryant M.Standard SAR ATR Evaluation Experiment Using the MSTAR Public Release Data Set.SPIE,1998,3370:566—573.
  • 2Bryant M,Garber F.SVM Classifier Applied to the MSTAR Public Data Set.SPIE,Orlando,Florida,1999,3721:355—359.
  • 3Zhao Qun,Principe J C.Support Vector Machine for SAR Automatic Target Recognition.IEEE Trans on Aerospace and Electronic System,2001,37(2):643—654.
  • 4Zhao Qun,Principe J C,Brennan V L,Xu Dongxin,Wang Zheng.Synthetic Aperture Radar Automatic Target Recognition with Three Strategies of Learning and Representation.Optical Engineering,2000,39(5):1230—1244.
  • 5Mahalanobis A,Jr.Forman A,Day N,Bower M,Cherry R.Multi.Class SAR ATR Using Shift—Invariant Correlation Filters.Pattem Recognition,1994,27(4):619—626.
  • 6Vapnik V N.Statistical Learning Theory.New York:John Wiley & Sons,Inc.,1998.
  • 7Keydel E R,Lee SW,Moore J T.MSTAR Extended Operating Conditions.A Tutorial.SPIE,1996,2757:228—242.
  • 8Zhao Qun,Xu Dongxin,Principe J C.Pose Estimation of SAR Automatic Target Recognition.In:Proc of Image Understanding Workshop,Monterey,CA,1998,827—832.

同被引文献30

  • 1成功,赵巍,毛士艺.基于快速提升KLDA准则的MSTAR SAR目标特征提取与识别研究[J].航空学报,2007,28(3):667-672. 被引量:2
  • 2Novak L M, Owirka G J,et al. Performance of 10-and 20-1raget MSE classification [J].IEEE Transactions on Aerospace and Electronic Systems,2000,36(4) : 1279 - 1289.
  • 3Jones, B Bhanu. Recognition of articulated and occluded objects [J]. IEEE Transactions on Pattem Analysis and Machine Intelligence, 1999,21 (7) : 603 - 613.
  • 4W S Zheng, J H Lai, S Z Li. 1D-LDA vs 2D-LDA: when is vector-based linear discriminant analysis better than matrixbased[J]. Pattern Recognition, 2008,41 (7) : 2156 - 2172.
  • 5L Ming, B Yang. 2D-LDA: a statistical linear discriminant analysis for image matrix [ J ]. Patten Recognition Letter, 2005,26 (5) :527 - 532.
  • 6S Noushath, G Hemantha Kumar. ( 2D)2LDA: a efficient approach for face recognition [ J ]. Pattem Recognition, 2006, 39(7):1396- 1400.
  • 7M Zhu, A M Martinez. Subclass discriminant analysis[J].IEEE Transactions, 2006, PAMI-28 (8) : 1274 - 1286.
  • 8Ross T, Worrell S, Velten V, et al. Standard SAR ATR evaluation experiment using the MSTAR public release dataset[ A].IEEE of the International Society for Optical Engineering (SPIE) [ C]. Florida, 1998,3370:566 - 573.
  • 9K Fukunaga. Introduction to Statistical Pattern Recognition (2^nd edition) [M]. Academic Press, 1990.
  • 10B Chen, L Yuan, H W Liu, Z Bao. Kernel subclass discrirni- nant analysis[J].Neurocomputing, 2007, 71 ( 1 - 3 ) : 455 - 458.

引证文献3

二级引证文献13

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部