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Volume 31 Issue 5
Dec.  2010
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Pan Zhuo, Wang Bin-hui, Gao Xin, Wang Yan-fei. Kernel Correlation Filter for Vehicle Detection and Recognition in SAR Images[J]. Journal of Electronics & Information Technology, 2009, 31(5): 1148-1152. doi: 10.3724/SP.J.1146.2008.00221
Citation: Pan Zhuo, Wang Bin-hui, Gao Xin, Wang Yan-fei. Kernel Correlation Filter for Vehicle Detection and Recognition in SAR Images[J]. Journal of Electronics & Information Technology, 2009, 31(5): 1148-1152. doi: 10.3724/SP.J.1146.2008.00221

Kernel Correlation Filter for Vehicle Detection and Recognition in SAR Images

doi: 10.3724/SP.J.1146.2008.00221
  • Received Date: 2008-02-27
  • Rev Recd Date: 2008-10-27
  • Publish Date: 2009-05-19
  • SAR target detection and recognition is sensitive to targets azimuth. To solve the problem, based on correlation theory and kernel feature analysis, a kernel correlation filter which is strongly robust to targets azimuth distortion is proposed. The novel filter exploits eigenvectors to reduce the dependence of the training set and extends linear combination of eigenvectors nonlinearly to improve the classification. Moreover, to keep the computation tractable in high dimensional space, the kernel function is employed. Comparative tests using MSTAR database demonstrate the kernel correlation filter performs high detection probability with low false alarm probability and implements target detection and recognition accurately without templates and target poses estimation.
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  • Ross T D, Bradley J J, and Hudson L J,et al.. SAR ATR -Sowhats the problem? - An MSTAR perspective. Proceedingsof SPIE-Algorithms for Synthetic Aperture Radar ImageryVI, Orlando, Florida, April 1999, 3721: 662-672.[2]Devore M D and OSullivan J A. Performance complexitystudy of several approaches to automatic target recognitionfrom SAR images[J].IEEE Trans. on Aerospace and ElectronicSystems.2002, 38(2):632-648[3]Shenoy R K. The design and use of unconstrained imagefilters and features for SAR detection and recognition. [Ph.D.dessirtation], Carnegie Mellon University, 2001.[4]Singh R. Advanced correlation filter for multi-class syntheticaperture radar detection and classification. [M.S thesis],Carnegie Mellon University, 2002.[5]Casasent D and Patnaik R. Automated synthesis of distortion-invariant filters: AutoMinace. Proceedings of SPIEIntelligentRobots and Computer Vision XXIV, 2006, Boston,USA, 6384: 638401.[6]Patnaik R and Casasent D. MSTAR object classification andconfuser and clutter rejection using Minace Filters.Proceedings of SPIE-Automatic Target Recognition XVI,Orlando, Florida, April 2006, 6234: 62340S1.[7]Vijaya Kumar B V K. Tutorial survey of composite filterdesigns for optical correlators[J].Applied Optics.1992, 31(23):4773-4801[8]Mahalanobis A, Vijaya Kumar B V K, and Casasent D.Minimum average correlation energy filter[J].Applied Optics.1987, 26(17):3633-3640[9]Rsvichandran G and Casasent D. Minimum noise andcorrelation energy filter[J].Applied Optics.1992, 31(11):1823-1833[10]Isaacs J C, Foo S Y, and Bases A M. Novel kernels and kernelPCA for patten recognitoin. Proceedings of the IEEEInternational sumposium on Computational Intelligence inRobotics and Automation, FL, USA, June, 2007: 438-443.[11]Vijaya Kumar B V K and Xie C Y. Correlation patternrecognition for face recognition[J].Proc. IEEE.2006, 94(11):1963-1975[12] MSTAR Public Release Dataset, website: https:// www.sdms.afrl.af.mil/datasets/mstar/
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