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基于核Rayleigh商二次相关滤波器的红外目标检测 被引量：5

Infrared target detection using kernel Rayleigh quotient quadratic correlation filter
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摘要 Rayleigh商二次相关滤波器(RQQCF)是一种重要的目标检测方法,但其直接对原始图像数据进行操作,目标检测效果不总是很理想.核方法可以描述图像的高阶统计特性,有效抑制噪声及杂波,提高目标检测效果.将Ray-leigh商二次相关滤波器映射到高维核特征空间,完整推导核Rayleigh商二次相关滤波器(KRQQCF),提出用核特征提取方法解决其实现问题,并将算法用到红外目标检测中.实验中以核主成分分析(KPCA)特征提取方法为例,用真实场景下采集的红外数据对KRQQCF的性能进行检验,结果表明KRQQCF的性能明显高于RQQCF,该方法能实现对红外目标的准确检测,是一种有效的红外目标检测方法. Rayleigh quotient quadratic correlation filter（RQQCF） is an important technique for target detection.Since it operates directly on image data,satisfying results can＇t be always achieved when it is used in infrared target detection.Higher-order statistical properties of the image can effectively suppress the noise and clutter and improve the detection results which can be realized by means of kernel method in kernel space.In this paper,kernel Rayleigh quotient quadratic correlation filter（KRQQCF） was developed by extending RQQCF to the higher-dimensional space,i.e.,the kernel space.Though the derivation was completed,this kernel filter couldn＇t be achieved directly.Kernel feature extraction method was proposed to settle this problem.The algorithm was used to detect infrared targets,and kernel principal component analysis（KPCA） was adopted to obtain this KRQQCF in experiments.Experimental results using real-life infrared images confirm the excellent performance of KRQQCF,and that KRQQCF outperforms RQQCF significantly in infrared target detection.Consequently,KRQQCF is an effective method for infrared target detection and can achieve accurate detection results.
作者 吴燕茹 程咏梅 赵永强 高仕博
机构地区 西北工业大学自动化学院
出处 《红外与毫米波学报》 SCIE EI CAS CSCD 北大核心 2011年第2期142-148,共7页 Journal of Infrared and Millimeter Waves
基金 国家自然科学基金(60634030 60602056) 高等学校博士学科点专项科研基金(20060699032) 航空科学基金(2007EC53037)
关键词 核Rayleigh商二次相关滤波器 Rayleigh商二次相关滤波器 红外目标检测 核主成分分析 kernel Rayleigh quotient quadratic correlation filter Rayleigh quotient quadratic correlation filter infrared small targets detection kernel principal component analysis（KPCA）
分类号 TP391.4 [自动化与计算机技术—计算机应用技术]
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参考文献10

  • 1Khan J F, Alam M S, Bhuiyan S. Automatic target detection in forward-looking infrared imagery via probabilistic neural networks [ J ]. Applied Optics, 2009,48 ( 3 ) : 46d-476.
  • 2Liu R, Liu E, Yang J, et al. Infrared small target detection with kernel Fukunaga-Koontz transform [ J ]. Measurement Science and Technology, 2007,18 ( 9 ) : 3025-3035.
  • 3Ragothaman P, Mikhael W B, Muise R, et al. Adaptive determination of eigenvalues and eigenvectors from perturbed autocorrelation matrices for automatic target recognition[ J]. Proc. SPIE ,2006 ,6234 :62340F.
  • 4Ragothaman P, Mahalanobis A, Muise R, et al. A performance comparison of the transform domain Rayleigh quotient quadratic correlation filter (TDRQQCF) approach to the regularized RQQCF[ J ]. Proc. SPIE,2008,6967:69670P.
  • 5Mikhael W B, Ragothaman P, Muise R, et al. An efficient quadratic correlation filter for automatic target recognition [ J]. Proc. SPIE,2007,6566:65660W.
  • 6Mahalanobis A, Muise R R, Stanfill S R, et al. Design and application of quadratic correlation filters for target detection [ J ]. IEEE Transactions on Aerospace and Electronic Systems, 2004,40 ( 3 ) : 837-850.
  • 7Huo X, Elad M, Flesia A G, et al. Optimal reduced-rank quadratic classifiers using the Fukunaga-Koontz transform with applications to automated target recognition[ J]. Proc. SPIE, 2003,5094 : 59-72.
  • 8Mahalanobis A, Muise R R, Stanfill S R. Quadratic correlation filter design methodology for target detection and surveillance applications [ J ]. Applied optics, 2004,43 ( 27 ) : 5198-5205.
  • 9Sims S, Mahalanobis A. Performance evaluation of quadratic correlation filters for target detection and discrimination in infrared imagery [ J ]. Optical Engineering, 2004,43 ( 8 ) : 1705.
  • 10Jian Y, Frangi A F, Jing-Yu Y, et al. KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition [ J ]. IEEE Transactions on Pattern Analysis and Machine Intelligence ,2005,27 (2) :230-244.

同被引文献46

  • 1曾明,李建勋.基于自适应形态学Top-Hat滤波器的红外弱小目标检测方法[J].上海交通大学学报,2006,40(1):90-93. 被引量：28
  • 2张惠娟,梁彦,程咏梅,潘泉,张洪才.运动弱小目标先跟踪后检测技术的研究进展[J].红外技术,2006,28(7):423-430. 被引量：26
  • 3姜斌,王宏强,黎湘,郭桂蓉.海杂波背景下的目标检测新方法[J].物理学报,2006,55(8):3985-3991. 被引量：22
  • 4Mahalanobis A, Muise R R, Stanfill S R, et al. Design and Application of Quadratic Correlation Filters for Target Detection[J]. IEEE Transactions on Aerospace and Electronic Systems, 2004, 40(3): 837-850.
  • 5Mahalanobis A, Muise R R. Quadratic Correlation Filters for Optical Correlators[C]//Proc. of SP1E'03. Orlando, USA:Is. n.], 2003.
  • 6Muise R R, Mahalanobis A, Mohapatra R. Constrained Quadratic Correlation Filters for Target Detection[J]. Applied Optics, 2004, 43(2): 304-314.
  • 7Liu Ruiming, Liu Erqi, Yang Jie. Infrared Small Target Detection with Kernel Fukunaga-Koontz Transform[J]. Measurement Science and Technology, 2007, 18(9): 3025- 3035.
  • 8Wei Kun, Zhao Yongqiang, Pan Quan, et al. IR Target Detection Based on Kernel PCA and Quadratic Correlation Filters[C]//Proc. of the 4th International Conference on Image and Graphics. [S. 1.]: IEEE Press, 2007.
  • 9Muise R R, Mahalanobis A, Mohapatra R. Constrained Qua- dratic Correlation Filters for Target Detection[J]. Applied Optics, 2004, 43(2): 304-314.
  • 10Muise R R. Quadratic Filters for Automatic Pattern Recog- nition[D]. Orlando, UAS: University of Central Florida, 2003.

引证文献5

二级引证文献21

红外与毫米波学报
2011年 第2期

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