SAR图像目标ROI自动获取技术研究
|
摘要
针对SAR图像解译应用的需求,为了从大幅SAR图像中快速有效地发现目标ROI,深入研究了SAR图像目标ROI自动获取技术。以发展实用化的SAR图像目标ROI自动获取技术为目的,在大量高分辨率SAR实测数据的支持下,采用理论分析和实验验证的研究方法,对SAR图像杂波统计建模、SAR图像自动目标检测、SAR图像自动目标鉴别等关键技术进行了系统深入的研究。
第一章总结了SAR图像目标ROI自动获取技术研究的背景、意义,简明扼要地评述了SAR图像目标ROI自动获取技术的研究进展,指出了现存的问题,概括了论文的主要工作及创新。
第二章研究了SAR图像杂波统计建模问题。首先综述了SAR图像统计建模的相关技术;在此基础上,结合实测数据库,深入分析了SAR图像的乘性噪声模型,得出了一些对杂波统计模型应用及进一步研究有意义的结论;运用实测SAR图像中表征不同地物类型的大量数据,深入分析了SAR图像杂波的统计特性。得出了在目前已有实用的统计模型中,G~0分布最适于描述SAR图像杂波统计特性,它对于均匀、一般不均匀和极不均匀杂波区域都能较精确建模的结论。
第三章以构建实用化的目标检测过程为目的,研究了SAR图像的自动目标检测问题。在全面系统地总结前人工作的基础上,利用SAR图像杂波统计建模的研究成果,重点研究了目标检测的CFAR技术。提出了一种基于自动筛选的智能CFAR目标检测算法。该算法避免了传统CFAR技术应用于目标检测时的局限性,能够智能判断目标检测所处的杂波环境,以G~0分布为背景杂波统计模型,建立了不同杂波环境下统一的目标CFAR检测,大大增强了目标检测的自动性;在理论分析智能CFAR算法计算量的基础上,提出了智能CFAR算法相应的快速算法,大大增加了算法的实用性。
第四章以构建实用化的目标鉴别过程为目的,研究了SAR图像的自动目标鉴别问题。在全面系统地总结前人工作的基础上,对鉴别的特征提取、特征选择、鉴别器的设计等展开了系列的研究。提出了一种目标鉴别的新方案,该方案包括目标鉴别的框架、模型以及算法;提出了基于特征选取鉴别和基于编队提取鉴别“序贯”连接相结合的目标鉴别框架;在基于特征选取进行目标鉴别的方法中,提出了目标鉴别的“松耦合”模型;提出了“松耦合”模型下目标鉴别的特征提取方法,包括已有特征的筛选和3个新的对比度特征的提出;改进了一种基于GA的特征选择方法,克服了已有方法对特征优劣评价不全面的问题;设计了加权二次距离鉴别器,提高了鉴别的性能;研究了基于目标编队知识进行进一步杂波虚警剔除的方法。
第五章系统地总结了全文的工作,并给出了进一步研究的建议。
The topic of this thesis is to deeply investigate the automatic techniques of acquiring target's ROI (Region-of-interests) from SAR images rapidly and effectively, which is among the demands of SAR image interpretation applications. Aiming at developing the practical techniques of automatically acquiring target's ROI, some key techniques such as statistical modeling of SAR clutter images, automatic target detection and discrimination from SAR images are systematically studied by theoretic analysis and experimental validation with lots of real high-resolution SAR data.
In chapter 1 the research background and significance of automatically acquiring target's ROI from SAR images are summarized. Then, the progress of this research is reviewed briefly and the currently existing problems are pointed out. Finally, the main work and the innovations of this thesis are summarized.
Chapter 2 deals with the problem of statistical modeling of clutter in SAR images. Firstly, the relevant techniques of statistical modeling of clutter in SAR images are reviewed in detail. Secondly, based on this review, the multiplicative noise model (product model) for SAR images is analyzed comprehensively to reach some significant conclusions for the application and development of statistical modeling of clutter in SAR images. The clutter is analyzed comprehensively with the real SAR data characterizing different terrain categories. A conclusion is reached that the G°distribution is most appropriate to describe the statistical property of clutter and is relatively accurate for statistically modeling the homogeneous, heterogeneous and extremely heterogeneous clutter regions.
Aiming at building the practical automatic target detection process, the problem of automatic target detection in SAR images is studied in chapter 3. Based on an extensive survey of the existing studies and the conclusion of chapter 2, we focus on the CFAR detection algorithms. An intelligent and fast CFAR algorithm based on automatic censoring is proposed for target detection in SAR images. This algorithm avoids the limitation of the conventional CFAR algorithms and can intelligently decide the clutter environment of detection. By introducing the G°distribution as the statistical model of clutter in this algorithm, the uniform CFAR detection is established under different clutter environments to enhance the automatic degree of target detection. Based on the theoretical analysis of the computation cost of the intelligent CFAR algorithm, the corresponding fast algorithm of the intelligent CFAR algorithm is also proposed to improve the practicality of target detection.
In chapter 4, in order to conduct the powerfully practical automatic target discrimination process, the problem of automatic target discrimination in SAR images is studied. Based on the extensive summarization of the existing studies, a series of studies including feature extraction and selection of target discrimination, the design of discriminator, and so on are done. A new scheme of target discrimination in SAR images, consisted of frames, models and algorithms, is proposed. Under such a scheme, a global frame, combining orderly the algorithm based on feature extraction and that based on knowledge, is then proposed. Moreover, in the method of target discrimination based on feature extraction, a "loose-coupling" model is given. The existing features are chosen and three new features about the contrast are given under the "loose-coupling". Meanwhile, an algorithm of feature selection based on Genetic Algorithm is also modified to solve the problem that the existing algorithm can not evaluate the goodness-of-features comprehensively. The weighted quadratic distance discriminator is designed to improve the performance of target discrimination. Finally, a method based on the knowledge of target groups to remove clutter false alarms is also given.
Chapter 5 concludes the research of this thesis. Some problems and interesting area for future research are pointed out.
第一章总结了SAR图像目标ROI自动获取技术研究的背景、意义,简明扼要地评述了SAR图像目标ROI自动获取技术的研究进展,指出了现存的问题,概括了论文的主要工作及创新。
第二章研究了SAR图像杂波统计建模问题。首先综述了SAR图像统计建模的相关技术;在此基础上,结合实测数据库,深入分析了SAR图像的乘性噪声模型,得出了一些对杂波统计模型应用及进一步研究有意义的结论;运用实测SAR图像中表征不同地物类型的大量数据,深入分析了SAR图像杂波的统计特性。得出了在目前已有实用的统计模型中,G~0分布最适于描述SAR图像杂波统计特性,它对于均匀、一般不均匀和极不均匀杂波区域都能较精确建模的结论。
第三章以构建实用化的目标检测过程为目的,研究了SAR图像的自动目标检测问题。在全面系统地总结前人工作的基础上,利用SAR图像杂波统计建模的研究成果,重点研究了目标检测的CFAR技术。提出了一种基于自动筛选的智能CFAR目标检测算法。该算法避免了传统CFAR技术应用于目标检测时的局限性,能够智能判断目标检测所处的杂波环境,以G~0分布为背景杂波统计模型,建立了不同杂波环境下统一的目标CFAR检测,大大增强了目标检测的自动性;在理论分析智能CFAR算法计算量的基础上,提出了智能CFAR算法相应的快速算法,大大增加了算法的实用性。
第四章以构建实用化的目标鉴别过程为目的,研究了SAR图像的自动目标鉴别问题。在全面系统地总结前人工作的基础上,对鉴别的特征提取、特征选择、鉴别器的设计等展开了系列的研究。提出了一种目标鉴别的新方案,该方案包括目标鉴别的框架、模型以及算法;提出了基于特征选取鉴别和基于编队提取鉴别“序贯”连接相结合的目标鉴别框架;在基于特征选取进行目标鉴别的方法中,提出了目标鉴别的“松耦合”模型;提出了“松耦合”模型下目标鉴别的特征提取方法,包括已有特征的筛选和3个新的对比度特征的提出;改进了一种基于GA的特征选择方法,克服了已有方法对特征优劣评价不全面的问题;设计了加权二次距离鉴别器,提高了鉴别的性能;研究了基于目标编队知识进行进一步杂波虚警剔除的方法。
第五章系统地总结了全文的工作,并给出了进一步研究的建议。
The topic of this thesis is to deeply investigate the automatic techniques of acquiring target's ROI (Region-of-interests) from SAR images rapidly and effectively, which is among the demands of SAR image interpretation applications. Aiming at developing the practical techniques of automatically acquiring target's ROI, some key techniques such as statistical modeling of SAR clutter images, automatic target detection and discrimination from SAR images are systematically studied by theoretic analysis and experimental validation with lots of real high-resolution SAR data.
In chapter 1 the research background and significance of automatically acquiring target's ROI from SAR images are summarized. Then, the progress of this research is reviewed briefly and the currently existing problems are pointed out. Finally, the main work and the innovations of this thesis are summarized.
Chapter 2 deals with the problem of statistical modeling of clutter in SAR images. Firstly, the relevant techniques of statistical modeling of clutter in SAR images are reviewed in detail. Secondly, based on this review, the multiplicative noise model (product model) for SAR images is analyzed comprehensively to reach some significant conclusions for the application and development of statistical modeling of clutter in SAR images. The clutter is analyzed comprehensively with the real SAR data characterizing different terrain categories. A conclusion is reached that the G°distribution is most appropriate to describe the statistical property of clutter and is relatively accurate for statistically modeling the homogeneous, heterogeneous and extremely heterogeneous clutter regions.
Aiming at building the practical automatic target detection process, the problem of automatic target detection in SAR images is studied in chapter 3. Based on an extensive survey of the existing studies and the conclusion of chapter 2, we focus on the CFAR detection algorithms. An intelligent and fast CFAR algorithm based on automatic censoring is proposed for target detection in SAR images. This algorithm avoids the limitation of the conventional CFAR algorithms and can intelligently decide the clutter environment of detection. By introducing the G°distribution as the statistical model of clutter in this algorithm, the uniform CFAR detection is established under different clutter environments to enhance the automatic degree of target detection. Based on the theoretical analysis of the computation cost of the intelligent CFAR algorithm, the corresponding fast algorithm of the intelligent CFAR algorithm is also proposed to improve the practicality of target detection.
In chapter 4, in order to conduct the powerfully practical automatic target discrimination process, the problem of automatic target discrimination in SAR images is studied. Based on the extensive summarization of the existing studies, a series of studies including feature extraction and selection of target discrimination, the design of discriminator, and so on are done. A new scheme of target discrimination in SAR images, consisted of frames, models and algorithms, is proposed. Under such a scheme, a global frame, combining orderly the algorithm based on feature extraction and that based on knowledge, is then proposed. Moreover, in the method of target discrimination based on feature extraction, a "loose-coupling" model is given. The existing features are chosen and three new features about the contrast are given under the "loose-coupling". Meanwhile, an algorithm of feature selection based on Genetic Algorithm is also modified to solve the problem that the existing algorithm can not evaluate the goodness-of-features comprehensively. The weighted quadratic distance discriminator is designed to improve the performance of target discrimination. Finally, a method based on the knowledge of target groups to remove clutter false alarms is also given.
Chapter 5 concludes the research of this thesis. Some problems and interesting area for future research are pointed out.
引文
[1]张澄波.综合孔径雷达原理、系统分析与应用.北京:科学出版社,1989.
[2]刘永坦.雷达成像技术.哈尔滨:哈尔滨工业大学出版社,1999.
[3]张直中.微波成像技术.北京:科学出版社.1990.
[4]朱述龙,张占睦.遥感图像获取与分析.北京:科学出版社,2000.
[5][美]M.I.Skolnik著,王军等译.雷达手册.北京:电子工业出版社,2003.
[6]魏钟铨等.合成孔径雷达卫星.国家863计划308主题空间信息获取与处理系统专著,北京:科学出版社,2001.
[7]舒宁.雷达遥感原理.武汉:武汉测绘科技大学出版社,2000.
[8]谢寿生,徐永进.微波遥感技术与应用.北京:电子工业出版社,1987.
[9]张直中.机载和星载合成孔径雷达导论.北京:电子工业出版社,2003.
[10][法]H.Maitre著,孙洪译.合成孔径雷达图像处理.北京:电子工业出版社,2003.
[11]保铮等.雷达成像技术.北京:电子工业出版社.2003.
[12]第二届中国合成孔径雷达会议论文集.南京,2005.
[13]L.M.Novak,G.J.Owirka,A.L.Weaver.Automatic Target Recognition using Enhanced Resolution SAR Data,IEEE Transactions on Aerospace and Electronic Systems 1999,35(1):157-175.
[14]L.Alberto,L.D.Carcloso.Computer Aided Recognition of Man-made Structures in Aerial Photograghs.Naval Postgraduate School.AD report.Dec.1999.
[15]Special Issue on Advances in Synthetic Aperture Radar.IEE Proceedings Radar,Sonar & Navigation,2003,150(3).
[16]D.E.Dudgeon,R.T.Lacoss.An Overview of Automatic Target Recognition,Lincoln Laboratory Journal,1993,6(1):3-10.
[17]D.E.Kreithen,et al.Discriminating Targets from Clutter.The Lincoln Laboratory Journal,1993,6(1):25-51.
[18]L.M.Novak,et al.Performance of a High-Resolution Polarimetric SAR Automatic Target Recognition System.The Lincoln Laboratory Journal,1993,6(1):11-24.
[19]L.M.Novak,et al.Effects of Polarization and Resolution on the Performance of a SAR Automatic Target Recognition System.The Lincoln Laboratory Journal,1995,8(1):49-67.
[20]C.J.Oliver,S.Quegan.Understanding Synthetic Aperture Radar Images.Artech House:Boston,1998.
[21]M.Greenspan,et al.Development and Evaluation of A Real Time SAR ATR System. IEEE, 1998,38-43.
[22] G.B. Goldstein. False Alarm Regulation in Log-normal and Weibull Clutter.IEEE Trans. on AES, 1973, 9(1):84-92.
[23] H. Rohling. Radar CFAR Thresholding in Clutter and Multiple Target Situations.IEEE Trans. on AES, 1983,19(3):608-621.
[24] L.M. Novak and M.C. Burl, Optimal Speckle Reduction in Polarimetric SAR Imagery, IEEE Trans. Aerosp. Electron. Syst.March,1990
[25] L.M. Novak and M.C. Burl, Optimal Speckle Reduction in POL-SAR Imagery and Its Effect on Target Detection,Proc. SPIE 1101,84(1989)
[26] R.D. Chaney, M.C. Burl, and L. M. Novak, On the Performance of Polarimetric Target Detection Algorithms, Proc. 1990 IEEE International Radar Conf.,Arlington, VA, 7-10, May 1990, p.520.
[27] M. C. Burl, et al. Texture Discrimination in Synthetic Aperture Radar Imagery.Twenty-Third Asilomar Conference on Signals, Systems and Computers, 1989,399-404.
[28] L.M. Novak, M.C. Burl, and W.W. Irving, Optimal Polarimetric Processing for Enhanced Target Detection, IEEE Trans. Aerosp. Electron. Syst. 29, 234(1993).
[29] L.M. Novak, M.C. Burl, R.D. Chaney and G.J. Owirka. Optimal Processing of Polarimetric Synthetic Aperture Radar Imagery, Lincoln Laboratory Journal, 3(2),summer, 1990.
[30] L. M. Novak, et al. Radar Target Identification Using Spatial Matched Filters.Pattern Recognition, 1994, 27(4): 607-617.
[31] G. J. Owirka, et al. A New SAR ATR Algorithm Suite. SPIE, 1994, 2230:336-343.
[32] L. M. Novak, M.B. Sechtin, and M.J. Cardullo. Studies of Target Detection Algorithms That Use Polarimetric Radar Data. AES, March 1989.
[33] W.W. Irving, G.J. Owirka, and L.M. Novak. Adaptive Processing of POL-SAR Imagery. Asilomar, Conference, November 1990.
[34] J.C. Henry The Lincoln Laboratory 35GHz Airborne Polarimetric SAR Imaging Radar System. IEEE National Telesystems Conference, Atlanta,GA(26-27,March,1991)
[35] L. M. Novak, Target detection studies using fully polarimetric data collected by the Lincoln Laboratory MMW SAR, Radar 92. International Conference, 1992,167170.
[36] L. M. Novak, et al. The Automatic Target-Recognition System in SAIP. The Lincoln Laboratory Journal, 1997, 10(2): 187-202.
[37] R.A. English, et al. Development of an ATR Workbench for SAR Imagery.Technical Report, DRDC, Ottawa, 2005.
[38]J.C.Oliver,et al.http://www.infosar.co.uk/misc/demo.html.
[39]P.Druyts,et al.SAHARA:Semi-Automatic Help for Region Analysis.Proceedings of the Joint Workshop of ISPRS Working Groups Ⅰ/1,Ⅰ/3 and ⅣA:Sensors and Mapping from Space.Germany Hannover,1997,267-274.
[40]W.Roller,et al.Detection and Recognition of Vehicles in High-Resolution SAR Imagery.SPIE,2001,4380:142-152.
[41]R.Dressier,et al.COTS SAR Processing Software.IEEE National Radar Conference,1996:136-141.
[42]M.E.Yobin,et al.Smuggling Interdiction Using An Adaptation of The AN/APG-76 Multimode Radar.IEEE AES Systems Magazine,1996,19-24.
[43]B.Bhanu,et al.Introduction to the Special Issue on Automatic Target Detection and Recognition,IEEE Transactions on Image Processing,1997,6(1):1-6.
[44]T.D.Ross,et al.SAR ATR-So What's the problem? -An MSTAR Perspective,SPIE,1999,3721,662-672.
[45]高贵,等.SAR图像统计建模研究综述.电子学报,在审.
[46]高贵,等.SAR图像目标检测研究进展与展望,电子学报,录用待刊.
[47]高贵,等.SAR图像目标鉴别研究之(一):进展与展望,电子学报,在审.
[48]张琦.基于统计模型的高分辨SAR图像车辆目标检测方法.国防科技大学硕士学位论文,2005.
[49]H.Arseoault,et al.Properties of Speckle Integrated with a Finite Aperture and Logarithmicaly Transformed.J.Opt.Soc.Am.,1976,66(11):1160-1163.
[50]K.D.Ward.Compound Representation of High Resolution Sea Clutter.Electron.Lett.,1981,7:561-565.
[51]A.C.Frery,et al.A Model for Extremely Heterogeneous Clutter,IEEE Trans.GRS,1997,35(3):648-659.
[52]A.C.Frery,et al.Altrmative distributions for the multiplicative model in SAR images.Int.Geosci.Remote Sensing Symp.,Florence,Italy,1995,1:169-171.
[53]J.S.Salazar Ⅱ.Detection Schemes for Synthetic Aperture Radar Imagery Based On a Beta Prime Statistical Model[Doctor's Dissertation].Florida University,1999.
[54]H.J.Muller.Modeling of extremely heterogeneous radar backscatter.IGARSS '97,1997,4:1603-1605.
[55]H.J.Muller,et al.G-statistics for scaled SAR data.IGARSS '99,1999,2:1297-1299.
[56]J.S.Salazar Ⅱ,et al.Statistical Modeling of Target and Cluter in Single-Look Non-Polorimetric SAR Imagery.International Conference Signal and Image Processing,1998,Las Vegas,USA.
[57]A.P.Blake,et al.High Resolution SAR Clutter Textural Analysis and Simulation.SPIE Vol.2584,101-108,1995.
[58]A.P.Blake,et al.High Resolution SAR Clutter Textural Analysis.IEE Colloquium on Recent Developments in Radar and Sonar Imaging Systems:What Next? 1995,UK,10/1-10/9.
[59]S.F.George.The detection of nonfluctuating targets in log-normal clutter,NRL Report 6796,1968.
[60]V.Anastassopoulos.High Resolution Radar Clutter Statistics.IEEE Trans.AES,1999,35(1):43-59.
[61]V.Anastassopoulos,et al.A Generalized Compound Model for Radar Clutter.IEEE 1994 National Radar Conference,Atlanta,1994,41-45.
[62]V.Anastassopoulos,et al.A New Clutter Model for SAR Images.International Conference on Applications of Photonic Technology,ICAPT'94,Toronto,1994,21-23.
[63]V.Anastassopoulos,et al.High Resolution Radar Clutter Classification.IEEE International Radar Conference,Washington,DC,1995,8-11.
[64]D.A.Shnidman.Generalized Radar Clutter Model.IEEE Trans.AES,35(3):857-865,1999.
[65]J.B.Billingsley,et al.Statistical Analyses of Measured Radar Ground Clutter Data.IEEE Trans.AES,1999,35(3):579-593.
[66]L.P.Fred.Texture and Speckle in High Resolution Synthetic Aperture Radar Clutter.IEEE Trans.Remote Sensing,1993,31(1):192-203.
[67]A.Lopes,et al.Statistical distribution and texture in multilook and complex SAR images.International Geoscience and Remote Sensing Syposium,Washington,DC,1990,3:2427-2430.
[68]B.Jorgensen.Statistical Properties of the Generalized Inverse Gaussian Distribution.New York:Springer-Verlag,1982,Lecture Notes in Statistics,9.
[69]G.Moser,et al.SAR amplitude probability density function estimation based on a generalized Gaussian scattering model.SPIE,2004,5573:307-318.
[70]G.Gao,et al.Fast Detecting and Locating Groups of Targets in High-Resolution SAR images.Pattern Recognition,2007,40(4):1378-1384.
[71]方学立.UWB-SAR图像中的目标检测与鉴别.国防科学技术大学博士学位论文,2005.
[72]E.E.Kuruoglu,et al.Modeling SAR Images With a Generalization of the Rayleigh Distribution.The 23th Annual Asilomar Conference on Signals,Systems and Computers,2000,1:224-228.
[73]E.E.Kuruoglu,et al.Modeling SAR Images With a Generalization of the Rayleigh Distribution.IEEE Trans.Image Processing,2004,13(4):527-533.
[74]E.Conte,et al.Characterization of Radar Clutter as A Spherically Invariant Random Process.Inst.Elect.Eng.,1987,134:191-197.
[75]C.J.Nikias,et al.Signal Processing with Alpha-Stable Distributions and Applications.New York:Wiley,1995.
[76]R.D.Pierce.RCS Characterization Using the Alpha-Stable distribution.IEEE 1996 Nat.Radar Conf.,1996,394-419.
[77]A.Banerjee,et al.Adaptive Target Detection in Foliage-Penetrating SAR Images Using Alpha-Stable Models.IEEE Trans.Image Processing,1999,8(12):1823-1831.
[78]E.E.Kuruoglu,et al.Approximation of Alpha-Stable Probability Densities Using Finete Mixtures of Gaussian.EUSIPCO'98 Eur.Signal Processing Conf.,Rhodes,Greece,1998,989-992.
[79]R.Kappor,et al.UWB Radar Detection of Targets in Foliage Using Alpha-Stabel Clutter Models.IEEE Trans.AES,1999,35(3):819-833.
[80]D.Blacknell.A Mixture Distribution Model for Correlated SAR Clutter,SPIE,1996,2958:38-49,
[81]D.Blacknell.Target Detection in Correlated SAR Clutter.IEE Proc-RSN,2000,147(1):9-16.
[82]C.J.Oliver.Correlated K-distribution Clutter Models.Optica Acta,1985,32(12):1515-1547.
[83]J.W.Goodman,Statistical properties of laser speckle patterns,Laser Speckle and Related Phenomena.J.C.Dinty,Ed.New York:Academic,1980.
[84]M.D.DeVore,et al.ATR Performance of a Rician Model for SAR Images.SPIE,2000,4050.
[85]E.Jakeman,et al.A model for non-Rayleigh sea echo.IEEE Trans.Antennas Propagat.,24(6),1976,806-814.
[86]J.K.Jao.Amplitude distribution of composite terrain radar clutter and the K distribution.IEEE Trans.Antennas Propagat.,Vol.AP -32,1984,1049-1061.
[87]J.S.Lee,et al.Intensity and phase statistics of multilook polarimetric and interferometric SAR imagery.IEEE Trans.Geosci.Remote Sensing.1994,32(6):1017-1027.
[88]D.Blacknell,et al.Estimators and Distributions in Single and Multi-look Polarimetric and Interferometric data.IGRASS'94,IEEE,1994:8-12.
[89]邹焕新.SAR图像舰船目标与航迹检测方法研究.国防科学技术大学博士学位论文.2003.
[90]Q.Jiang,et al.Ship Detection in RADARSAT SAR Imagery Using PNN-Model[C].Proceedings of ADRO Symposium'98,1998.
[91]C.M.Bishop.Neural networks for pattern recognition,2nd edition,Oxford University Press,1996.
[92]P.Mantero,et al.Partially supervised classification of remote sensing images using SVM-based probability density estimation,IEEE honorary workshop,2003(in press),2003.
[93]V.N.Vapnik,Statistical Learning Theory,John Wiley and Sons Inc.,1998.
[94]Y.Delignon,et al.Modelling non-Rayleigh speckle distribution in SAR images.IEEE Trans.GRS.,2002,40(6):1430-1435.
[95]K.Fukunaga.Introduction to Statistical Pattern Recognition.New York:Academic Press,1990.
[96]F.T.Ulaby,et al.Textural information in SAR images,IEEE Trans.GRS,1986,24:235-245.
[97]L.M.Kaplan.Analysis of Multiplicative Speckle Models for Template-Based SAR ATR.IEEE Trans.AES,2001,37(4):1424-1432.
[98]C.L.Martinez,et al.Polarimetric SAR Speckle Noise Model.IEEE Trans.GRS,2003,41(10):2232-2242.
[99]L.P.Fred.Texture and Speckle in High Resolution Synthetic Aperture Radar Clutter.IEEE Trans.GRS,1993,31(1):192-203.
[100]H.Xie,et al.Statistical Properties of Logarithmically Transformed Speckle,IEEE Trans.GRS,2002,40(3):721-727.
[101]高贵,等.SAR图像乘性噪声模型分析.信号处理,录用待刊.
[102]M.Thr,K.C.Chin,and J.W.Goodman,When is speckle noise multiplicative.Appl.Opt.,Vol.21,1982,1157-1159.
[103]齐治昌.数值分析及其应用.长沙:国防科技大学出版社.1996.
[104]粟塔山.最优化计算原理与算法程序设计.长沙:国防科技大学出版社,2002.
[105]刘福声,罗鹏飞.统计信号处理.长沙:国防科技大学出版社.1999.
[106]D.Borghys.Interpretation and Registration of High-Resolution Polarimetric SAR Images.ENSTE 031,Paris,2001.
[107]R.S.Raghavan.A Method for Estimating Parameters of K-distributed Clutter.IEEE Trans.AES,1991,27(2):238-246.
[108]M.D.DcVore,et al.Statistical Assessment of Model Fit for Synthetic Aperture Radar Data.SPIE 2001,4382:379-388.
[109]I.R.Joughin,et al.Maximum Likelihood Estimation of K Distribution Parameters for SAR Data,IEEE Trans.GRS,1993,31(5):989-999
[110]D.Blacknell,Comparison of Parameter Estimators for K-distribution,IEE Proc-Radar,Sonar Naving,1994,141(1):45-52.
[111]H.Akaike,Information theory and an extension of maximum likelihood principle,2nd International Symposium on Information Theory.1973,267-281.
[112]孙即祥.现代模式识别.长沙:国防科技大学出版社.2002.
[113]L.M.Novak,S.R.Hesse.On the Performance of Order-Statistics CFAR Detectors.IEEE Signals,Systems and Computers,Conference Record of the Twenty-Fifth Asilomar Conference on,1991:835-840.
[114]J.Li,E.G.Zelnio.Target Detection with Synthetic Aperture Radar.IEEE Trans.on AES.,1997,32(2):613-627.
[115]S.Kuttikkad,R.Chellappa.Non-Gaussian CFAR Techniques for Target Detection in High Resolution SAR Images.Proceedings.ICIP-94,1994,1:910-914.
[116]D.Blacknell.Target detection in correlated clutter.Radar 97,1997,449:214_218.
[117]D.Blacknell.Target Detection in Correlated SAR Clutter.IEE Proc.-Radar,Sonar Navig.,2000,147(1):9-16.
[118]T.Bucciarelli,et al.Optimum CFAR Detection Against Compound Gaussian Clutter with Partially Correlated Texture.IEE Proc.Radar Sonar and Navigation,1996,143(2):95-104.
[119]P.Lombardo,M.Sciotti,L.M.Kaplan.SAR Prescreening using Both Target and Shadow Information,IEEE Radar Conference,pp 147-152,2001.
[120]J.Ritcey.An Order-Statistics-Based CFAR for SAR Applications.Electrical Engineering Dept,University of Washington,Seattle,WA.,September 1990.
[121]R.J.Fogler,L.D.Hosterler.SAR Clutter Suppression Using Probability Density Skewness.IEEE Trans.on AES.,1994,30(2):622-626.
[122]M.W.Koch,M.M.Moya.Cueing Feature Discovery and One-Class Learning for Synthetic Aperture Radar Automatic Target Recognition.Neural Network,1995,8(7/8):1081-1102.
[123]L.S.Wilson.Two CFAR Algorithms for Interfering Targets and Non-Homogeneous Clutter.IEEE Trans.on AES.,1993,29(1):57-72.
[124]I.McConnell,C.J.Oliver.A Comparison of Segmentation Methods with Standard CFAR for Point Target Detection.Europto Conf.on SAR Image Analysis,Simulation and Modelling IV,SPIE Proc.,1998,3497:76-87.
[125]I.McConnell,C.J.Oliver.Segmentation-Based Target Detection in SAR.Europto Conf.on SAR Image Analysis,Modelling and Techniques Ⅱ,SPIE Proc.,Florence,Italy,1999,3869:45-54.
[126]M.E.Smith,P.K.Varshney.VI-CFAR:A Novel CFAR Algorithm Based on Data Variability.IEEE,National Radar Conference,1997,263:268.
[127]M.E.Smith,P.K.Varshney.Intelligent CFAR Processor Based on Data Variability.IEEE Trans.on AES.,2000,36(3):837-847.
[128]黄祥,等.基于区域分类的智能恒虚警SAR图像目标检测.武汉大学学报(理学版),2004,50(1):104-108。
[129]M.D.Bisceglie,C.Galdi.CFAR Detection of Extended Objects in High-Resolution SAR Images.IEEE Trans.on GRS.,2005,43(4):833-842.
[130]M.D.Bisceglie,C.Galdi.CFAR Detection of Extended Objects in High Resolution SAR Images.IGARSS '01,2001,2674_2676 vol.6
[131]Y.Wang,et al.Detection of Point Targets in High Resolution Synthetic Aperture Radar Images.ICASSP-94,1994,V/9_V12 vol.5.
[132]Q.H.Pham,et al.Multistage Algorithm for Detection of Targets in SAR Image Data.SPIE,1997,3070:66-74.
[133]F.Amoozegar,M.K.Sundareshan.A Robust Neural Network Scheme for Constant False Alarm Rate Processing for Target Detection in Clutter Environment.Proceedings of the American Control Conference,Baltimore,Maryland,1994,1727-1730.
[134]P.Weber,S.Haykin.Order Statistics CFAR for Two-Parameter Distributions with Variable Skewness.IEEE Trans.on AES.,Vol.21,1985.
[135]Y.Lu,et al.CFAR Detection for K-Distribution Multi-Looked SAR Image.IEEE International Radar Conf.284-288.
[136]Y.J.Yu,et al.CFAR Detector for High-Resolution Multi-Look SAR Image.Electronics Letters,1999,35(19):1668-1670.
[137]P.P.Gandhi,S.A.Kassam.Analysis of Some CFAR Processors in Nonhomogeneous Background.IEEE Trans.on AES.,1988,24(2):427-445.
[138]L.M.Novak,M.C.Burl,W.W.Irving and G.J.Owirka.Optimal Polarimetric Processing for Enhanced Target Detection,IEEE,1999,69-75.
[139]L.M.Novak,et al.An Efficient Multi-Target SAR ATR Algorithm.IEEE,1998,3-13.
[140]R.Xu,et al.Target Isolation for SAR Images.SPIE,2001,4382:191-194.
[141]G.R.Bentiz.High-Definition Vector Imaging for Synthetic Aperture Radar.IEEE,1998,1209-1209.
[142]D.Casasent,S.Ashizawa.SAR Detection and Recognition Filters.SPIE,1997,3070:125-134.
[143]J.X.Zhang,et al.SAR Image Enhancement for Small Target Detection.IEEE,2003,449-452.
[144]M.R.Allen,L.E.Hoff.Wide-Angle Wideband SAR Matched Filter Image Formation for Enhanced Detection Performance.SPIE,1994,2230:302-314.
[145]J.G.Nanis,et al.Adaptive Filters for Detection of Targets in Foliage.IEEE,1994,101-103.
[146]M.Scheffe,et al.Multicovariance Matched Filter for Target Detection and Background Recognition.SPIE,1994,2235:203-208.
[147]M.Allen,et al.Neural Processing of SAR Imagery for Enhanced Target Detection.SPIE,1995,2847:201-210.
[148]P.Wan,et al.New Method of Point Target Detection for SAR Image.SPIE,2000,4082:136-140.
[149]P.Thanyasrisung,et al.Reduced-rank Automatic Target Detection and Recognition.IEEE,2000,1530-1534.
[150]R.Shenoy,D.Casasent.Unconstrained Eigen-Filter Pairs for SAR Detection.SPIE,2002,4734:1-8.
[151]M.Kim.Focus of attention based on Gamma kernels for automatic target recognition.Doctor's Dissertion,Florida University,1996.43-70.
[152]J.C.Principe,et al.Target prescreening based on a quadratic Gamma discriminator.IEEE Trans.on AES.,1998,34(3):706-715.
[153]J.C.Principe,et al.The Gamma filter- A new class of adaptive ⅡR filters with restricted feedback,IEEE Transactions on Signal Processing,41,2(1993),649-656.
[154]J.C.Principe,et al.Target Discrimination in Synthetic Aperture Radar Using Artificial Neural Networks.IEEE Trans.on IP.,1998,7(8):1136-1149.
[155]M.Kim,et al.A New CFAR Stencil for Target Detections in Synthetic Aperture Radar(SAR) Imagery.SPIE,1996,2757:432-438.
[156]M.Kim,J.C.Principe.Artificial Neural Networks with Gamma Kernels for Automatic Target Detection.IEEE,1996,1594-1599.
[157]L.K.Yen,et al.A CFAR Intensity Pattern Detector For MMW SAR Images.IEEE,1998,974-978.
[158]J.S.Salowe.Very Fast SAR Detection.SPIE,1996,2755:58-69.
[159]张翠,等.一种高分辨率SAR图像快速目标检测算法.遥感学报,2005,9(1):45-49.
[160]高贵,等.SAR图像目标检测的两种CFAR算法对比研究.信号处理,录用待刊.
[161]W.Phillips,R.Chellappa.Target Detection in SAR:Parallel Algorithms,Context Extraction,and Region Adaptive Techniques.SPIE,1997,3070:76-87.
[162]M.Brizi,et al.Exploiting The Shadow Information to Increase The Target Detection Performance in SAR Images.Intern.Conf.on Radar Systems,Radar 99-Brest(France),1999.
[163]H.Leung,et al.Detection of Small Objects in Clutter Using a GA-RBF Neural Network.IEEE Trans.on AES.,2002,38(1):98-118.
[164]G.A.Lampropoulos,H.Leung.On CFAR Detection of Small Man Made Targets Using Chaotic and Statistical CFAR Detectors.SPIE,1999,3809:29-41.
[165]L.I.Perlovsky,W.H.Schoendorf.Model-Based Neural Network for Target Detection in SAR Images.IEEE Trans.on IP.,1997,6(1):203-216.
[166]L.K.Yen.Focus of Attention for Millimeter and Ultra Wideband Synthetic Aperture Radar Imagery.University of Florida,Doctor's Dissertation,1998.
[167]J.G.Landowski,R.S.Loe.Target Cluster Detection in Cluttered Synthetic Aperture Radar Imagery.SPIE,1989,1099:9-16.
[168]方学立,等.基于位置相关的SAR图像中分布式目标检测.电子与信息学报,2006,28(2):350-353。
[169]L.M.Kaplan.Improved SAR Target Detection via Extended Fractal Features.IEEE Trans.on AES.,2001,37(2):436-450.
[170]L.M.Kaplan,et al.SAR Target Detection by Fusion of CFAR,Variance,and Fractal Statistics.SPIE,1998,3374:167-178.
[171]L.M.Kaplan,R.Murenzi.Evaluation of CFAR and Texture Based Target Detection Statistics on SAR Imagery.IEEE,1998,2141-2144.
[172]L.M.Kaplan,et al.Extended Fractal Feature for First Stage SAR:Target Detection.1999,SPIE,3721:35-46.
[173]N.S.Subotic,et al.A Multiresolution Generalized Likelihood Ratio Detection Approach to Target Screening in Synthetic Aperture Radar Data.SPIE,1995,2487:226-234.
[174]M.Tello,et al.A Novel Algorithm for Ship Detection in SAR Imagery Based on The Wavelet Transform.IEEE GRS.Letters,2005,2(2):201-205.
[175]N.S.Subotic,et al.Multiresolution Target Detection in SAR Imagery.IEEE,1995,2157-2160.
[176]N.S.Subotic,et al.A Multiresolution Approach to Target Detection in Synthetic Aperture Radar Data.IEEE Radar Conference,1995,pp.122-126.
[177]D.Blacknell.Contextural Information in SAR Target Detection.IEE Proc.Radar,Sonar Navig.,2001,148(1):41-47.
[178]S.M.Marni,et al.Context-Aided False Alarm Reduction for SAR Automatic Target Recognition.IEEE Radar Conference,1997,pp.885-888.
[179]D.Greig,et al.Knowledge-Based Methods for Small Object Detection in SAR Images.SPIE,2003,4883:121-130.
[180]R.Paget,et al.(Automatic) Target Detection in Synthetic Aperture Radar Imagery Via Terrain Recognition.IEEE IGRASS,2001,pp.54-57.
[181]张翠,高分辨率SAR图像自动目标识别方法研究,国防科技大学博士学位论文,2003。
[182] R. Meth. Synthetic Aperture Radar Vehicle Detection via Subapture Coherence,http://www.asc2002.com/manuscripts/J/Jo-02.pdf. 2002.
[183] C.L. Wang, et al. Man-Made Target Detection in SAR Imagery. IEEE, 2005.
[184] W.W. Irving, S.M. Verbout. False Alarm Reduction Through Road Area Delimitation. IEEE International Radar Conf. 1995, 656-661.
[185] J.K. Jiao, et al. Coherent Spatial Filtering for SAR Detection of Stationary Targets. IEEE Trans. on AES., 1999,35(2):614-626.
[186] A. Filippidis, et al. Fusion of Intelligent Agents for the Detection of Aircraft in SAR Images. IEEE Trans. on PAMI., 2000, 22(4):378-384.
[187] L.M. Kaplan, et al. Detection of Broadside Targets during Image Formation using a Quad-Tree Approach. IEEE International Radar Conference, Arlington, Virginia,2000: 104-109.
[188] L.M. Kaplan, et al. Prescreening during Image Formation for Ultra-WideBand Radar. IEEE Trans. on AES, 2002, 38(1):74-88.
[189] L.M. Lu, et al. A Target Detection Method in Range-Doppler Domain from SAR Echo Data. IEEE International Conference on Pattern Recognition. Quebec,Canada, 2002, 1:91-94.
[190] J.D. Gorman, et al. Multiresolution Detection of Coherent Radar Targets. IEEE,1994, 446-450.
[191] M. McClure, et al. Multiresolution Signature -Based SAR Target Detection. SPIE,1998,3370:318-329.
[192] A.E. El-Rouby, et al. Application of Frequency Correlation Function to Radar Target Detection. IEEE Trans. on AES., 2003, 39(1): 125-139.
[193] J. Vihonen, et al. New Detection Algorithm for Poor Signal-to-Noise Conditions.IEEE, 2003, 345-349.
[194] D. Shoham. Phase-Stability Detection of Stationary Targets. IEEE, 1990,558-562.
[195] L. M. Novak, et al. An Efficient Multi-Target SAR ATR Algorithm. IEEE, 1998,3-13.
[196] D. E. Kreithen, et al. A Theoretical Analysis of a Ranking Discrimination Algorithm. IEEE, 1992: 431-435.
[197] S. M. Verbout, et al. New Image Features for Discriminating Targets from Clutter.SPIE, 1998,3395: 120-137.
[198] T. Cooke, et al. Comparison of Selected Features for Target Detection in Synthetic Aperture Radar Imagery. Digital Signal Processing, 2000, 10(4):286-296.
[199] O. Laskin, et al. Automatic Target Recognition in Synthetic Aperture Radar Images. 2000: 1-13.
[200] Z. H. Chen. Machine Learning Approach Towards Automatic Target Recognition.Harvard University, 2001.
[201] K. Ranney, et al. Comparison of Three Different Detectors Applied to Synthetic Aperture Radar Data. SPIE, 2002, 4727:91-99.
[202] B. Bhanu, et al. Genetic Algorithm Based Feature Selection For Target Detection in SAR Images. Image and Vision Computing, 2003,21(7): 591-608.
[203] Y. Q. Lin, et al. Evolutionary Feature Synthesis for Object Recognition. IEEE Trans. Systems, Man, And Cybernetics-Part C: Applications and Reviews, 2005,35(2): 156-171.
[204] S.D. Halversen, et al. New Approaches for Detecting Groups of Targets. the Twenty-Eighth Asilomar Conference on Signals, Systems and Computers, 1994:137-140.
[205] G. J. Owirka, et al. An Algorithm for Detecting Groups of Targets. IEEE International Radar Conference. 1995: 641-643.
[206] D. Blacknell, et al. SAR Image Understanding Using Contextual Information.SPIE, 2002,4543:73-84.
[207] R. Kothari, et al. On Lateral Connections in Feed-Forward Neural Networks.IEEE International Conf. Neural Networks, Washington, DC, USA, 1996, 3:1594-1599.
[208] R. Kothari, et al. Self-Regulation of Model Order in Feed-Forward Neural Networks. IEEE International Conf. Neural Networks, Houston, Texas, USA,1997: 1966-1971.
[209] P. Runkle, et al. Hidden Markov Models for Multi-Aspect Target Classification.IEEE Trans. Signal Processing, 1999,47(7): 2035-2040.
[210] P. Runkle, et al. Multi-Aspect Target Detection for SAR Imagery Using Hidden Markov Models. IEEE Trans. GRS, 2001, 39(1): 46-55.
[211] B. Krishnapuram, et al. Physics-Based Detection of Targets in SAR Imagery Using Support Vector Machines. IEEE Sensors Journal, 2003, 3(2): 147-157.
[212] W. W. Irving, et al. A Multiresolution Approach to Discrimination in SAR Imagery. IEEE Trans. AES, 1997, 33(4): 1157-1168.
[213] M. Cetin, et al. A Statistical Method For Discrimination of Natural Terrain Types from SAR Data. IEEE, 1998: 587-591.
[214] D. E. Kreithen, et al. A Discrimination Algorithm And The Effect of Resolution.IEEE National Radar Conference, 1993, 128-133.
[215] L. M. Novak, et al. Effect of Polarization and Resolution on SAR ATR. IEEE Transaction on AES. 1997,33(1): 102-115.
[216] M. Denny, et al. SAR Image Feature Extraction Using Shadow Information. SPIE,2003,4883:192-200.
[217] A. C. van den Broek, et al. Robustness of Features for Automatic Target Discrimination in High Resolution Polarimetric SAR Data. SPIE, 2003,5095:242-253.
[218] K. Fukunaga, et al. The Acquisition Probability for A Minimum Distance One-Class Classifier. IEEE Trans. AES, 1987,23(4):493-498.
[219] D. Blacknell. Adaptive Design and Characterisation of Features for SAR ATR.SPIE, 2001,4382: 252-263.
[220] D. Blacknell. Feature Analysis for SAR ATR. SPIE, 2000, 4173: 75-86.
[221] L. S. Liebovitch, et al. A Fast Algorithm to Determine Fractal Dimensions By Box Counting. Physics Letters A, 1989, 141(8,9): 386-390.
[222] Y. Q. Lin, et al. Object Detection via Feature Synthesis Using MDL-Based Genetic Programming. IEEE Trans. Systems, Man, And Cybernetics-Part B:Cybernetics, 2005, 35(3): 538-547.
[223] K. Krawiec, et al. Visual Learning by Revolutionary Feature Synthesis. IEEE Trans. Systems, Man, And Cybernetics-Part B: Cybernetics, 2005, 35(3):409-425.
[224] Y. Q. Lin. Feature synthesis and analysis by evolutionary computation for object detection and recognition. University of California, 2003.
[225] T. Cooke, et al. Comparison of Selected Features for Target Detection in Synthetic Aperture Radar Imagery. In Proceedings of 33th Asilomar Conference on Signals, System and Computers, Pacific Grove, CA, USA, 1999, 2: 859-863.
[226] L. A. Lisoba da Silva, Cardoso. Computer Aided Recognition of Man-Made Structures in Aerial [Master's Dissertation]. Naval Postgraduate School Monterey,1999.
[227] D. Borghys. Interpretation and Registration of High-Resolution Polarimetric SAR Images[D]. ENSTE 031, Paris, 2001.
[228] T. R. Damarla, et al. Automatic Target Detection Algorithm for Foliage Penetrating Ultra-Wideband SAR Data using Split Spectral Analysis. SPIE, 1999,3704:113-120.
[229] A. Radisavljevic, et al. Target Prescreening Based on a Quadratic Gamma Detector. SPIE, 1995.
[230] C. H. Fosgate, et al. Multi-Scale Segmentation and Anomaly Enhancement of SAR Imagery. IEEE Trans. IP, 1997,6(1):7-20.
[231] D. Howard, et al. Multi-Scale Models for Target Detection and Background Discrimination in Synthetic Aperture Radar Imagery. Digital Signal Processing,1999, 9(3): 149-161.
[232] J. Schroeder, et al. Multi-Scale Models for Target Detection in Complex Synthetic Aperture Radar. In Proceedings of Information, Decision and Control, Adelaide,Southern,Australia,1999:77-82.
[233]J.S.De Bonet,et al.Flexible Histograms:A Multiresolution Target Discrimination Model.http://www.ai.mit.edu/projects/lv.
[234]R.Kothari.A Function Approximation Framework for ROI Determination in SAR Images.http://www.mbvlab.wpafb.af.mil/paper.html,2004.
[235]R.Kothari,et al.On the Use of Model Order for Detection Potential Target Location in SAR Images.http://www.mbvlah.wpafb,af.mil/paper.html,2004.
[236]P.Runkle,et al.Multi-Aspect Target Detection in SAR Imagery Using Hidden Markov Models.SPIE,1999,3721:214-223.
[237]A.Robert,et al.MRF-Based Algorithms for Segmentation of SAR Images.the Proceeding of the 1998 International Conference on Image Processing.Paris:IEEE,1998.770-774.
[238]钟雪莲,等.SAR图像中目标的自动检测与辨别.中国图象图形学报,2005,10(6):688-697.
[2]刘永坦.雷达成像技术.哈尔滨:哈尔滨工业大学出版社,1999.
[3]张直中.微波成像技术.北京:科学出版社.1990.
[4]朱述龙,张占睦.遥感图像获取与分析.北京:科学出版社,2000.
[5][美]M.I.Skolnik著,王军等译.雷达手册.北京:电子工业出版社,2003.
[6]魏钟铨等.合成孔径雷达卫星.国家863计划308主题空间信息获取与处理系统专著,北京:科学出版社,2001.
[7]舒宁.雷达遥感原理.武汉:武汉测绘科技大学出版社,2000.
[8]谢寿生,徐永进.微波遥感技术与应用.北京:电子工业出版社,1987.
[9]张直中.机载和星载合成孔径雷达导论.北京:电子工业出版社,2003.
[10][法]H.Maitre著,孙洪译.合成孔径雷达图像处理.北京:电子工业出版社,2003.
[11]保铮等.雷达成像技术.北京:电子工业出版社.2003.
[12]第二届中国合成孔径雷达会议论文集.南京,2005.
[13]L.M.Novak,G.J.Owirka,A.L.Weaver.Automatic Target Recognition using Enhanced Resolution SAR Data,IEEE Transactions on Aerospace and Electronic Systems 1999,35(1):157-175.
[14]L.Alberto,L.D.Carcloso.Computer Aided Recognition of Man-made Structures in Aerial Photograghs.Naval Postgraduate School.AD report.Dec.1999.
[15]Special Issue on Advances in Synthetic Aperture Radar.IEE Proceedings Radar,Sonar & Navigation,2003,150(3).
[16]D.E.Dudgeon,R.T.Lacoss.An Overview of Automatic Target Recognition,Lincoln Laboratory Journal,1993,6(1):3-10.
[17]D.E.Kreithen,et al.Discriminating Targets from Clutter.The Lincoln Laboratory Journal,1993,6(1):25-51.
[18]L.M.Novak,et al.Performance of a High-Resolution Polarimetric SAR Automatic Target Recognition System.The Lincoln Laboratory Journal,1993,6(1):11-24.
[19]L.M.Novak,et al.Effects of Polarization and Resolution on the Performance of a SAR Automatic Target Recognition System.The Lincoln Laboratory Journal,1995,8(1):49-67.
[20]C.J.Oliver,S.Quegan.Understanding Synthetic Aperture Radar Images.Artech House:Boston,1998.
[21]M.Greenspan,et al.Development and Evaluation of A Real Time SAR ATR System. IEEE, 1998,38-43.
[22] G.B. Goldstein. False Alarm Regulation in Log-normal and Weibull Clutter.IEEE Trans. on AES, 1973, 9(1):84-92.
[23] H. Rohling. Radar CFAR Thresholding in Clutter and Multiple Target Situations.IEEE Trans. on AES, 1983,19(3):608-621.
[24] L.M. Novak and M.C. Burl, Optimal Speckle Reduction in Polarimetric SAR Imagery, IEEE Trans. Aerosp. Electron. Syst.March,1990
[25] L.M. Novak and M.C. Burl, Optimal Speckle Reduction in POL-SAR Imagery and Its Effect on Target Detection,Proc. SPIE 1101,84(1989)
[26] R.D. Chaney, M.C. Burl, and L. M. Novak, On the Performance of Polarimetric Target Detection Algorithms, Proc. 1990 IEEE International Radar Conf.,Arlington, VA, 7-10, May 1990, p.520.
[27] M. C. Burl, et al. Texture Discrimination in Synthetic Aperture Radar Imagery.Twenty-Third Asilomar Conference on Signals, Systems and Computers, 1989,399-404.
[28] L.M. Novak, M.C. Burl, and W.W. Irving, Optimal Polarimetric Processing for Enhanced Target Detection, IEEE Trans. Aerosp. Electron. Syst. 29, 234(1993).
[29] L.M. Novak, M.C. Burl, R.D. Chaney and G.J. Owirka. Optimal Processing of Polarimetric Synthetic Aperture Radar Imagery, Lincoln Laboratory Journal, 3(2),summer, 1990.
[30] L. M. Novak, et al. Radar Target Identification Using Spatial Matched Filters.Pattern Recognition, 1994, 27(4): 607-617.
[31] G. J. Owirka, et al. A New SAR ATR Algorithm Suite. SPIE, 1994, 2230:336-343.
[32] L. M. Novak, M.B. Sechtin, and M.J. Cardullo. Studies of Target Detection Algorithms That Use Polarimetric Radar Data. AES, March 1989.
[33] W.W. Irving, G.J. Owirka, and L.M. Novak. Adaptive Processing of POL-SAR Imagery. Asilomar, Conference, November 1990.
[34] J.C. Henry The Lincoln Laboratory 35GHz Airborne Polarimetric SAR Imaging Radar System. IEEE National Telesystems Conference, Atlanta,GA(26-27,March,1991)
[35] L. M. Novak, Target detection studies using fully polarimetric data collected by the Lincoln Laboratory MMW SAR, Radar 92. International Conference, 1992,167170.
[36] L. M. Novak, et al. The Automatic Target-Recognition System in SAIP. The Lincoln Laboratory Journal, 1997, 10(2): 187-202.
[37] R.A. English, et al. Development of an ATR Workbench for SAR Imagery.Technical Report, DRDC, Ottawa, 2005.
[38]J.C.Oliver,et al.http://www.infosar.co.uk/misc/demo.html.
[39]P.Druyts,et al.SAHARA:Semi-Automatic Help for Region Analysis.Proceedings of the Joint Workshop of ISPRS Working Groups Ⅰ/1,Ⅰ/3 and ⅣA:Sensors and Mapping from Space.Germany Hannover,1997,267-274.
[40]W.Roller,et al.Detection and Recognition of Vehicles in High-Resolution SAR Imagery.SPIE,2001,4380:142-152.
[41]R.Dressier,et al.COTS SAR Processing Software.IEEE National Radar Conference,1996:136-141.
[42]M.E.Yobin,et al.Smuggling Interdiction Using An Adaptation of The AN/APG-76 Multimode Radar.IEEE AES Systems Magazine,1996,19-24.
[43]B.Bhanu,et al.Introduction to the Special Issue on Automatic Target Detection and Recognition,IEEE Transactions on Image Processing,1997,6(1):1-6.
[44]T.D.Ross,et al.SAR ATR-So What's the problem? -An MSTAR Perspective,SPIE,1999,3721,662-672.
[45]高贵,等.SAR图像统计建模研究综述.电子学报,在审.
[46]高贵,等.SAR图像目标检测研究进展与展望,电子学报,录用待刊.
[47]高贵,等.SAR图像目标鉴别研究之(一):进展与展望,电子学报,在审.
[48]张琦.基于统计模型的高分辨SAR图像车辆目标检测方法.国防科技大学硕士学位论文,2005.
[49]H.Arseoault,et al.Properties of Speckle Integrated with a Finite Aperture and Logarithmicaly Transformed.J.Opt.Soc.Am.,1976,66(11):1160-1163.
[50]K.D.Ward.Compound Representation of High Resolution Sea Clutter.Electron.Lett.,1981,7:561-565.
[51]A.C.Frery,et al.A Model for Extremely Heterogeneous Clutter,IEEE Trans.GRS,1997,35(3):648-659.
[52]A.C.Frery,et al.Altrmative distributions for the multiplicative model in SAR images.Int.Geosci.Remote Sensing Symp.,Florence,Italy,1995,1:169-171.
[53]J.S.Salazar Ⅱ.Detection Schemes for Synthetic Aperture Radar Imagery Based On a Beta Prime Statistical Model[Doctor's Dissertation].Florida University,1999.
[54]H.J.Muller.Modeling of extremely heterogeneous radar backscatter.IGARSS '97,1997,4:1603-1605.
[55]H.J.Muller,et al.G-statistics for scaled SAR data.IGARSS '99,1999,2:1297-1299.
[56]J.S.Salazar Ⅱ,et al.Statistical Modeling of Target and Cluter in Single-Look Non-Polorimetric SAR Imagery.International Conference Signal and Image Processing,1998,Las Vegas,USA.
[57]A.P.Blake,et al.High Resolution SAR Clutter Textural Analysis and Simulation.SPIE Vol.2584,101-108,1995.
[58]A.P.Blake,et al.High Resolution SAR Clutter Textural Analysis.IEE Colloquium on Recent Developments in Radar and Sonar Imaging Systems:What Next? 1995,UK,10/1-10/9.
[59]S.F.George.The detection of nonfluctuating targets in log-normal clutter,NRL Report 6796,1968.
[60]V.Anastassopoulos.High Resolution Radar Clutter Statistics.IEEE Trans.AES,1999,35(1):43-59.
[61]V.Anastassopoulos,et al.A Generalized Compound Model for Radar Clutter.IEEE 1994 National Radar Conference,Atlanta,1994,41-45.
[62]V.Anastassopoulos,et al.A New Clutter Model for SAR Images.International Conference on Applications of Photonic Technology,ICAPT'94,Toronto,1994,21-23.
[63]V.Anastassopoulos,et al.High Resolution Radar Clutter Classification.IEEE International Radar Conference,Washington,DC,1995,8-11.
[64]D.A.Shnidman.Generalized Radar Clutter Model.IEEE Trans.AES,35(3):857-865,1999.
[65]J.B.Billingsley,et al.Statistical Analyses of Measured Radar Ground Clutter Data.IEEE Trans.AES,1999,35(3):579-593.
[66]L.P.Fred.Texture and Speckle in High Resolution Synthetic Aperture Radar Clutter.IEEE Trans.Remote Sensing,1993,31(1):192-203.
[67]A.Lopes,et al.Statistical distribution and texture in multilook and complex SAR images.International Geoscience and Remote Sensing Syposium,Washington,DC,1990,3:2427-2430.
[68]B.Jorgensen.Statistical Properties of the Generalized Inverse Gaussian Distribution.New York:Springer-Verlag,1982,Lecture Notes in Statistics,9.
[69]G.Moser,et al.SAR amplitude probability density function estimation based on a generalized Gaussian scattering model.SPIE,2004,5573:307-318.
[70]G.Gao,et al.Fast Detecting and Locating Groups of Targets in High-Resolution SAR images.Pattern Recognition,2007,40(4):1378-1384.
[71]方学立.UWB-SAR图像中的目标检测与鉴别.国防科学技术大学博士学位论文,2005.
[72]E.E.Kuruoglu,et al.Modeling SAR Images With a Generalization of the Rayleigh Distribution.The 23th Annual Asilomar Conference on Signals,Systems and Computers,2000,1:224-228.
[73]E.E.Kuruoglu,et al.Modeling SAR Images With a Generalization of the Rayleigh Distribution.IEEE Trans.Image Processing,2004,13(4):527-533.
[74]E.Conte,et al.Characterization of Radar Clutter as A Spherically Invariant Random Process.Inst.Elect.Eng.,1987,134:191-197.
[75]C.J.Nikias,et al.Signal Processing with Alpha-Stable Distributions and Applications.New York:Wiley,1995.
[76]R.D.Pierce.RCS Characterization Using the Alpha-Stable distribution.IEEE 1996 Nat.Radar Conf.,1996,394-419.
[77]A.Banerjee,et al.Adaptive Target Detection in Foliage-Penetrating SAR Images Using Alpha-Stable Models.IEEE Trans.Image Processing,1999,8(12):1823-1831.
[78]E.E.Kuruoglu,et al.Approximation of Alpha-Stable Probability Densities Using Finete Mixtures of Gaussian.EUSIPCO'98 Eur.Signal Processing Conf.,Rhodes,Greece,1998,989-992.
[79]R.Kappor,et al.UWB Radar Detection of Targets in Foliage Using Alpha-Stabel Clutter Models.IEEE Trans.AES,1999,35(3):819-833.
[80]D.Blacknell.A Mixture Distribution Model for Correlated SAR Clutter,SPIE,1996,2958:38-49,
[81]D.Blacknell.Target Detection in Correlated SAR Clutter.IEE Proc-RSN,2000,147(1):9-16.
[82]C.J.Oliver.Correlated K-distribution Clutter Models.Optica Acta,1985,32(12):1515-1547.
[83]J.W.Goodman,Statistical properties of laser speckle patterns,Laser Speckle and Related Phenomena.J.C.Dinty,Ed.New York:Academic,1980.
[84]M.D.DeVore,et al.ATR Performance of a Rician Model for SAR Images.SPIE,2000,4050.
[85]E.Jakeman,et al.A model for non-Rayleigh sea echo.IEEE Trans.Antennas Propagat.,24(6),1976,806-814.
[86]J.K.Jao.Amplitude distribution of composite terrain radar clutter and the K distribution.IEEE Trans.Antennas Propagat.,Vol.AP -32,1984,1049-1061.
[87]J.S.Lee,et al.Intensity and phase statistics of multilook polarimetric and interferometric SAR imagery.IEEE Trans.Geosci.Remote Sensing.1994,32(6):1017-1027.
[88]D.Blacknell,et al.Estimators and Distributions in Single and Multi-look Polarimetric and Interferometric data.IGRASS'94,IEEE,1994:8-12.
[89]邹焕新.SAR图像舰船目标与航迹检测方法研究.国防科学技术大学博士学位论文.2003.
[90]Q.Jiang,et al.Ship Detection in RADARSAT SAR Imagery Using PNN-Model[C].Proceedings of ADRO Symposium'98,1998.
[91]C.M.Bishop.Neural networks for pattern recognition,2nd edition,Oxford University Press,1996.
[92]P.Mantero,et al.Partially supervised classification of remote sensing images using SVM-based probability density estimation,IEEE honorary workshop,2003(in press),2003.
[93]V.N.Vapnik,Statistical Learning Theory,John Wiley and Sons Inc.,1998.
[94]Y.Delignon,et al.Modelling non-Rayleigh speckle distribution in SAR images.IEEE Trans.GRS.,2002,40(6):1430-1435.
[95]K.Fukunaga.Introduction to Statistical Pattern Recognition.New York:Academic Press,1990.
[96]F.T.Ulaby,et al.Textural information in SAR images,IEEE Trans.GRS,1986,24:235-245.
[97]L.M.Kaplan.Analysis of Multiplicative Speckle Models for Template-Based SAR ATR.IEEE Trans.AES,2001,37(4):1424-1432.
[98]C.L.Martinez,et al.Polarimetric SAR Speckle Noise Model.IEEE Trans.GRS,2003,41(10):2232-2242.
[99]L.P.Fred.Texture and Speckle in High Resolution Synthetic Aperture Radar Clutter.IEEE Trans.GRS,1993,31(1):192-203.
[100]H.Xie,et al.Statistical Properties of Logarithmically Transformed Speckle,IEEE Trans.GRS,2002,40(3):721-727.
[101]高贵,等.SAR图像乘性噪声模型分析.信号处理,录用待刊.
[102]M.Thr,K.C.Chin,and J.W.Goodman,When is speckle noise multiplicative.Appl.Opt.,Vol.21,1982,1157-1159.
[103]齐治昌.数值分析及其应用.长沙:国防科技大学出版社.1996.
[104]粟塔山.最优化计算原理与算法程序设计.长沙:国防科技大学出版社,2002.
[105]刘福声,罗鹏飞.统计信号处理.长沙:国防科技大学出版社.1999.
[106]D.Borghys.Interpretation and Registration of High-Resolution Polarimetric SAR Images.ENSTE 031,Paris,2001.
[107]R.S.Raghavan.A Method for Estimating Parameters of K-distributed Clutter.IEEE Trans.AES,1991,27(2):238-246.
[108]M.D.DcVore,et al.Statistical Assessment of Model Fit for Synthetic Aperture Radar Data.SPIE 2001,4382:379-388.
[109]I.R.Joughin,et al.Maximum Likelihood Estimation of K Distribution Parameters for SAR Data,IEEE Trans.GRS,1993,31(5):989-999
[110]D.Blacknell,Comparison of Parameter Estimators for K-distribution,IEE Proc-Radar,Sonar Naving,1994,141(1):45-52.
[111]H.Akaike,Information theory and an extension of maximum likelihood principle,2nd International Symposium on Information Theory.1973,267-281.
[112]孙即祥.现代模式识别.长沙:国防科技大学出版社.2002.
[113]L.M.Novak,S.R.Hesse.On the Performance of Order-Statistics CFAR Detectors.IEEE Signals,Systems and Computers,Conference Record of the Twenty-Fifth Asilomar Conference on,1991:835-840.
[114]J.Li,E.G.Zelnio.Target Detection with Synthetic Aperture Radar.IEEE Trans.on AES.,1997,32(2):613-627.
[115]S.Kuttikkad,R.Chellappa.Non-Gaussian CFAR Techniques for Target Detection in High Resolution SAR Images.Proceedings.ICIP-94,1994,1:910-914.
[116]D.Blacknell.Target detection in correlated clutter.Radar 97,1997,449:214_218.
[117]D.Blacknell.Target Detection in Correlated SAR Clutter.IEE Proc.-Radar,Sonar Navig.,2000,147(1):9-16.
[118]T.Bucciarelli,et al.Optimum CFAR Detection Against Compound Gaussian Clutter with Partially Correlated Texture.IEE Proc.Radar Sonar and Navigation,1996,143(2):95-104.
[119]P.Lombardo,M.Sciotti,L.M.Kaplan.SAR Prescreening using Both Target and Shadow Information,IEEE Radar Conference,pp 147-152,2001.
[120]J.Ritcey.An Order-Statistics-Based CFAR for SAR Applications.Electrical Engineering Dept,University of Washington,Seattle,WA.,September 1990.
[121]R.J.Fogler,L.D.Hosterler.SAR Clutter Suppression Using Probability Density Skewness.IEEE Trans.on AES.,1994,30(2):622-626.
[122]M.W.Koch,M.M.Moya.Cueing Feature Discovery and One-Class Learning for Synthetic Aperture Radar Automatic Target Recognition.Neural Network,1995,8(7/8):1081-1102.
[123]L.S.Wilson.Two CFAR Algorithms for Interfering Targets and Non-Homogeneous Clutter.IEEE Trans.on AES.,1993,29(1):57-72.
[124]I.McConnell,C.J.Oliver.A Comparison of Segmentation Methods with Standard CFAR for Point Target Detection.Europto Conf.on SAR Image Analysis,Simulation and Modelling IV,SPIE Proc.,1998,3497:76-87.
[125]I.McConnell,C.J.Oliver.Segmentation-Based Target Detection in SAR.Europto Conf.on SAR Image Analysis,Modelling and Techniques Ⅱ,SPIE Proc.,Florence,Italy,1999,3869:45-54.
[126]M.E.Smith,P.K.Varshney.VI-CFAR:A Novel CFAR Algorithm Based on Data Variability.IEEE,National Radar Conference,1997,263:268.
[127]M.E.Smith,P.K.Varshney.Intelligent CFAR Processor Based on Data Variability.IEEE Trans.on AES.,2000,36(3):837-847.
[128]黄祥,等.基于区域分类的智能恒虚警SAR图像目标检测.武汉大学学报(理学版),2004,50(1):104-108。
[129]M.D.Bisceglie,C.Galdi.CFAR Detection of Extended Objects in High-Resolution SAR Images.IEEE Trans.on GRS.,2005,43(4):833-842.
[130]M.D.Bisceglie,C.Galdi.CFAR Detection of Extended Objects in High Resolution SAR Images.IGARSS '01,2001,2674_2676 vol.6
[131]Y.Wang,et al.Detection of Point Targets in High Resolution Synthetic Aperture Radar Images.ICASSP-94,1994,V/9_V12 vol.5.
[132]Q.H.Pham,et al.Multistage Algorithm for Detection of Targets in SAR Image Data.SPIE,1997,3070:66-74.
[133]F.Amoozegar,M.K.Sundareshan.A Robust Neural Network Scheme for Constant False Alarm Rate Processing for Target Detection in Clutter Environment.Proceedings of the American Control Conference,Baltimore,Maryland,1994,1727-1730.
[134]P.Weber,S.Haykin.Order Statistics CFAR for Two-Parameter Distributions with Variable Skewness.IEEE Trans.on AES.,Vol.21,1985.
[135]Y.Lu,et al.CFAR Detection for K-Distribution Multi-Looked SAR Image.IEEE International Radar Conf.284-288.
[136]Y.J.Yu,et al.CFAR Detector for High-Resolution Multi-Look SAR Image.Electronics Letters,1999,35(19):1668-1670.
[137]P.P.Gandhi,S.A.Kassam.Analysis of Some CFAR Processors in Nonhomogeneous Background.IEEE Trans.on AES.,1988,24(2):427-445.
[138]L.M.Novak,M.C.Burl,W.W.Irving and G.J.Owirka.Optimal Polarimetric Processing for Enhanced Target Detection,IEEE,1999,69-75.
[139]L.M.Novak,et al.An Efficient Multi-Target SAR ATR Algorithm.IEEE,1998,3-13.
[140]R.Xu,et al.Target Isolation for SAR Images.SPIE,2001,4382:191-194.
[141]G.R.Bentiz.High-Definition Vector Imaging for Synthetic Aperture Radar.IEEE,1998,1209-1209.
[142]D.Casasent,S.Ashizawa.SAR Detection and Recognition Filters.SPIE,1997,3070:125-134.
[143]J.X.Zhang,et al.SAR Image Enhancement for Small Target Detection.IEEE,2003,449-452.
[144]M.R.Allen,L.E.Hoff.Wide-Angle Wideband SAR Matched Filter Image Formation for Enhanced Detection Performance.SPIE,1994,2230:302-314.
[145]J.G.Nanis,et al.Adaptive Filters for Detection of Targets in Foliage.IEEE,1994,101-103.
[146]M.Scheffe,et al.Multicovariance Matched Filter for Target Detection and Background Recognition.SPIE,1994,2235:203-208.
[147]M.Allen,et al.Neural Processing of SAR Imagery for Enhanced Target Detection.SPIE,1995,2847:201-210.
[148]P.Wan,et al.New Method of Point Target Detection for SAR Image.SPIE,2000,4082:136-140.
[149]P.Thanyasrisung,et al.Reduced-rank Automatic Target Detection and Recognition.IEEE,2000,1530-1534.
[150]R.Shenoy,D.Casasent.Unconstrained Eigen-Filter Pairs for SAR Detection.SPIE,2002,4734:1-8.
[151]M.Kim.Focus of attention based on Gamma kernels for automatic target recognition.Doctor's Dissertion,Florida University,1996.43-70.
[152]J.C.Principe,et al.Target prescreening based on a quadratic Gamma discriminator.IEEE Trans.on AES.,1998,34(3):706-715.
[153]J.C.Principe,et al.The Gamma filter- A new class of adaptive ⅡR filters with restricted feedback,IEEE Transactions on Signal Processing,41,2(1993),649-656.
[154]J.C.Principe,et al.Target Discrimination in Synthetic Aperture Radar Using Artificial Neural Networks.IEEE Trans.on IP.,1998,7(8):1136-1149.
[155]M.Kim,et al.A New CFAR Stencil for Target Detections in Synthetic Aperture Radar(SAR) Imagery.SPIE,1996,2757:432-438.
[156]M.Kim,J.C.Principe.Artificial Neural Networks with Gamma Kernels for Automatic Target Detection.IEEE,1996,1594-1599.
[157]L.K.Yen,et al.A CFAR Intensity Pattern Detector For MMW SAR Images.IEEE,1998,974-978.
[158]J.S.Salowe.Very Fast SAR Detection.SPIE,1996,2755:58-69.
[159]张翠,等.一种高分辨率SAR图像快速目标检测算法.遥感学报,2005,9(1):45-49.
[160]高贵,等.SAR图像目标检测的两种CFAR算法对比研究.信号处理,录用待刊.
[161]W.Phillips,R.Chellappa.Target Detection in SAR:Parallel Algorithms,Context Extraction,and Region Adaptive Techniques.SPIE,1997,3070:76-87.
[162]M.Brizi,et al.Exploiting The Shadow Information to Increase The Target Detection Performance in SAR Images.Intern.Conf.on Radar Systems,Radar 99-Brest(France),1999.
[163]H.Leung,et al.Detection of Small Objects in Clutter Using a GA-RBF Neural Network.IEEE Trans.on AES.,2002,38(1):98-118.
[164]G.A.Lampropoulos,H.Leung.On CFAR Detection of Small Man Made Targets Using Chaotic and Statistical CFAR Detectors.SPIE,1999,3809:29-41.
[165]L.I.Perlovsky,W.H.Schoendorf.Model-Based Neural Network for Target Detection in SAR Images.IEEE Trans.on IP.,1997,6(1):203-216.
[166]L.K.Yen.Focus of Attention for Millimeter and Ultra Wideband Synthetic Aperture Radar Imagery.University of Florida,Doctor's Dissertation,1998.
[167]J.G.Landowski,R.S.Loe.Target Cluster Detection in Cluttered Synthetic Aperture Radar Imagery.SPIE,1989,1099:9-16.
[168]方学立,等.基于位置相关的SAR图像中分布式目标检测.电子与信息学报,2006,28(2):350-353。
[169]L.M.Kaplan.Improved SAR Target Detection via Extended Fractal Features.IEEE Trans.on AES.,2001,37(2):436-450.
[170]L.M.Kaplan,et al.SAR Target Detection by Fusion of CFAR,Variance,and Fractal Statistics.SPIE,1998,3374:167-178.
[171]L.M.Kaplan,R.Murenzi.Evaluation of CFAR and Texture Based Target Detection Statistics on SAR Imagery.IEEE,1998,2141-2144.
[172]L.M.Kaplan,et al.Extended Fractal Feature for First Stage SAR:Target Detection.1999,SPIE,3721:35-46.
[173]N.S.Subotic,et al.A Multiresolution Generalized Likelihood Ratio Detection Approach to Target Screening in Synthetic Aperture Radar Data.SPIE,1995,2487:226-234.
[174]M.Tello,et al.A Novel Algorithm for Ship Detection in SAR Imagery Based on The Wavelet Transform.IEEE GRS.Letters,2005,2(2):201-205.
[175]N.S.Subotic,et al.Multiresolution Target Detection in SAR Imagery.IEEE,1995,2157-2160.
[176]N.S.Subotic,et al.A Multiresolution Approach to Target Detection in Synthetic Aperture Radar Data.IEEE Radar Conference,1995,pp.122-126.
[177]D.Blacknell.Contextural Information in SAR Target Detection.IEE Proc.Radar,Sonar Navig.,2001,148(1):41-47.
[178]S.M.Marni,et al.Context-Aided False Alarm Reduction for SAR Automatic Target Recognition.IEEE Radar Conference,1997,pp.885-888.
[179]D.Greig,et al.Knowledge-Based Methods for Small Object Detection in SAR Images.SPIE,2003,4883:121-130.
[180]R.Paget,et al.(Automatic) Target Detection in Synthetic Aperture Radar Imagery Via Terrain Recognition.IEEE IGRASS,2001,pp.54-57.
[181]张翠,高分辨率SAR图像自动目标识别方法研究,国防科技大学博士学位论文,2003。
[182] R. Meth. Synthetic Aperture Radar Vehicle Detection via Subapture Coherence,http://www.asc2002.com/manuscripts/J/Jo-02.pdf. 2002.
[183] C.L. Wang, et al. Man-Made Target Detection in SAR Imagery. IEEE, 2005.
[184] W.W. Irving, S.M. Verbout. False Alarm Reduction Through Road Area Delimitation. IEEE International Radar Conf. 1995, 656-661.
[185] J.K. Jiao, et al. Coherent Spatial Filtering for SAR Detection of Stationary Targets. IEEE Trans. on AES., 1999,35(2):614-626.
[186] A. Filippidis, et al. Fusion of Intelligent Agents for the Detection of Aircraft in SAR Images. IEEE Trans. on PAMI., 2000, 22(4):378-384.
[187] L.M. Kaplan, et al. Detection of Broadside Targets during Image Formation using a Quad-Tree Approach. IEEE International Radar Conference, Arlington, Virginia,2000: 104-109.
[188] L.M. Kaplan, et al. Prescreening during Image Formation for Ultra-WideBand Radar. IEEE Trans. on AES, 2002, 38(1):74-88.
[189] L.M. Lu, et al. A Target Detection Method in Range-Doppler Domain from SAR Echo Data. IEEE International Conference on Pattern Recognition. Quebec,Canada, 2002, 1:91-94.
[190] J.D. Gorman, et al. Multiresolution Detection of Coherent Radar Targets. IEEE,1994, 446-450.
[191] M. McClure, et al. Multiresolution Signature -Based SAR Target Detection. SPIE,1998,3370:318-329.
[192] A.E. El-Rouby, et al. Application of Frequency Correlation Function to Radar Target Detection. IEEE Trans. on AES., 2003, 39(1): 125-139.
[193] J. Vihonen, et al. New Detection Algorithm for Poor Signal-to-Noise Conditions.IEEE, 2003, 345-349.
[194] D. Shoham. Phase-Stability Detection of Stationary Targets. IEEE, 1990,558-562.
[195] L. M. Novak, et al. An Efficient Multi-Target SAR ATR Algorithm. IEEE, 1998,3-13.
[196] D. E. Kreithen, et al. A Theoretical Analysis of a Ranking Discrimination Algorithm. IEEE, 1992: 431-435.
[197] S. M. Verbout, et al. New Image Features for Discriminating Targets from Clutter.SPIE, 1998,3395: 120-137.
[198] T. Cooke, et al. Comparison of Selected Features for Target Detection in Synthetic Aperture Radar Imagery. Digital Signal Processing, 2000, 10(4):286-296.
[199] O. Laskin, et al. Automatic Target Recognition in Synthetic Aperture Radar Images. 2000: 1-13.
[200] Z. H. Chen. Machine Learning Approach Towards Automatic Target Recognition.Harvard University, 2001.
[201] K. Ranney, et al. Comparison of Three Different Detectors Applied to Synthetic Aperture Radar Data. SPIE, 2002, 4727:91-99.
[202] B. Bhanu, et al. Genetic Algorithm Based Feature Selection For Target Detection in SAR Images. Image and Vision Computing, 2003,21(7): 591-608.
[203] Y. Q. Lin, et al. Evolutionary Feature Synthesis for Object Recognition. IEEE Trans. Systems, Man, And Cybernetics-Part C: Applications and Reviews, 2005,35(2): 156-171.
[204] S.D. Halversen, et al. New Approaches for Detecting Groups of Targets. the Twenty-Eighth Asilomar Conference on Signals, Systems and Computers, 1994:137-140.
[205] G. J. Owirka, et al. An Algorithm for Detecting Groups of Targets. IEEE International Radar Conference. 1995: 641-643.
[206] D. Blacknell, et al. SAR Image Understanding Using Contextual Information.SPIE, 2002,4543:73-84.
[207] R. Kothari, et al. On Lateral Connections in Feed-Forward Neural Networks.IEEE International Conf. Neural Networks, Washington, DC, USA, 1996, 3:1594-1599.
[208] R. Kothari, et al. Self-Regulation of Model Order in Feed-Forward Neural Networks. IEEE International Conf. Neural Networks, Houston, Texas, USA,1997: 1966-1971.
[209] P. Runkle, et al. Hidden Markov Models for Multi-Aspect Target Classification.IEEE Trans. Signal Processing, 1999,47(7): 2035-2040.
[210] P. Runkle, et al. Multi-Aspect Target Detection for SAR Imagery Using Hidden Markov Models. IEEE Trans. GRS, 2001, 39(1): 46-55.
[211] B. Krishnapuram, et al. Physics-Based Detection of Targets in SAR Imagery Using Support Vector Machines. IEEE Sensors Journal, 2003, 3(2): 147-157.
[212] W. W. Irving, et al. A Multiresolution Approach to Discrimination in SAR Imagery. IEEE Trans. AES, 1997, 33(4): 1157-1168.
[213] M. Cetin, et al. A Statistical Method For Discrimination of Natural Terrain Types from SAR Data. IEEE, 1998: 587-591.
[214] D. E. Kreithen, et al. A Discrimination Algorithm And The Effect of Resolution.IEEE National Radar Conference, 1993, 128-133.
[215] L. M. Novak, et al. Effect of Polarization and Resolution on SAR ATR. IEEE Transaction on AES. 1997,33(1): 102-115.
[216] M. Denny, et al. SAR Image Feature Extraction Using Shadow Information. SPIE,2003,4883:192-200.
[217] A. C. van den Broek, et al. Robustness of Features for Automatic Target Discrimination in High Resolution Polarimetric SAR Data. SPIE, 2003,5095:242-253.
[218] K. Fukunaga, et al. The Acquisition Probability for A Minimum Distance One-Class Classifier. IEEE Trans. AES, 1987,23(4):493-498.
[219] D. Blacknell. Adaptive Design and Characterisation of Features for SAR ATR.SPIE, 2001,4382: 252-263.
[220] D. Blacknell. Feature Analysis for SAR ATR. SPIE, 2000, 4173: 75-86.
[221] L. S. Liebovitch, et al. A Fast Algorithm to Determine Fractal Dimensions By Box Counting. Physics Letters A, 1989, 141(8,9): 386-390.
[222] Y. Q. Lin, et al. Object Detection via Feature Synthesis Using MDL-Based Genetic Programming. IEEE Trans. Systems, Man, And Cybernetics-Part B:Cybernetics, 2005, 35(3): 538-547.
[223] K. Krawiec, et al. Visual Learning by Revolutionary Feature Synthesis. IEEE Trans. Systems, Man, And Cybernetics-Part B: Cybernetics, 2005, 35(3):409-425.
[224] Y. Q. Lin. Feature synthesis and analysis by evolutionary computation for object detection and recognition. University of California, 2003.
[225] T. Cooke, et al. Comparison of Selected Features for Target Detection in Synthetic Aperture Radar Imagery. In Proceedings of 33th Asilomar Conference on Signals, System and Computers, Pacific Grove, CA, USA, 1999, 2: 859-863.
[226] L. A. Lisoba da Silva, Cardoso. Computer Aided Recognition of Man-Made Structures in Aerial [Master's Dissertation]. Naval Postgraduate School Monterey,1999.
[227] D. Borghys. Interpretation and Registration of High-Resolution Polarimetric SAR Images[D]. ENSTE 031, Paris, 2001.
[228] T. R. Damarla, et al. Automatic Target Detection Algorithm for Foliage Penetrating Ultra-Wideband SAR Data using Split Spectral Analysis. SPIE, 1999,3704:113-120.
[229] A. Radisavljevic, et al. Target Prescreening Based on a Quadratic Gamma Detector. SPIE, 1995.
[230] C. H. Fosgate, et al. Multi-Scale Segmentation and Anomaly Enhancement of SAR Imagery. IEEE Trans. IP, 1997,6(1):7-20.
[231] D. Howard, et al. Multi-Scale Models for Target Detection and Background Discrimination in Synthetic Aperture Radar Imagery. Digital Signal Processing,1999, 9(3): 149-161.
[232] J. Schroeder, et al. Multi-Scale Models for Target Detection in Complex Synthetic Aperture Radar. In Proceedings of Information, Decision and Control, Adelaide,Southern,Australia,1999:77-82.
[233]J.S.De Bonet,et al.Flexible Histograms:A Multiresolution Target Discrimination Model.http://www.ai.mit.edu/projects/lv.
[234]R.Kothari.A Function Approximation Framework for ROI Determination in SAR Images.http://www.mbvlab.wpafb.af.mil/paper.html,2004.
[235]R.Kothari,et al.On the Use of Model Order for Detection Potential Target Location in SAR Images.http://www.mbvlah.wpafb,af.mil/paper.html,2004.
[236]P.Runkle,et al.Multi-Aspect Target Detection in SAR Imagery Using Hidden Markov Models.SPIE,1999,3721:214-223.
[237]A.Robert,et al.MRF-Based Algorithms for Segmentation of SAR Images.the Proceeding of the 1998 International Conference on Image Processing.Paris:IEEE,1998.770-774.
[238]钟雪莲,等.SAR图像中目标的自动检测与辨别.中国图象图形学报,2005,10(6):688-697.