红外成像系统性能评估方法研究
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摘要
随着新工艺、新结构和新技术的引入,影响新型红外成像系统性能的因素发生了明显的变化,针对以往典型性能评估方法和模型对这些新技术考虑不足或没有考虑的问题,本文从三个方面开展了针对新型红外成像系统的性能评估方法研究,以适应不断发展的新型红外成像系统性能评估的需要:
(1)基于全数字仿真的成像系统性能评估。利用三维场景模型和红外成像系统模型,结合三维驱动引擎及显示交互技术,完成成像红外导引系统模型与三维场景模型的对接,建立了综合考虑目标-背景-大气-成像系统的全数字仿真模型,实现了基于数字仿真的红外成像系统性能评估;
(2)基于客观TOD判别模型的成像系统性能评估。以人眼视觉系统的信号传递特性以及眼/脑系统信息处理特性为基础,结合三角形方向判别阈值法的特点,建立了用于替代观察者进行TOD测量的客观判别模型,开展了客观判别模型与观察者的对比试验。分析结果表明,在常用的角空间频率范围内,客观判别能够准确预测观察者对三角形样条方向的判别性能,可以替代观察者进行TOD曲线测量,从而实现更为准确的成像系统性能评估;
(3)基于背景杂波度量的成像系统性能预测。首先结合红外成像系统的信号传输和处理过程,分析了背景杂波的本质与属性,以及背景杂波对红外成像系统目标获取性能的影响。其次,针对不同的杂波评估需求,建立了两种杂波度量尺度:基于结构相似度的背景杂波度量尺度以及基于CRLB的背景杂波度量尺度。开展外场测量试验,获取试验处理数据。试验结果分析表明,两种杂波尺度都与背景信号对目标探测系统的干扰有很好的一致性,可以用于ATR系统目标获取性能预测。
With the development of electro-optical technology, factors that affect the performance of infrared imaging systems (IRIS) have been changed. Traditional performance characterization methods or models become insufficient for the novel IRIS since they only consider a few or even none of these factors. Hence, it is necessary to improve traditional performance evaluation methods to satisfy the performance evaluation of the novel IRIS. In this paper, the following works are made.
(1) Digital simulation for IRIS performance evaluation. By techniques of 3D-engine-control and display-interrelation, the interface between the 3D scene model and the IRIS model is constructed. And the digital simulation system for IRIS performance evaluation is set up, which is all inclusive of the components involved in the signal transmission chain, including the targets, the background, the atmosphere and the IRIS. And then, IRIS performance evaluation is performed upon this simulation system.
(2) Objective Triangle Orientation Discrimination (TOD) for IRIS performance evaluation. Based on the characteristics of signal transmitting and information processing of human eye/brain systems, and the mechanism of TOD method, an objective discrimination model (ODM) is constructed. Comparison experiments are held for TOD performance data of ODM and civilian observers. Experimental results show that ODM can predict the TOD performance of civilian observers well within the common used angle frequency range. Thus, ODM can be used for TOD measurements instead of civilian observers, which results in a better IRIS performance evaluation by eliminating the artificial influence.
(3) Background clutter characterization for IRIS performance evaluation. Background clutter has been gradually developing into the limiting factor of IRIS targets acquisition performance. Thus, it is important to find out how and in which way clutter affects IRIS performance. Based on the procedure of signal transmitting and processing of IRIS, the nature and characteristics of background clutter are analyzed, together with the mechanism it affects IRIS performance. Then, two clutter metrics are developed for clutter quantification, which are the Target Structure Similarity Metric (CMTSSM) and the CRLB Metric. Characteristics of the two clutter metrics are analyzed. With real IR image data, experiments are held for the relation between the clutter metrics outputs and the Automatic Target Recognition (ATR) system performance. Experimental results show that the trends of the clutter level varying with the target range are in good consistency with those of the ATR false alarm probability, which indicates the proposed clutter metrics are strong predictors of the background clutter level and can be used for ATR performance evaluation.
(1)基于全数字仿真的成像系统性能评估。利用三维场景模型和红外成像系统模型,结合三维驱动引擎及显示交互技术,完成成像红外导引系统模型与三维场景模型的对接,建立了综合考虑目标-背景-大气-成像系统的全数字仿真模型,实现了基于数字仿真的红外成像系统性能评估;
(2)基于客观TOD判别模型的成像系统性能评估。以人眼视觉系统的信号传递特性以及眼/脑系统信息处理特性为基础,结合三角形方向判别阈值法的特点,建立了用于替代观察者进行TOD测量的客观判别模型,开展了客观判别模型与观察者的对比试验。分析结果表明,在常用的角空间频率范围内,客观判别能够准确预测观察者对三角形样条方向的判别性能,可以替代观察者进行TOD曲线测量,从而实现更为准确的成像系统性能评估;
(3)基于背景杂波度量的成像系统性能预测。首先结合红外成像系统的信号传输和处理过程,分析了背景杂波的本质与属性,以及背景杂波对红外成像系统目标获取性能的影响。其次,针对不同的杂波评估需求,建立了两种杂波度量尺度:基于结构相似度的背景杂波度量尺度以及基于CRLB的背景杂波度量尺度。开展外场测量试验,获取试验处理数据。试验结果分析表明,两种杂波尺度都与背景信号对目标探测系统的干扰有很好的一致性,可以用于ATR系统目标获取性能预测。
With the development of electro-optical technology, factors that affect the performance of infrared imaging systems (IRIS) have been changed. Traditional performance characterization methods or models become insufficient for the novel IRIS since they only consider a few or even none of these factors. Hence, it is necessary to improve traditional performance evaluation methods to satisfy the performance evaluation of the novel IRIS. In this paper, the following works are made.
(1) Digital simulation for IRIS performance evaluation. By techniques of 3D-engine-control and display-interrelation, the interface between the 3D scene model and the IRIS model is constructed. And the digital simulation system for IRIS performance evaluation is set up, which is all inclusive of the components involved in the signal transmission chain, including the targets, the background, the atmosphere and the IRIS. And then, IRIS performance evaluation is performed upon this simulation system.
(2) Objective Triangle Orientation Discrimination (TOD) for IRIS performance evaluation. Based on the characteristics of signal transmitting and information processing of human eye/brain systems, and the mechanism of TOD method, an objective discrimination model (ODM) is constructed. Comparison experiments are held for TOD performance data of ODM and civilian observers. Experimental results show that ODM can predict the TOD performance of civilian observers well within the common used angle frequency range. Thus, ODM can be used for TOD measurements instead of civilian observers, which results in a better IRIS performance evaluation by eliminating the artificial influence.
(3) Background clutter characterization for IRIS performance evaluation. Background clutter has been gradually developing into the limiting factor of IRIS targets acquisition performance. Thus, it is important to find out how and in which way clutter affects IRIS performance. Based on the procedure of signal transmitting and processing of IRIS, the nature and characteristics of background clutter are analyzed, together with the mechanism it affects IRIS performance. Then, two clutter metrics are developed for clutter quantification, which are the Target Structure Similarity Metric (CMTSSM) and the CRLB Metric. Characteristics of the two clutter metrics are analyzed. With real IR image data, experiments are held for the relation between the clutter metrics outputs and the Automatic Target Recognition (ATR) system performance. Experimental results show that the trends of the clutter level varying with the target range are in good consistency with those of the ATR false alarm probability, which indicates the proposed clutter metrics are strong predictors of the background clutter level and can be used for ATR performance evaluation.
引文
[1] Lettington A H, Hong Q H, A discrete modulation transfer function for focal plane arrays. Infrared Physics. 1993. 34(1):109-114.
[2] A Marshall, K.St.J.Mruphy. Modeling and simulation of IR scenes and sensors in the UK-current and future capabilities. SPIE Proceedings on Infrared Technology and Applications XXII. 1996. Vol.2744: 591-598.
[3] Christer Wigren. Model of image generation in optronic(electro-optical) sensor systems(IGOSS). SPIE Proceedings on Infrared Imaging Systems: Design, Analysis, Modeling and Testing IX. 1998. Vol.3377: 89-94.
[4] Christelle Garnier, Rene Colloree, Jihed Flifla et al. Physically-based infrared sensor efforts modeling. SPIE Proceedings on Infrared Imaging Systems: Design, Analysis, Modeling and Testing X. 1999. Vol.3701: 81-94.
[5] Douglas C.McKee. Sensor modeling using programmable interface for Hardware-in-the-loop IR/EO testing. SPIE Proceedings on Technologies for Synthetic Environments: Hardware-in-the-Loop Testing IV. 1999. Vol.3697: 470-481.
[6]刘鑫.三维地面场景红外仿真方法研究.西安电子科技大学硕士论文. 2003.
[7]王学伟,熊璋,沈同圣,周晓东.红外成像制导导弹虚拟战场仿真.系统仿真学报. 2002. Vol.14(7): 922-924.
[8]钱惟贤,陈钱,顾国华,张强.凝视型红外热像仿真技术研究.红外. 2006. Vol.27(6):1-5.
[9]陈澄,石静,费锦东.凝视红外成像制导系统数学建模与仿真技术.红外与激光工程. 2007. Vol.36(1):19-22.
[10]彭星光,高晓光.红外成像制导反舰导弹模块化数字仿真.火力与指挥控制. 2008. Vel.33(3):28-34.
[11] Gerald C.Holst. Testing and Evaluation of Infrared Imaging Systems. America: JCD Publishing and SPIE Optical Engineering Press. Second Edition: Preface to the second edition. 2000.
[12] Army Electronic Command. Night vision laboratory performance model for thermal viewing system. AD/A-011212. 1975.
[13] L.Scott, J.D’Agostino, NVEOD FLIR92 Thermal Imaging Systems Performance Model, SPIE Proceedings. 1992. 1689: 194-203.
[14] W.Wittenstein, Thermal Range Model TRM3. SPIE Proceedings. 1998. Vol.3436:413-424,
[15] Vollmerhausen R and Driggers R G,NVTherm: next generation night vision model,Proceeding of IRIS Passive Sensors. 1999. Vol.11:121-134.
[16] Tana Maurer, Ronald G.Driggers, Richard Vollmerhausen, and Melvin Freidman. 2002 NVTherm Improvements.Proc.SPIE. 2002. Vol.4719:15-23.
[17] P.Bijl and J.M.Valeton, TOD, A new method to characterize electro-optical system performance, Proc. SPIE. 1998. Vol.3377: 182-193.
[18] P.Bijl and J.M.Valeton. TOD, the alternative to MRTD and MRC [J], Opt. Eng. 1998. 37(7) :1976-1983.
[19] Joseph Kostrzewa, John Long, John Graff, et al. TOD versus MRT when evaluating thermal imagers that exhibit dynamic performance. Proc. SPIE. 2003. Vol.5076: 220-232.
[20] Piet Bijl, J.Mathieu Valeton, Arie N.de Jong, TOD predicts target acquisition performance and scanning thermal imager, Proc. SPIE. 2000. Vol.4030: 96-103.
[21] Dirk-Jan J.de Lange, J.Mathieu Valeton, Piet Bijl, Automatic characterization electro-optical sensor with image-processing, using the Triangle Orientation Discrimination (TOD) method, SPIE Proceedings. 2000 Vol..4030:104-111.
[22] Bijl P and Valeton J M,“Guidelines for accurate TOD measurement”, SPIE. Proceedings. 1999. Vol.3701:14-25.
[23] Bijl, Piet; Valeton, J. Mathieu. Validation of the new triangle orientation discrimination method and ACQUIRE model predictions using observerperformance date for ship targets. Opt.Eng. 1998. 37(07), 1984-1994.
[24] McHugh, Stephen W.; Irwin, Alan; Valeton, J. M.; Bijl, Piet. TOD test method for characterizing electro-optical system performance. Proc. SPIE. 2001. 4372:39-45.
[25] Piet Bijl, J.Mathieu Valeton and Maarten A.Hogervorst. A critical evaluation of test patterns for EO system performance characterization. Proc.SPIE.2001. 4372:27-38.
[26] Piet Bijl, Maarten A.Hogervorst. A test method for multi-band imaging system. Proc.SPIE. 2003. Vol.5076:208-219.
[27] Piet Bijl, Maarten A. Hogervorst, J. M. Valeton. TOD, NVTherm, and TRM3 model calculations: a comparison. Proc. SPIE. 2002. Vol.4719:51-62.
[28] Ronald G. Driggers, Van Hodgkin, Rich Vollmerhausen, and Patrick O’Shea. Minimum Resolvable Temperature Difference Measurements on Undersampled Imagers. Proc. SPIE. 2003. Vol.5076: 179-189.
[29] Ronald G. Driggers, Rich Vollmerhausen, Wolfgan Wittenstein, Piet Bijl and J.Mathieu Valeton. Infrared Imager Models for Undersampled Imaging Systems.Proc. SPIE. 2000. Vol.5076: 179-189.
[30] Maarten A.Hogervorst, Piet Bijl and J.Mathieu Valeton. Capturing the sampling effects: a TOD sensor performance model [J]. Proc. SPIE. 2001. Vol.4372: 62-73.
[31]金伟其,高稚允等.一代通用组件热像仪静态性能计算机软件包SPTIS,光学技术(增刊). 1995. pp49-53.
[32]周燕,金伟其等.基于人眼视觉的光电成像系统性能评价方法研究.兵工学报. 2002. Vol.23 (4): 504-507.
[33]金伟其,高稚允等.光电成像系统与人眼视觉的匹配问题.红外技术. 2000. Vol.22 (5): 40-44.
[34]李旭东,艾克聪,张安锋.热成像系统MRTD数学模型的研究.应用光学. 2004. Vol.25 (6): 38-42.
[35]陈立学.采样成像系统的传递性能分析.光学学报. 1995. Vol.15(11): 1547-1551.
[36]陈自宽,翟宏琛,母国光.光电成像系统中的光学传递函数.光学技术. 1988,No.1: 57-60.
[37]童默颍,常本康,钱芸生,刘磊.红外焦平面阵列调制传递函数研究.红外与毫米波学报. 2003. Vol.22 (5): 365-367.
[38]王晓蕊,张建奇,常红花,刘鑫.一种表征红外成像系统性能的新方法.红外技术. 2003. Vol.25(2): 24-28.
[39] Xiao-rui Wang, Jian-qi Zhang, Zhuo-xiang Feng, Fang-ming Hu. TOD performance theoretical model for staring thermal imagers. International Journal of Infrared and Millimeter Waves. 2004. Vol.25(11):1645-1656.
[40] Xiao-rui Wang, Jian-qi Zhang, Zhuo-xiang Feng , Hong-hua Chang. Equation-based triangle orientation discrimination sensor performance model based on sampling effects. Applied Optics. 2005. Vol.44(4): 498-505.
[41]王晓蕊,张建奇,冯卓祥,常红花.凝视红外成像系统的三角方向鉴别与之曲线预测.光学学报. 2005. Vol.25 (8): 1036-1041.
[42]王晓蕊.红外焦平面成像系统建模及TOD性能表征方法研究.西安电子科技大学博士论文. 2005.
[43] Jianqi Zhang, xiao-rui Wang, Fang-ming Hu. TOD performance theoretical model for scanning infrared imagers. Infrared physics & technology. 2006. Vol.48 (1): 32-38.
[44]王晓蕊,张建奇,冯卓祥,常红花.三角形方向鉴别阈值性能理论模型研究.红外与毫米波学报. 2006. Vol.25 (2): 118-122.
[45] Z.Avital, Y.Hadar, G. Reiter, G. Tidhar, and S.R Rotmam,“The influence of clutteron human target acquisition,”SPIE Proc. 1992. Vol.1971: 436-446.
[46] K.Gillian, Groves, and M. Kim, et al.,“Quantification of clutter in EO tracking systems,”SPIE Proceedings. 1994. Vol.2221: 287-295.
[47]常洪花.光电图像背景杂波的定量表征及其对成像系统目标获取性能的影响.西安电子科技大学博士论文. 2006.
[48] D. E. Schmieder, M. R. Weathersby,”Detection performance in clutter with variable resolution,”J. IEEE Trans on Aerospace and Electronic Systems. 1983. 19(4): 622-630.
[49] David Klick, Philip Blumunau, Joseph Theriault, Detection of targets in infrared clutter. Proc.SPIE. 2001. Vol.4370: 120-133.
[50] Aaron D.Lanterman, Joseph A, O’Sullivan, Michael l. Miller. Kullback-Leibler Distance for quantifying clutter and models, Opt. Eng. December 1999. 38(12):2134-2146.
[51] James A. Retches, C.P. Walters, Rudolf G.Buser, B.D. Guenther, Aided and Automatic Target Recognition Based Upon Sensory Inputs From image Forming Systems[J], IEEE transactions on pattern analysis and machine intelligence. September 1997. Vol.19(9): 1004-1019.
[52] Lance M. Kaplan, Romain Murenze, Edward Asika, Kameswara Namuduri[C], Effect of Signal-to-Clutter Ratio on Template-Based ATR, SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery V. April 1998. Vol.3370: 408-419.
[53] William R. Reynolds. Toward Quantifying Infrared Clutter. Proc.SPIE. 1990. Vol.1311: 232-240.
[54] David L. Wilson, Image-based contrast-to-clutter modeling of detection, Opt. Eng. September 2001. Vol.40, No.9: 1852-1857.
[55] John W. Hilgers, William P. Vockel, William R. Reynolds, Sensor and Detection Algorithm Based Clutter Metrics. Proc.SPIE. 1997. Vol.3062: 267-277.
[56] S. Richard F. Sims. Signal to clutter measurement and ATR performance. Proc.SPIE. 1998. Vol.3371: 398-403.
[57] Peng Zhang, Jing Peng, S. Richard F. Sims. New subspace methods for ATR. Proc.SPIE. 2005. Vol.5807: 349-358.
[58] Margaret A. Phillips. S. Richard F. Sims. A Signal to Clutter Measure for ATR Performance Comparison. Proc.SPIE. 1997. Vol.3069: 74-81.
[59] Scott K. Ralph, John Irvine, Magnus Snorrason, Mark R. Stevens, David Vanstone, An Image Metric-Based ATR Performance Prediction Testbed, IEEE AppliedImagery and Pattern Recognition Workshop, 2005. Proceedings. 34th, April 2005.
[60] Abhijit Mahalanobis, S. Richard F. Sims, Alan Van Nevel, Signal-to clutter measure for measuring automatic target recognition performance using complimentary eigenvalue distribution analysis, Opt. Eng. April 2003. Vol.42, No.4: 1144-1151.
[61] James W. Sherman, David N. Spector, et al, The Infrared and Electro Optical Systems Handbook, Vol.8, pp.357-359.
[62] Christian M. Birkemark, CAMEVA. a methodology for estimation of target detectability. Opt. Eng. September 2001.Vol.40, No.9: 1835-1843.
[63] S. R. Rotman, G. Tidhar, Clutter Metrics for Target Detection System, IEEE Transactions on aerospace and electronic systems. 1994.Vol.30, No.1: 81-91.
[64] Thomas J. Meitzler, Labib Arafeh, Grant R. Gerhart, Fuzzy logic approach for computing the probability of target detection in cluttered scenes, Opt. Eng. December 1996. Vol.35, No.12: 3623-3636.
[65] J.Michael Cathcart, Theodore J.Doll, David E. Schmieder, Target Detection in Urban Clutter, IEEE Transactions on systems, man, and cybernetics. September/October 1989. Vol.19, No.5: 1242-1250.
[66] S. Richard F. Sims, Putting ATR performance on an equal basis– The measurement of knowledge base distortion and relevant clutter, Proc.SPIE. 2000.Vol.4050:55-60.
[67] K. Fukunaga, W. L. Koontz, Representation of random processes using the finite Karhunen-Loeve expansion, IEEE Trans. Info. 1970. Vol.16, No.1: 85-10.
[68]常洪花,张建奇,基于机器视觉系统的红外背景杂波量化技术,红外技术, Sep.2005. Vol.27, No.5: 403-407.
[69] R. Richter, J.S. Davis and M.J. Duggin,“A radiometric sensor performance model including atmospheric and infrared clutter effects,”SPIE proc. 1997. Vol.3062: 322-329
[70] Kenneth A. Melendez, Michael Koligman, and Robert W. Fries,“AIRMS infrared performance model,”Proc.SPIE. 1997.Vol.3063: 62-75.
[71] J. D’Agostino, W. Lawson and D. Wilson,“Concepts for search and detection model improvements,”SPIE Proc. 1997. Vol. 3063: 14-22.
[72] J. D. McGlynn and D. J. Sofianos,“Parametric Model-Based Characterization of IR Clutter,”SPIE Proc. 1998. Vol. 2470: 236-244.
[73] R.D. Brewer, J.V. Richard, John D. McGlynn, and D. J. Sofianos,“An IR seeker/sensor dynamic performance prediction model,”SPIE proc. 1995. Vol. 2470: 89-97.
[74] I. Taig, A. Vander, N. S. Kopeika, and S.R. Rotman,“Effect of image restoration ontarget acquisition,”SPIE proc. 2001. Vpl.4370: 25-35.
[75] M. Diani, N.Acito, and G. Corsini,“New background clutter subspace basis selection criterion for clutter cancellation in infra-red naval surveillance systems,”proceedings of SPIE. 2003. Vol.4885: 452-459.
[76]艾斯卡尔,那斯尔江.一种基于局部加权非参数回归估计的杂波抑制技术.激光与红外. 2005. Vol. 35 (4): 294-296.
[77]艾斯卡尔·艾木都拉.基于加权系数自适应选择的背景杂波抑制技术研究.新疆大学学报(自然科学版). 2008. Vol.25 (2): 137-141.
[78]吕雁,苏新主.一种基于背景预测的红外杂波抑制新方法.系统工程与电子技术. 2007. Vol.29 (8): 1272-1274.
[79]李智杰,高新波,姬红兵.基于Curvelet变换的红外图像背景杂波抑制算法.红外技术. 2004. Vol.26 (5): 1-4.
[80]鲜海滢,李晓峰,李在铭.基于背景杂波自适应预测的微小目标检测.光电工程. 2008. Vol.35 (5): 12-16.
[81]聂洪山,赵新昱2,陈晓飞,赵军锁.红外小目标时域检测算法研究.红外技术. 2007. Vol.29 (7): 398-403.
[82]武斌,姬红兵,李鹏.基于三阶累积量的红外弱小运动目标检测新方法.红外与毫米波学报. 2006. Vol.25 (5): 364-367.
[83]杨磊,杨杰,凌建国,刘尔琦.基于背景复杂程度估计的红外图像预处理.系统工程与电子技术. 2006. Vol.28 (8): 1151-1153.
[84]徐华,邵晓鹏,刘德连.基于时域廓线的云杂波背景下红外弱小目标检测.科学技术与工程. 2008. Vol.8 (3): 638-642.
[85]李吉成,沈振康,鲁新平,李秋华.基于背景杂波自适应预测的红外弱小目标检测.激光与红外. 2004. Vol.34 (6): 478-480.
[86]王晓蕊,张建奇.基于机器视觉的红外成像系统探测器性能评估.红外与毫米波学报. 2003. Vol.22 (4): 273-276.
[87]胡方明,王晓蕊,张建奇,林虹.运动效应和背景杂波对红外成像系统性能评估的修正.红外与毫米波学报. 2004. Vol.23 (1): 59-63.
[88] Honghua Chang and Jianqi Zhang. New Metrics for Clutter Affecting Human Target Acquisition. IEEE Transactions on Aerospace & Electronic System. Jan. 2006. Vol. 42. No. 1: 361-368.
[89]常洪花,张建奇,李勇.背景杂波对经典人眼目标获取性能模型的修正.红外与毫米波学报. 2005. Vol.24 (6): 450-454.
[90] Honghua Chang, Jianqi Zhang, Xiaorui Wang, Yong Li. Background clutter and detection-based staring IR seeker performance evaluation. SPIE. Proc. 2004.Vol.5640:381-390,
[91] Honghua Chang, Jianqi Zhang. Evaluation of human detection performance using TSSIM clutter metrics. Optical Engineering. 2006. Vol.45 (9): 096404.
[92]常洪花,张建奇.基于人眼视觉系统的红外背景杂波量化技术.红外技术. 2005. Vol.26 (5): 13-17.
[93] Honghua Chang, Jianqi Zhang. Detection Probability and Detection Time Using Clutter Metrics. Infrared Physics & Technology. Infrared Physics & Technology. 2007. Vol.51 (2): 83-90.
[94]邵晓鹏.红外纹理生成方法研究.西安电子科技大学博士论文. 2005.
[95]张健.红外场景实时生成技术研究.西安电子科技大学硕士学位论文. 2005.
[96] Renold G. Driggers, Paul Cox, Timothy Edwards. Introduction to Infrared and Electro-Optical Systems. pp.179-181. 1999.
[97] Christelle Garnier, Rene Coloree, Jihed Flifla. General framework for infrared sensor modeling. SPIE. Proc. 1998. Vol.3377:59-70.
[98]杨宜禾,岳敏,周维真.红外系统(第二版).北京:国防工业出版社. pp.199-223. 1995.
[99] H. V. Holst. Miscellaneous Modulation Transfer Function (MTF) Effects Relating to Sampling Summing. SPIE. Proc. 1991. Vol.1488:165-176.
[100]李华,曲卫东,党冬妮等.基于模糊模式识别红外成像系统TOD参数测量.激光与红外. 2003. Vol.33 (5): 392-395.
[101] Norman B.Nill. A visual model weighted cosine transform for image compression and quality assessment. IEEE transactions on communications, 1985, com-33(6): 551~557
[102]杨军,王成,王云峰.一种基于HVS模型的图像质量信噪比评价方法[J].河北科技大学学报. 2002. Vol.23 (4): 80~85.
[103]魏崇奎,成礼智.一种基于掩盖效应的感知域图像质量评价方法[J].中国图象图形学报. 2004. Vol.9 (2): 195~200.
[104] J. L. Mannos, D. J. Sakrison. The effect of a visual fidelity criterion on the encoding of images. IEEE Transactions on Information Theory. 1974. Vol.20 (2): 525-536.
[105] K. N. Ngan, K. S. Leong, and H. Singh. Cosine transform coding incorporating human visual system model. SPIE fiber’86, Cambridge, MA USA. 1986. 165-171.
[106] Lloyd J M. Thermal imaging system.尹白云等译.北京:国防工业出版社. 1981.
[107] Ratches J. A. Night vision laboratory static performance model for thermal viewing systems. U.S. Army Electronics Command. Report No1 ECOM 7043. 1975
[108] Barten P. G. The SQRI method: a new method for the evaluation of visible resolution on a display. Proc. Soc. Inf. Dispel. 1987. Vol.28: 253-262.
[109] Jacobson R. An evaluation of image quality metrics. The journal of photographic science. 1995. Vol.43: 7-16.
[110] Bowonkoon Chitprasert, K.R.Rao. Human Visual Weighted Progressive Image Transmission[J]. IEEE Transactions on communications. 1990. Vol.38(7): 1040~1044.
[111] R. L. De Valois, K. K. De Valois. Spatial Vision. Oxford University Press, 1988.
[112] J. G. Daugman. Spatial Visual Channels in the Fourier Plane. Vision Research. 1984. Vol. 24: 891-910.
[113] Christian J. van den Branden Lambrecht. A working spatio-temporal model of the human visual system for image restoration and quality assessment applications. IEEE Proc. ICASSP’96, 1996, 4: 2291~2294
[114] J. G. Daugman. Two-Dimensional Spectral Analysis of the of Cortical Receptive Field Profiles. Vision Research. 1980. Vol. 20: 847-856.
[115] Gerald C.Holst. Electro-Optical Imaging System Performance. America: JCD Publishing and SPIE Optical Engineering Press. Second Edition: Preface to the second edition. 2000.
[116] Richard Harvey, Stephen King, Richard Aldridge, and J. Andrew Bangham. Comparing image resamplers via a model of the human vision system. http://scooby.sys.uea.ac.uk/papers/bmvc97_2.ps.gz
[117] Giorgio Fumera, Fabio Roli, A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier System, IEEE transactions on pattern analysis and machine intelligence. 2005. Vol.27 (6): 942-956.
[118] Leonard Neiberg, David P. Casasent, Feature Space Trajectory (FST) Classifier Neural Network Proc. SPIE. 1994.Vol.2353: 276-292.
[119] Ulf Grenander, Michael I. Miller, Anuj Srivastava, Hilbert-Schmidt Lower Bounds for Estimators on Matrix Lie Groups for ATR, IEEE transactions on pattern analysis and machine intelligence. August 1998.Vol.20. No.8: 790-802.
[120] David Casasent, Rajesh Shenoy, Feature space trajectory for distorted-object classification and pose estimation in synthetic aperture radar, Opt. Eng. October 1997. Vol.36. No.10: 2719-2782.
[121] Phil Clare, Mark Bernhardt, William Oxford et al.. A new approach to anomaly detection in hyperspectral images. SPIE Proc. 2003. Vol.5093: 17-28.
[122] D. Manolakis, D. Marden, J. Kerekes et al.. On the statistics of hyperspectral imaging data. SPIE Proc. 2001. Vol.4381: 308-316.
[123] B. A. Bastami, H. Amindavar. A new method for detectability of signals in K-distributed clutter [J]. IEEE Transactions on Signal Processing and Its Applications. 2003. Vol.1 (1-4): 345-348.
[124] M. Bernhardt, W. J. Oxford, P. E. Clare et al.. Statistical detection algorithms in fat-tailed hyperspectral background clutter. SPIE Proc. 2004. Vol.5573: 215-225.
[125] G. Rellier, X. Descombes, F. Falzon et al.. Texture feature analysis using a Gauss2Markov model in hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing. 2004. Vol.42 (7): 1543-1551.
[126] T. N. Tran, R. Wehrens, D. H. Hoekman et al.. Initialization of Markov random field clustering of large remote sensing images. IEEE Transactions on Geoscience and Remote Sensing. 2005. Vol.43 (8): 1912-1919.
[127] A. H. S. Solberg, T. Taxt, A. K. Jain. A Markov random field model for classification of multisource satellite imagery. IEEE Transactions on Geoscience and Remote Sensing. 1996. Vol.34 (1): 100-113.
[128] S. Geman, D. Geman. Stochastic relaxation, Gibbs distribution, and bayesian restoration of images [J]. IEEE Transactions Pattern Analysis Machine. Intelligence. 1984. Vol.6 (6): 721-741.
[129] Keinosuke Fukunaga, Warren L. G. Koontz. Application of the Karhunen-Loeve Expansion to Feature Selection and Ordering. IEEE Transactions on Computers. 1970. Vol. C-19 (4): 311-318.
[130] Steven M. Kay著,罗鹏飞等译.统计信号处理基础——估值与检测理论.北京.电子工业出版社. 2003. 24-25.
[2] A Marshall, K.St.J.Mruphy. Modeling and simulation of IR scenes and sensors in the UK-current and future capabilities. SPIE Proceedings on Infrared Technology and Applications XXII. 1996. Vol.2744: 591-598.
[3] Christer Wigren. Model of image generation in optronic(electro-optical) sensor systems(IGOSS). SPIE Proceedings on Infrared Imaging Systems: Design, Analysis, Modeling and Testing IX. 1998. Vol.3377: 89-94.
[4] Christelle Garnier, Rene Colloree, Jihed Flifla et al. Physically-based infrared sensor efforts modeling. SPIE Proceedings on Infrared Imaging Systems: Design, Analysis, Modeling and Testing X. 1999. Vol.3701: 81-94.
[5] Douglas C.McKee. Sensor modeling using programmable interface for Hardware-in-the-loop IR/EO testing. SPIE Proceedings on Technologies for Synthetic Environments: Hardware-in-the-Loop Testing IV. 1999. Vol.3697: 470-481.
[6]刘鑫.三维地面场景红外仿真方法研究.西安电子科技大学硕士论文. 2003.
[7]王学伟,熊璋,沈同圣,周晓东.红外成像制导导弹虚拟战场仿真.系统仿真学报. 2002. Vol.14(7): 922-924.
[8]钱惟贤,陈钱,顾国华,张强.凝视型红外热像仿真技术研究.红外. 2006. Vol.27(6):1-5.
[9]陈澄,石静,费锦东.凝视红外成像制导系统数学建模与仿真技术.红外与激光工程. 2007. Vol.36(1):19-22.
[10]彭星光,高晓光.红外成像制导反舰导弹模块化数字仿真.火力与指挥控制. 2008. Vel.33(3):28-34.
[11] Gerald C.Holst. Testing and Evaluation of Infrared Imaging Systems. America: JCD Publishing and SPIE Optical Engineering Press. Second Edition: Preface to the second edition. 2000.
[12] Army Electronic Command. Night vision laboratory performance model for thermal viewing system. AD/A-011212. 1975.
[13] L.Scott, J.D’Agostino, NVEOD FLIR92 Thermal Imaging Systems Performance Model, SPIE Proceedings. 1992. 1689: 194-203.
[14] W.Wittenstein, Thermal Range Model TRM3. SPIE Proceedings. 1998. Vol.3436:413-424,
[15] Vollmerhausen R and Driggers R G,NVTherm: next generation night vision model,Proceeding of IRIS Passive Sensors. 1999. Vol.11:121-134.
[16] Tana Maurer, Ronald G.Driggers, Richard Vollmerhausen, and Melvin Freidman. 2002 NVTherm Improvements.Proc.SPIE. 2002. Vol.4719:15-23.
[17] P.Bijl and J.M.Valeton, TOD, A new method to characterize electro-optical system performance, Proc. SPIE. 1998. Vol.3377: 182-193.
[18] P.Bijl and J.M.Valeton. TOD, the alternative to MRTD and MRC [J], Opt. Eng. 1998. 37(7) :1976-1983.
[19] Joseph Kostrzewa, John Long, John Graff, et al. TOD versus MRT when evaluating thermal imagers that exhibit dynamic performance. Proc. SPIE. 2003. Vol.5076: 220-232.
[20] Piet Bijl, J.Mathieu Valeton, Arie N.de Jong, TOD predicts target acquisition performance and scanning thermal imager, Proc. SPIE. 2000. Vol.4030: 96-103.
[21] Dirk-Jan J.de Lange, J.Mathieu Valeton, Piet Bijl, Automatic characterization electro-optical sensor with image-processing, using the Triangle Orientation Discrimination (TOD) method, SPIE Proceedings. 2000 Vol..4030:104-111.
[22] Bijl P and Valeton J M,“Guidelines for accurate TOD measurement”, SPIE. Proceedings. 1999. Vol.3701:14-25.
[23] Bijl, Piet; Valeton, J. Mathieu. Validation of the new triangle orientation discrimination method and ACQUIRE model predictions using observerperformance date for ship targets. Opt.Eng. 1998. 37(07), 1984-1994.
[24] McHugh, Stephen W.; Irwin, Alan; Valeton, J. M.; Bijl, Piet. TOD test method for characterizing electro-optical system performance. Proc. SPIE. 2001. 4372:39-45.
[25] Piet Bijl, J.Mathieu Valeton and Maarten A.Hogervorst. A critical evaluation of test patterns for EO system performance characterization. Proc.SPIE.2001. 4372:27-38.
[26] Piet Bijl, Maarten A.Hogervorst. A test method for multi-band imaging system. Proc.SPIE. 2003. Vol.5076:208-219.
[27] Piet Bijl, Maarten A. Hogervorst, J. M. Valeton. TOD, NVTherm, and TRM3 model calculations: a comparison. Proc. SPIE. 2002. Vol.4719:51-62.
[28] Ronald G. Driggers, Van Hodgkin, Rich Vollmerhausen, and Patrick O’Shea. Minimum Resolvable Temperature Difference Measurements on Undersampled Imagers. Proc. SPIE. 2003. Vol.5076: 179-189.
[29] Ronald G. Driggers, Rich Vollmerhausen, Wolfgan Wittenstein, Piet Bijl and J.Mathieu Valeton. Infrared Imager Models for Undersampled Imaging Systems.Proc. SPIE. 2000. Vol.5076: 179-189.
[30] Maarten A.Hogervorst, Piet Bijl and J.Mathieu Valeton. Capturing the sampling effects: a TOD sensor performance model [J]. Proc. SPIE. 2001. Vol.4372: 62-73.
[31]金伟其,高稚允等.一代通用组件热像仪静态性能计算机软件包SPTIS,光学技术(增刊). 1995. pp49-53.
[32]周燕,金伟其等.基于人眼视觉的光电成像系统性能评价方法研究.兵工学报. 2002. Vol.23 (4): 504-507.
[33]金伟其,高稚允等.光电成像系统与人眼视觉的匹配问题.红外技术. 2000. Vol.22 (5): 40-44.
[34]李旭东,艾克聪,张安锋.热成像系统MRTD数学模型的研究.应用光学. 2004. Vol.25 (6): 38-42.
[35]陈立学.采样成像系统的传递性能分析.光学学报. 1995. Vol.15(11): 1547-1551.
[36]陈自宽,翟宏琛,母国光.光电成像系统中的光学传递函数.光学技术. 1988,No.1: 57-60.
[37]童默颍,常本康,钱芸生,刘磊.红外焦平面阵列调制传递函数研究.红外与毫米波学报. 2003. Vol.22 (5): 365-367.
[38]王晓蕊,张建奇,常红花,刘鑫.一种表征红外成像系统性能的新方法.红外技术. 2003. Vol.25(2): 24-28.
[39] Xiao-rui Wang, Jian-qi Zhang, Zhuo-xiang Feng, Fang-ming Hu. TOD performance theoretical model for staring thermal imagers. International Journal of Infrared and Millimeter Waves. 2004. Vol.25(11):1645-1656.
[40] Xiao-rui Wang, Jian-qi Zhang, Zhuo-xiang Feng , Hong-hua Chang. Equation-based triangle orientation discrimination sensor performance model based on sampling effects. Applied Optics. 2005. Vol.44(4): 498-505.
[41]王晓蕊,张建奇,冯卓祥,常红花.凝视红外成像系统的三角方向鉴别与之曲线预测.光学学报. 2005. Vol.25 (8): 1036-1041.
[42]王晓蕊.红外焦平面成像系统建模及TOD性能表征方法研究.西安电子科技大学博士论文. 2005.
[43] Jianqi Zhang, xiao-rui Wang, Fang-ming Hu. TOD performance theoretical model for scanning infrared imagers. Infrared physics & technology. 2006. Vol.48 (1): 32-38.
[44]王晓蕊,张建奇,冯卓祥,常红花.三角形方向鉴别阈值性能理论模型研究.红外与毫米波学报. 2006. Vol.25 (2): 118-122.
[45] Z.Avital, Y.Hadar, G. Reiter, G. Tidhar, and S.R Rotmam,“The influence of clutteron human target acquisition,”SPIE Proc. 1992. Vol.1971: 436-446.
[46] K.Gillian, Groves, and M. Kim, et al.,“Quantification of clutter in EO tracking systems,”SPIE Proceedings. 1994. Vol.2221: 287-295.
[47]常洪花.光电图像背景杂波的定量表征及其对成像系统目标获取性能的影响.西安电子科技大学博士论文. 2006.
[48] D. E. Schmieder, M. R. Weathersby,”Detection performance in clutter with variable resolution,”J. IEEE Trans on Aerospace and Electronic Systems. 1983. 19(4): 622-630.
[49] David Klick, Philip Blumunau, Joseph Theriault, Detection of targets in infrared clutter. Proc.SPIE. 2001. Vol.4370: 120-133.
[50] Aaron D.Lanterman, Joseph A, O’Sullivan, Michael l. Miller. Kullback-Leibler Distance for quantifying clutter and models, Opt. Eng. December 1999. 38(12):2134-2146.
[51] James A. Retches, C.P. Walters, Rudolf G.Buser, B.D. Guenther, Aided and Automatic Target Recognition Based Upon Sensory Inputs From image Forming Systems[J], IEEE transactions on pattern analysis and machine intelligence. September 1997. Vol.19(9): 1004-1019.
[52] Lance M. Kaplan, Romain Murenze, Edward Asika, Kameswara Namuduri[C], Effect of Signal-to-Clutter Ratio on Template-Based ATR, SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery V. April 1998. Vol.3370: 408-419.
[53] William R. Reynolds. Toward Quantifying Infrared Clutter. Proc.SPIE. 1990. Vol.1311: 232-240.
[54] David L. Wilson, Image-based contrast-to-clutter modeling of detection, Opt. Eng. September 2001. Vol.40, No.9: 1852-1857.
[55] John W. Hilgers, William P. Vockel, William R. Reynolds, Sensor and Detection Algorithm Based Clutter Metrics. Proc.SPIE. 1997. Vol.3062: 267-277.
[56] S. Richard F. Sims. Signal to clutter measurement and ATR performance. Proc.SPIE. 1998. Vol.3371: 398-403.
[57] Peng Zhang, Jing Peng, S. Richard F. Sims. New subspace methods for ATR. Proc.SPIE. 2005. Vol.5807: 349-358.
[58] Margaret A. Phillips. S. Richard F. Sims. A Signal to Clutter Measure for ATR Performance Comparison. Proc.SPIE. 1997. Vol.3069: 74-81.
[59] Scott K. Ralph, John Irvine, Magnus Snorrason, Mark R. Stevens, David Vanstone, An Image Metric-Based ATR Performance Prediction Testbed, IEEE AppliedImagery and Pattern Recognition Workshop, 2005. Proceedings. 34th, April 2005.
[60] Abhijit Mahalanobis, S. Richard F. Sims, Alan Van Nevel, Signal-to clutter measure for measuring automatic target recognition performance using complimentary eigenvalue distribution analysis, Opt. Eng. April 2003. Vol.42, No.4: 1144-1151.
[61] James W. Sherman, David N. Spector, et al, The Infrared and Electro Optical Systems Handbook, Vol.8, pp.357-359.
[62] Christian M. Birkemark, CAMEVA. a methodology for estimation of target detectability. Opt. Eng. September 2001.Vol.40, No.9: 1835-1843.
[63] S. R. Rotman, G. Tidhar, Clutter Metrics for Target Detection System, IEEE Transactions on aerospace and electronic systems. 1994.Vol.30, No.1: 81-91.
[64] Thomas J. Meitzler, Labib Arafeh, Grant R. Gerhart, Fuzzy logic approach for computing the probability of target detection in cluttered scenes, Opt. Eng. December 1996. Vol.35, No.12: 3623-3636.
[65] J.Michael Cathcart, Theodore J.Doll, David E. Schmieder, Target Detection in Urban Clutter, IEEE Transactions on systems, man, and cybernetics. September/October 1989. Vol.19, No.5: 1242-1250.
[66] S. Richard F. Sims, Putting ATR performance on an equal basis– The measurement of knowledge base distortion and relevant clutter, Proc.SPIE. 2000.Vol.4050:55-60.
[67] K. Fukunaga, W. L. Koontz, Representation of random processes using the finite Karhunen-Loeve expansion, IEEE Trans. Info. 1970. Vol.16, No.1: 85-10.
[68]常洪花,张建奇,基于机器视觉系统的红外背景杂波量化技术,红外技术, Sep.2005. Vol.27, No.5: 403-407.
[69] R. Richter, J.S. Davis and M.J. Duggin,“A radiometric sensor performance model including atmospheric and infrared clutter effects,”SPIE proc. 1997. Vol.3062: 322-329
[70] Kenneth A. Melendez, Michael Koligman, and Robert W. Fries,“AIRMS infrared performance model,”Proc.SPIE. 1997.Vol.3063: 62-75.
[71] J. D’Agostino, W. Lawson and D. Wilson,“Concepts for search and detection model improvements,”SPIE Proc. 1997. Vol. 3063: 14-22.
[72] J. D. McGlynn and D. J. Sofianos,“Parametric Model-Based Characterization of IR Clutter,”SPIE Proc. 1998. Vol. 2470: 236-244.
[73] R.D. Brewer, J.V. Richard, John D. McGlynn, and D. J. Sofianos,“An IR seeker/sensor dynamic performance prediction model,”SPIE proc. 1995. Vol. 2470: 89-97.
[74] I. Taig, A. Vander, N. S. Kopeika, and S.R. Rotman,“Effect of image restoration ontarget acquisition,”SPIE proc. 2001. Vpl.4370: 25-35.
[75] M. Diani, N.Acito, and G. Corsini,“New background clutter subspace basis selection criterion for clutter cancellation in infra-red naval surveillance systems,”proceedings of SPIE. 2003. Vol.4885: 452-459.
[76]艾斯卡尔,那斯尔江.一种基于局部加权非参数回归估计的杂波抑制技术.激光与红外. 2005. Vol. 35 (4): 294-296.
[77]艾斯卡尔·艾木都拉.基于加权系数自适应选择的背景杂波抑制技术研究.新疆大学学报(自然科学版). 2008. Vol.25 (2): 137-141.
[78]吕雁,苏新主.一种基于背景预测的红外杂波抑制新方法.系统工程与电子技术. 2007. Vol.29 (8): 1272-1274.
[79]李智杰,高新波,姬红兵.基于Curvelet变换的红外图像背景杂波抑制算法.红外技术. 2004. Vol.26 (5): 1-4.
[80]鲜海滢,李晓峰,李在铭.基于背景杂波自适应预测的微小目标检测.光电工程. 2008. Vol.35 (5): 12-16.
[81]聂洪山,赵新昱2,陈晓飞,赵军锁.红外小目标时域检测算法研究.红外技术. 2007. Vol.29 (7): 398-403.
[82]武斌,姬红兵,李鹏.基于三阶累积量的红外弱小运动目标检测新方法.红外与毫米波学报. 2006. Vol.25 (5): 364-367.
[83]杨磊,杨杰,凌建国,刘尔琦.基于背景复杂程度估计的红外图像预处理.系统工程与电子技术. 2006. Vol.28 (8): 1151-1153.
[84]徐华,邵晓鹏,刘德连.基于时域廓线的云杂波背景下红外弱小目标检测.科学技术与工程. 2008. Vol.8 (3): 638-642.
[85]李吉成,沈振康,鲁新平,李秋华.基于背景杂波自适应预测的红外弱小目标检测.激光与红外. 2004. Vol.34 (6): 478-480.
[86]王晓蕊,张建奇.基于机器视觉的红外成像系统探测器性能评估.红外与毫米波学报. 2003. Vol.22 (4): 273-276.
[87]胡方明,王晓蕊,张建奇,林虹.运动效应和背景杂波对红外成像系统性能评估的修正.红外与毫米波学报. 2004. Vol.23 (1): 59-63.
[88] Honghua Chang and Jianqi Zhang. New Metrics for Clutter Affecting Human Target Acquisition. IEEE Transactions on Aerospace & Electronic System. Jan. 2006. Vol. 42. No. 1: 361-368.
[89]常洪花,张建奇,李勇.背景杂波对经典人眼目标获取性能模型的修正.红外与毫米波学报. 2005. Vol.24 (6): 450-454.
[90] Honghua Chang, Jianqi Zhang, Xiaorui Wang, Yong Li. Background clutter and detection-based staring IR seeker performance evaluation. SPIE. Proc. 2004.Vol.5640:381-390,
[91] Honghua Chang, Jianqi Zhang. Evaluation of human detection performance using TSSIM clutter metrics. Optical Engineering. 2006. Vol.45 (9): 096404.
[92]常洪花,张建奇.基于人眼视觉系统的红外背景杂波量化技术.红外技术. 2005. Vol.26 (5): 13-17.
[93] Honghua Chang, Jianqi Zhang. Detection Probability and Detection Time Using Clutter Metrics. Infrared Physics & Technology. Infrared Physics & Technology. 2007. Vol.51 (2): 83-90.
[94]邵晓鹏.红外纹理生成方法研究.西安电子科技大学博士论文. 2005.
[95]张健.红外场景实时生成技术研究.西安电子科技大学硕士学位论文. 2005.
[96] Renold G. Driggers, Paul Cox, Timothy Edwards. Introduction to Infrared and Electro-Optical Systems. pp.179-181. 1999.
[97] Christelle Garnier, Rene Coloree, Jihed Flifla. General framework for infrared sensor modeling. SPIE. Proc. 1998. Vol.3377:59-70.
[98]杨宜禾,岳敏,周维真.红外系统(第二版).北京:国防工业出版社. pp.199-223. 1995.
[99] H. V. Holst. Miscellaneous Modulation Transfer Function (MTF) Effects Relating to Sampling Summing. SPIE. Proc. 1991. Vol.1488:165-176.
[100]李华,曲卫东,党冬妮等.基于模糊模式识别红外成像系统TOD参数测量.激光与红外. 2003. Vol.33 (5): 392-395.
[101] Norman B.Nill. A visual model weighted cosine transform for image compression and quality assessment. IEEE transactions on communications, 1985, com-33(6): 551~557
[102]杨军,王成,王云峰.一种基于HVS模型的图像质量信噪比评价方法[J].河北科技大学学报. 2002. Vol.23 (4): 80~85.
[103]魏崇奎,成礼智.一种基于掩盖效应的感知域图像质量评价方法[J].中国图象图形学报. 2004. Vol.9 (2): 195~200.
[104] J. L. Mannos, D. J. Sakrison. The effect of a visual fidelity criterion on the encoding of images. IEEE Transactions on Information Theory. 1974. Vol.20 (2): 525-536.
[105] K. N. Ngan, K. S. Leong, and H. Singh. Cosine transform coding incorporating human visual system model. SPIE fiber’86, Cambridge, MA USA. 1986. 165-171.
[106] Lloyd J M. Thermal imaging system.尹白云等译.北京:国防工业出版社. 1981.
[107] Ratches J. A. Night vision laboratory static performance model for thermal viewing systems. U.S. Army Electronics Command. Report No1 ECOM 7043. 1975
[108] Barten P. G. The SQRI method: a new method for the evaluation of visible resolution on a display. Proc. Soc. Inf. Dispel. 1987. Vol.28: 253-262.
[109] Jacobson R. An evaluation of image quality metrics. The journal of photographic science. 1995. Vol.43: 7-16.
[110] Bowonkoon Chitprasert, K.R.Rao. Human Visual Weighted Progressive Image Transmission[J]. IEEE Transactions on communications. 1990. Vol.38(7): 1040~1044.
[111] R. L. De Valois, K. K. De Valois. Spatial Vision. Oxford University Press, 1988.
[112] J. G. Daugman. Spatial Visual Channels in the Fourier Plane. Vision Research. 1984. Vol. 24: 891-910.
[113] Christian J. van den Branden Lambrecht. A working spatio-temporal model of the human visual system for image restoration and quality assessment applications. IEEE Proc. ICASSP’96, 1996, 4: 2291~2294
[114] J. G. Daugman. Two-Dimensional Spectral Analysis of the of Cortical Receptive Field Profiles. Vision Research. 1980. Vol. 20: 847-856.
[115] Gerald C.Holst. Electro-Optical Imaging System Performance. America: JCD Publishing and SPIE Optical Engineering Press. Second Edition: Preface to the second edition. 2000.
[116] Richard Harvey, Stephen King, Richard Aldridge, and J. Andrew Bangham. Comparing image resamplers via a model of the human vision system. http://scooby.sys.uea.ac.uk/papers/bmvc97_2.ps.gz
[117] Giorgio Fumera, Fabio Roli, A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier System, IEEE transactions on pattern analysis and machine intelligence. 2005. Vol.27 (6): 942-956.
[118] Leonard Neiberg, David P. Casasent, Feature Space Trajectory (FST) Classifier Neural Network Proc. SPIE. 1994.Vol.2353: 276-292.
[119] Ulf Grenander, Michael I. Miller, Anuj Srivastava, Hilbert-Schmidt Lower Bounds for Estimators on Matrix Lie Groups for ATR, IEEE transactions on pattern analysis and machine intelligence. August 1998.Vol.20. No.8: 790-802.
[120] David Casasent, Rajesh Shenoy, Feature space trajectory for distorted-object classification and pose estimation in synthetic aperture radar, Opt. Eng. October 1997. Vol.36. No.10: 2719-2782.
[121] Phil Clare, Mark Bernhardt, William Oxford et al.. A new approach to anomaly detection in hyperspectral images. SPIE Proc. 2003. Vol.5093: 17-28.
[122] D. Manolakis, D. Marden, J. Kerekes et al.. On the statistics of hyperspectral imaging data. SPIE Proc. 2001. Vol.4381: 308-316.
[123] B. A. Bastami, H. Amindavar. A new method for detectability of signals in K-distributed clutter [J]. IEEE Transactions on Signal Processing and Its Applications. 2003. Vol.1 (1-4): 345-348.
[124] M. Bernhardt, W. J. Oxford, P. E. Clare et al.. Statistical detection algorithms in fat-tailed hyperspectral background clutter. SPIE Proc. 2004. Vol.5573: 215-225.
[125] G. Rellier, X. Descombes, F. Falzon et al.. Texture feature analysis using a Gauss2Markov model in hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing. 2004. Vol.42 (7): 1543-1551.
[126] T. N. Tran, R. Wehrens, D. H. Hoekman et al.. Initialization of Markov random field clustering of large remote sensing images. IEEE Transactions on Geoscience and Remote Sensing. 2005. Vol.43 (8): 1912-1919.
[127] A. H. S. Solberg, T. Taxt, A. K. Jain. A Markov random field model for classification of multisource satellite imagery. IEEE Transactions on Geoscience and Remote Sensing. 1996. Vol.34 (1): 100-113.
[128] S. Geman, D. Geman. Stochastic relaxation, Gibbs distribution, and bayesian restoration of images [J]. IEEE Transactions Pattern Analysis Machine. Intelligence. 1984. Vol.6 (6): 721-741.
[129] Keinosuke Fukunaga, Warren L. G. Koontz. Application of the Karhunen-Loeve Expansion to Feature Selection and Ordering. IEEE Transactions on Computers. 1970. Vol. C-19 (4): 311-318.
[130] Steven M. Kay著,罗鹏飞等译.统计信号处理基础——估值与检测理论.北京.电子工业出版社. 2003. 24-25.