期刊文献+

一种提高SAR目标识别率的有效方法 被引量:3

Efficient Approach of Improving SAR ATR Performance
下载PDF
导出
摘要 在合成孔径雷达自动目标识别SARATR中,SAR像的预处理是提高识别率的关键技术之一。给出了一种简单有效的SAR图像预处理方法,该方法首先对SAR目标像进行对数变换后,再做傅立叶变换。经预处理后的SAR像用支持矢量机SVM分类器进行目标识别。实验结果表明:本方法不但有效地提高了目标识别率,而且保证了目标的平移不变性并具有良好的推广能力。 The preprocessing of SAR images is one of the key issues to improve SAR ATR performance.A simple and efficient approach of processing SAR images before recognition is presented in this paper.SVM classifier is used to implement target recognition after prepro-cessing.Experimental results showed a better performance of classification,generalization as well as a shift-invariance of target.
出处 《中国民航学院学报》 2003年第3期6-9,13,共5页 Journal of Civil Aviation University of China
基金 国家自然科学基金资助项目(69902009 60272049) 中科院自动化所模式识别国家重点实验室开放课题 中国民用航空总局教育教学研究项目(0605)
关键词 合成孔径雷达 自动目标识别 对数变换 傅立叶变换 支持矢量机 synthetic aperture radar(SAR) automatic target recognition(ATR) log-transform Fourier transform support vector machine(SVM)
  • 相关文献

参考文献10

  • 1Leslie M Novak,Gregory J Owirka,William S Brower,et al.The automatic target recognition system in SAIP [J]. The Lincoln Laboratory Journal, 1997,10(2) : 186-202.
  • 2Timothy Ross,Stephen Worrell,Vincent Velten,et al. Standard SAR ATR evaluation experiment using the MSTAR public release data set[J].SPIE, 1998,3370(4) :566-573.
  • 3Zhao Qun,Principe Jose C,Brennan Victor L,et al. Synthetic aperture radar automatic target recognition with three strategies of learning and representation [J].Opt Eng,2000,39(5):1230-1244.
  • 4Bike Bryant,Fred Garber. SVM Classifier applied to the MSTAR public data set[J].SPIE, 1999,3271 (4) :355-360.
  • 5Zhao Qun,Principo Jose C. Support vector machine for SAR automatic target recognition[J]. IEEE Transactions on Aerospace and Electronic System,2001,37(2):643-654.
  • 6Vapnik V. The Nature of Statistical Learning Theory[M]. New York : Springer-Verlag Inc, 1995.
  • 7David Casasent,Satoshi Ashizawa. Synthetic aperture radar detection,recognition and clutter rejection with new minimum noise and correlation energy filters [J].Opt Eng,1997. (10):2729-2736.
  • 8Keydel Eric R,Shung Wu Lee,Moore T. MSTAR extended operating conditions,a tutorial[J].SPIE,1996,2757(3):228-242.
  • 9Frieβ T. Support Vector Neural Networks: the Kernel Adatron with Bias and Soft-margin[R].UK:University of Sheffield,1998.
  • 10Zhao Qun,Xu Dongxin,Jose C. Principe,pose e stimation of SAR automatic target recognition[A].Montreal,CA :Proceedings of Image Understanding Workshop, 1998, ( 11 ) : 827-832.

同被引文献19

  • 1韩先锋,李俊山,毕义明,孙满囤.基于混合遗传算法的景象匹配技术研究[J].微电子学与计算机,2004,21(8):102-105. 被引量:3
  • 2李素敏,张万清.地磁场资源在匹配制导中的应用研究[J].制导与引信,2004,25(3):19-21. 被引量:54
  • 3姜百汇.图像匹配技术在巡航导弹中的应用[J].战术导弹技术,2001(2):54-57. 被引量:8
  • 4康欣,韩崇昭,杨艺.基于结构的SAR图像配准[J].系统仿真学报,2006,18(5):1307-1310. 被引量:13
  • 5Novak L M, Halversen S D, Owirka G J, et al. Effects of polarization and resolution on SAR ATR [ J ]. IEEE Transactions on Aerospace and Electronic Systems, 1997, 33( 1 ) : 102-115.
  • 6Campbell W M, Sturim D E, Reynolds D A. Support vector machines using GMM supervectors for speaker verification [ J ]. IEEE Signal Processing Letters, 2006, 13 ( 5 ) : 308-311.
  • 7Ganapathiraju A, Hamaker J E, Picone J. Applications of support vector machines to speech recognition [ J ]. IEEE Transactions on Signal Processing, 2004, 52 (8) : 2348-2355.
  • 8Zhao Q, Principe J C. Support vector machines for SAR automatic target recognition [ J ] . IEEE Transactions on Aerospace and Electronic Systems, 2001, 37 (2) : 643-654.
  • 9Schumacher R, Schiller J. Non-cooperative target identification of battlefield target classification results based on SAR images [ J ]. Proceedings of the IEEE, 2005,93 : 167-172.
  • 10Douvilie P L. Measured and predicted synthetic aperture radar target comparison [ J ]. IEEE Transactions on Aerospace and Electronic Systems, 2002, 38( 1 ) : 25-37.

引证文献3

二级引证文献7

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