合成孔径雷达图像目标检测技术的研究
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摘要
合成孔径雷达(Synthetic Aperture Radar，SAR)是一种工作在微波波段的主动式雷达系统，使用相干成像方式。具有全天时、全天候、能穿透云雾烟尘，大面积成像的特点，在军事和民用领域受到普遍关注。目前，许多研究机构都致力于研发SAR图像自动目标识别系统(ATR)，SAR图像目标检测技术在整个目标识别系统中是一个重要的环节。在此背景下，本论文致力于研究SAR图像的目标检测技术。
对SAR图像统计特性的分析是进行SAR图像处理与分析的基础。传统的方法都是从SAR成像机理出发，从理论和SAR图像的实际情况来分析SAR的统计特性。本论文根据分布模型的选取准则(如K-S测试，A-D测试等)，通过实验的方法深入分析SAR图像的统计特征。基于恒虚警的SAR图像目标检测包括杂波强度的估计，分布模型的选取和其参数的估计。前者有单元平均，选大选小和有序统计量等估计算法。再结合杂波的统计模型，估计出分布的参数，从而给出检测阈值。论文中仔细分析了恒虚警检测算法的原理和实现。对所有算法实现并分析了真实图像的检测结果。
在此基础上，论文提出了新的基于区域分类的智能恒虚警检测算法。新的算法首先将传统滑动窗模型的背景区域进行分块，判断区域的类型，然后智能选取适当的子块作为真正的背景区域来进行参数的估计和阈值的计算。实验结果表明，该方法在同质性区域保持较好检测性能的同时，在多目标和杂波边缘的异质性区域也有较强的鲁棒性。在SAR图像目标检测结果评价和分析部分，给出了目标检测评价准则，通过这些准则对以上目标检测算法进行评价，评价结果验证了新算法的有效性。最后，介绍了SAR图像处理平台，说明了软件平台的功能和架构，并给出实际软件界面演示。
Synthetic Aperture Radar (SAR) is the kind of initiative radar system which works on microwave bandwidth with the coherent way. SAR has the power of imaging for larger area under all weather condition and without daylight. This made it extensively used in many fields, such as military, in which target detection and recognition technique has been researched and developed. Target detection techniques based on SAR images is one of the most important techniques in the whole Auto Target Recognition (ATR) system. This dissertation focuses on the research of target detection techniques based on SAR images.
The analysis of the statistic characteristics of SAR image is essential for the SAR image analysis and processing. In generally, based on the principle of SAR imaging, the traditional methods analyze the statistic characteristics of SAR image through the theory and certain conditions. However, based on the performance criterions of statistic models, such as Kolmogorov-Smirnov test, Anderson-Darling test, this dissertation analyzed deeply the statistic characteristics of SAR image through the experiments. Target detection techniques based on Const False Alarm Ration (CFAR) include estimator for the intensity of clutter, selector of the distribution models and estimator for the parameters of the models. The algorithms of estimating for the intensity of clutter are presented, such as cell-average, Smallest-of, Greatest-of and Order Statistic. We can obtain the detection threshold through estimating the parameters of the certain distribution model Then, this dissertation analyzes the principle of CFAR detection
algorithms detailedly, realizes all the algorithms and analyzes the detection results of real SAR images.
On the basis of traditional CFAR, a novel intelligent CFAR detection algorithm based on region classification is proposed in this dissertation. Firstly, the clutter region of moving windows model is divided into four parts. Secondly, some appropriate sub-blocks are selected as real background region, which is used to estimate the parameters and calculate the threshold of detection. The experimental results show that the new method has a good detection performance not only in homogeneous clutter background but also in non-homogenous clutter background including multi-target environment and clutter edge environment. In pan of the analysis and evaluation of detection results, the evaluation criterions of target detection are presented. Based on the evaluation criterions, the new algorithm is shown to be effective; Moreover, the platform of SAR images processing is introduced by explaining the function and framework of this software and showing the interface demo.
The research of this dissertation is supported by the national nature science fund, national hi-tech development plan (863) projects. Some research productions have been applied in the radar data processing module of the development of the universal remote sensing data processing software, which is the key project of the tenth-five years plan.
对SAR图像统计特性的分析是进行SAR图像处理与分析的基础。传统的方法都是从SAR成像机理出发，从理论和SAR图像的实际情况来分析SAR的统计特性。本论文根据分布模型的选取准则(如K-S测试，A-D测试等)，通过实验的方法深入分析SAR图像的统计特征。基于恒虚警的SAR图像目标检测包括杂波强度的估计，分布模型的选取和其参数的估计。前者有单元平均，选大选小和有序统计量等估计算法。再结合杂波的统计模型，估计出分布的参数，从而给出检测阈值。论文中仔细分析了恒虚警检测算法的原理和实现。对所有算法实现并分析了真实图像的检测结果。
在此基础上，论文提出了新的基于区域分类的智能恒虚警检测算法。新的算法首先将传统滑动窗模型的背景区域进行分块，判断区域的类型，然后智能选取适当的子块作为真正的背景区域来进行参数的估计和阈值的计算。实验结果表明，该方法在同质性区域保持较好检测性能的同时，在多目标和杂波边缘的异质性区域也有较强的鲁棒性。在SAR图像目标检测结果评价和分析部分，给出了目标检测评价准则，通过这些准则对以上目标检测算法进行评价，评价结果验证了新算法的有效性。最后，介绍了SAR图像处理平台，说明了软件平台的功能和架构，并给出实际软件界面演示。
Synthetic Aperture Radar (SAR) is the kind of initiative radar system which works on microwave bandwidth with the coherent way. SAR has the power of imaging for larger area under all weather condition and without daylight. This made it extensively used in many fields, such as military, in which target detection and recognition technique has been researched and developed. Target detection techniques based on SAR images is one of the most important techniques in the whole Auto Target Recognition (ATR) system. This dissertation focuses on the research of target detection techniques based on SAR images.
The analysis of the statistic characteristics of SAR image is essential for the SAR image analysis and processing. In generally, based on the principle of SAR imaging, the traditional methods analyze the statistic characteristics of SAR image through the theory and certain conditions. However, based on the performance criterions of statistic models, such as Kolmogorov-Smirnov test, Anderson-Darling test, this dissertation analyzed deeply the statistic characteristics of SAR image through the experiments. Target detection techniques based on Const False Alarm Ration (CFAR) include estimator for the intensity of clutter, selector of the distribution models and estimator for the parameters of the models. The algorithms of estimating for the intensity of clutter are presented, such as cell-average, Smallest-of, Greatest-of and Order Statistic. We can obtain the detection threshold through estimating the parameters of the certain distribution model Then, this dissertation analyzes the principle of CFAR detection
algorithms detailedly, realizes all the algorithms and analyzes the detection results of real SAR images.
On the basis of traditional CFAR, a novel intelligent CFAR detection algorithm based on region classification is proposed in this dissertation. Firstly, the clutter region of moving windows model is divided into four parts. Secondly, some appropriate sub-blocks are selected as real background region, which is used to estimate the parameters and calculate the threshold of detection. The experimental results show that the new method has a good detection performance not only in homogeneous clutter background but also in non-homogenous clutter background including multi-target environment and clutter edge environment. In pan of the analysis and evaluation of detection results, the evaluation criterions of target detection are presented. Based on the evaluation criterions, the new algorithm is shown to be effective; Moreover, the platform of SAR images processing is introduced by explaining the function and framework of this software and showing the interface demo.
The research of this dissertation is supported by the national nature science fund, national hi-tech development plan (863) projects. Some research productions have been applied in the radar data processing module of the development of the universal remote sensing data processing software, which is the key project of the tenth-five years plan.
引文
[1] F．F．Sabins．遥感原理及解译．地质出版社．1978年．
[2] F．T．Ulaby，R．K．Moore，A．K．Fung．微波遥感(第一卷)：微波遥感基础和辐射测量学．科学出版社．1988年．
[3] L.M. Novak, S.D. Halversen, GJ. Owirka, M. Hiett, Effects of Polarization and Resolution on SAR ATR [J]. IEEE Transactions on aerospace and electronic systems, 1997, 36(1): 102-115.
[4] L.M. Novak, G.J. Owirka, A.L. Weaver, Automatic target recognition using enhanced resolution SAR data[J], IEEE Transactions on aerospace and electronic systems, 1999, 35(1): 157-175.
[5] D.E. Kreithen, S.D. Halversen, and G.J. Owirka, Discriminating targets from clutter, Lincoln Lab. J., vol. 6, 1993, 25-52.
[6] Vassilis A nastassopoulos, George A.Lampropoulos, Optimal CFAR detection in Weibull clutter, IEEE.Trans. on Aerospace and Electronic Systems, Vol.31, No. 1, pp.52-64,1995
[7] L.M.Novak, M.C.Burl, W.W. Irving, Optimal polarimetric processing for enhanced target detection, IEEE.Trans. on Aerospace and Electronic Systems, Vol.29, No. 1, 1993
[8] Leonid I.Perlovsky, William H.Svhoendorf, Model base neural network for target detection in SAR images, IEEE. Trans. on Image Processing, Vol.6, No, 1, 203-215,1997
[9] Knut Eldhuset, An automatic ship and ship wake detection system for spaceborne SAR images in coastal regions, IEEE Trans.on, G.R.S, Vol.34, No.4, 1010-1019, 1996
[10] Quoc H.Pham, Timothy M.Brosnan, Multistage Algorithm for detection target in SAR image data, SPIE Vol.3070, 66-75, 1997
[11] L.Benyoussef, Y.Delignon, An optimal matched filter for target detection in images distorted by noise, TGARSS'98, 1000-1003, 1998
[12] Shuchman R.A., C.C.Wackerman, A.L.Maffett, R.G.Onstott, and L.L.Sutherland, The Discrimination of Sea Ice Types Using SAR Backscatter Statistics, International Geosicience and Remote Sensing Symposium, 1989, Vol.1,381-385
[13] Nystuen J.A., and F.W.Garcia, Jr, Sea Ice Classification Using SAR Backscatter Statistics, IEEE Trans. Geosci. Remote Sensing, 1992, Vol.30, No.3, 502-509
[14] Barber D.G., M.E.Shokr, R.A.Fernandes, E.D.Soulis,D.G.Flett, and E.ELeDrew, A Comparison of Second-order Classifiers for SAR Sea Ice Distrimination, Photogrammetric Engineering and Remote Sensing, 1993, Vol.59, No.9, 1397-1408
[15] L.-K. Soh and C. Tsatsoulis, Texture Analysis of SAR Sea Ice Imagery Using Gray Level Co-occurrence Matrices, IEEE Transactions on Geoscience and Remote Sensing, 1999, Vol 37, No. 2, 780-795
[16] Clausi, D.A., Comparison and Fusion of Co-occurrence, Gabor, and MRF Texture Features for Classification of SAR Sea Ice Imagery, Atmosphere & Oceans, 2001, Vol. 39, No. 4, 183-194
[17] Kaplan L.M., and Kuo, C.-C.J., Texture Roughness Analysis and Synthesis via Extended Self-similar (ESS) model, IEEE Trans. on, Pattern Analysis and Machine Intelligence, 1995, Vol.17, 1043-1056
[18] Novak L.M., S.D.Halversen, G.J.Owirka, M. Hiett, Effects of Polarisation and Resolution on the Performance of a SAR Automatic Target Recognition System, Lincoln Laborary J, Vol.6,49-67, 1995
[19] 郭华东等，雷达对地观测理论与应用，科学出版社，2000年12月。
[20] M.R. Cowper, B. Mulgrew and C.P. Unsworth, Investigation into the use of nonlinear predictor
networks to improve the performance of maritime surveillance radar target detectors, IEEE Pro.-Radar, Sonar Navig., Vol. 148, No.3, June 2001, 103-111.
[21] S. F. George, The detection of nonfluctuating targets in log-normal clutter, NRL Rebort 6796, 1968.
[22] J.D. Gibbons, Nonparametric methods for quantitative analysis, 2d ed., American Sciences Press, Columbus, Ohio.
[23] K. Pearson, On a criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen in random sampling, Phil. Mag.(5),50,1900,157-175.
[24] H. Chernoff, E.L. Lehmann, The Use of Maximum Likelihood Estimates in chi2 Test for goodness of fit, Ann. Math. Statist. 25:579-586(1954).
[25] J.K. Yarnold, The minimum expectation in chi2 goodness-of-fit tests and the accuracy of approximations for the null distribution, J. Am. Statist. Assoc., 65:864-886(1970).
[26] T. Gonzalez, S. Sahni, and W.R. Franta, An efficient algorithm for the Kolmogorov-Smirnov and Lilliefors Tests, ASSOC. Comput. Math. Trans. Math. Softwarw, 3:60-64(1977).
[27] M.A. Stephens, EDF statistics for goodness of fit and some comparisons, J. Am. Statist. Assoc., 69: 730-737(1974).
[28] H.W. Lilliefors, On the Kolmogorov-Smirnov test for normality with mean and variance unknown, J. Am. Statist. Assoc., 64:387-389(1969).
[29] J. Durbin, Kolmogorov-Smirnov tests whuen parameters are estimated with applications to tests of exponentiality and tests on spacings, Biometrika, 62:5-22(1975).
[30] R.C. Littell, J.T. McClave, and W.W. Often, Goodness-of-Fit tests for the two parameter weibull distribution, Commun. Statist., B8: 257-269(1979).
[31] R.B.D. Agostino, and M.A. Stephens, Goodness-of-fit tests, Marcel Dekker, New York(1986).
[32] H. Akaike, Information theory and an extension of maximum likelihood principle, 2rid International Symposium on Information Theory, eds. B.N. Petrov and F. Csaki, 1973, Akademiai Kiado, Budapest, 267-281.
[33] S. Sayama, H. Sekine, Weibull, log-Weibull and K-distributed ground clutter modeling analyzed by AIC, IEEE Transactions on Aerospace and Electrortic Systems, Vol. 37(3), July 2001, 1108-1113.
[34] P.P. Gandhi, and S.A. Kassam, Analysis of CFAR processors in nonhomogeneous background, IEEE Transactions on Aerospace and Electronic Systems, 24,4(1988), 427-445.
[35] 何友、关键、彭应宁、陆大著，雷达自动检测与恒虚警处理，清华大学出版社，1999年5月．
[36] Gandhi P P, and Kassam S A, Analysis of CFAR processors in nonhomogeneous background [J]. IEEE Transactions on Aerospace and Electronic Systems, 1988, 24(4): 427-445.
[37] Khalighi M A, Bastani M H. Adaptive CFAR Processor for Nonhomogenedous Environments[J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(3): 889-896.
[38] [4] Michael E Smith, Pramod K Varshney, Intelligent CFAR Processor Based on Data Variability [J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(3): 837-846.
[39] D.T. Kuan, A.A. Sawchuk, T.C. Strand, P. Chavel. Adaptive restoration of images with speckle. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7(2), 1985.
[40] Novak L M, Owirka G J, and Netishen C M. Performance of a High-resolution Polarimetric SAR Automatic Target Recognition System [J]. Lincoln Laboratory Journal, 1993, 6(1): 11-23.
[41] Billingsley J B, Farina A, Gini F. Statistical Analyses of Measured Radar Ground Clutter Data[J]. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(2): 579-592.
[42] Lombardo P and Sciotti M. Segmentation-based Technique for Ship Detection in SAR Images [J], IEE Pro.-Radar, Sonar Navig., 2001, 148(3): 147-159.
[43] Lance M Kaplan. Improved SAR Target Detection via Extended Fractal Features[J]. IEEE
Transactions on Aerospace and Electronic Systems, 2001, 37(2): 436-451.
[44] Touzi R, Lopes A, Bousquet P, A Statistical and Geometrical Edge Detector for SAR Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 26(6): 64-773.
[45] Oliver C, Quegan S, Understanding Synthetic Aperture Radar Images [M]. Norwood: Artech House Inc., 1998.
[46] J. C. Mossing, T. D. Ross, "An Evaluation of SAR ATR Algorithm Performance Sensitivity to MSTAR Extended Operating Conditions", pp.554-565, Proc. SPIE, vol 3370, Algorithms for Synthetic Aperture Radar Imagery Ⅴ, April 1998
[47] T. D. Ross, V. Velton, J. Mossing, S. Worrell, M. Bryant, "Standard SAR ATR Evaluation Experiments using the MSTAR Public Release Data Set", Proc. SPIE, vol 3370, SPIE'98 Algorithms for Synthetic Aperture Radar Imagery V, April 1998
[48] Hong Sun. Radiometric Multiresolution Analysis For Speckle Filtering. Depot legal: 2000.4eme, ISSN 0751-1345 ENST D(Paris) (France 1983-9999), Nov, 2000.
[49] Hong Sun, Maitre H. Radiometric Multiresolution Analysis for Speckle Filtering. CIE International Conference on Radar Processings. 2001
[50] 黄培荣，管鲍，孙洪，基于幅度统计多分辨率分析的SAR图像灰度压缩，武汉大学学报(理学版)，2003．1，vol．49，112～116
[51] 徐新，星载SAR图像处理与分析中若干关键技术的研究，武汉大学博士学位论文，2003年5月。
[2] F．T．Ulaby，R．K．Moore，A．K．Fung．微波遥感(第一卷)：微波遥感基础和辐射测量学．科学出版社．1988年．
[3] L.M. Novak, S.D. Halversen, GJ. Owirka, M. Hiett, Effects of Polarization and Resolution on SAR ATR [J]. IEEE Transactions on aerospace and electronic systems, 1997, 36(1): 102-115.
[4] L.M. Novak, G.J. Owirka, A.L. Weaver, Automatic target recognition using enhanced resolution SAR data[J], IEEE Transactions on aerospace and electronic systems, 1999, 35(1): 157-175.
[5] D.E. Kreithen, S.D. Halversen, and G.J. Owirka, Discriminating targets from clutter, Lincoln Lab. J., vol. 6, 1993, 25-52.
[6] Vassilis A nastassopoulos, George A.Lampropoulos, Optimal CFAR detection in Weibull clutter, IEEE.Trans. on Aerospace and Electronic Systems, Vol.31, No. 1, pp.52-64,1995
[7] L.M.Novak, M.C.Burl, W.W. Irving, Optimal polarimetric processing for enhanced target detection, IEEE.Trans. on Aerospace and Electronic Systems, Vol.29, No. 1, 1993
[8] Leonid I.Perlovsky, William H.Svhoendorf, Model base neural network for target detection in SAR images, IEEE. Trans. on Image Processing, Vol.6, No, 1, 203-215,1997
[9] Knut Eldhuset, An automatic ship and ship wake detection system for spaceborne SAR images in coastal regions, IEEE Trans.on, G.R.S, Vol.34, No.4, 1010-1019, 1996
[10] Quoc H.Pham, Timothy M.Brosnan, Multistage Algorithm for detection target in SAR image data, SPIE Vol.3070, 66-75, 1997
[11] L.Benyoussef, Y.Delignon, An optimal matched filter for target detection in images distorted by noise, TGARSS'98, 1000-1003, 1998
[12] Shuchman R.A., C.C.Wackerman, A.L.Maffett, R.G.Onstott, and L.L.Sutherland, The Discrimination of Sea Ice Types Using SAR Backscatter Statistics, International Geosicience and Remote Sensing Symposium, 1989, Vol.1,381-385
[13] Nystuen J.A., and F.W.Garcia, Jr, Sea Ice Classification Using SAR Backscatter Statistics, IEEE Trans. Geosci. Remote Sensing, 1992, Vol.30, No.3, 502-509
[14] Barber D.G., M.E.Shokr, R.A.Fernandes, E.D.Soulis,D.G.Flett, and E.ELeDrew, A Comparison of Second-order Classifiers for SAR Sea Ice Distrimination, Photogrammetric Engineering and Remote Sensing, 1993, Vol.59, No.9, 1397-1408
[15] L.-K. Soh and C. Tsatsoulis, Texture Analysis of SAR Sea Ice Imagery Using Gray Level Co-occurrence Matrices, IEEE Transactions on Geoscience and Remote Sensing, 1999, Vol 37, No. 2, 780-795
[16] Clausi, D.A., Comparison and Fusion of Co-occurrence, Gabor, and MRF Texture Features for Classification of SAR Sea Ice Imagery, Atmosphere & Oceans, 2001, Vol. 39, No. 4, 183-194
[17] Kaplan L.M., and Kuo, C.-C.J., Texture Roughness Analysis and Synthesis via Extended Self-similar (ESS) model, IEEE Trans. on, Pattern Analysis and Machine Intelligence, 1995, Vol.17, 1043-1056
[18] Novak L.M., S.D.Halversen, G.J.Owirka, M. Hiett, Effects of Polarisation and Resolution on the Performance of a SAR Automatic Target Recognition System, Lincoln Laborary J, Vol.6,49-67, 1995
[19] 郭华东等，雷达对地观测理论与应用，科学出版社，2000年12月。
[20] M.R. Cowper, B. Mulgrew and C.P. Unsworth, Investigation into the use of nonlinear predictor
networks to improve the performance of maritime surveillance radar target detectors, IEEE Pro.-Radar, Sonar Navig., Vol. 148, No.3, June 2001, 103-111.
[21] S. F. George, The detection of nonfluctuating targets in log-normal clutter, NRL Rebort 6796, 1968.
[22] J.D. Gibbons, Nonparametric methods for quantitative analysis, 2d ed., American Sciences Press, Columbus, Ohio.
[23] K. Pearson, On a criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen in random sampling, Phil. Mag.(5),50,1900,157-175.
[24] H. Chernoff, E.L. Lehmann, The Use of Maximum Likelihood Estimates in chi2 Test for goodness of fit, Ann. Math. Statist. 25:579-586(1954).
[25] J.K. Yarnold, The minimum expectation in chi2 goodness-of-fit tests and the accuracy of approximations for the null distribution, J. Am. Statist. Assoc., 65:864-886(1970).
[26] T. Gonzalez, S. Sahni, and W.R. Franta, An efficient algorithm for the Kolmogorov-Smirnov and Lilliefors Tests, ASSOC. Comput. Math. Trans. Math. Softwarw, 3:60-64(1977).
[27] M.A. Stephens, EDF statistics for goodness of fit and some comparisons, J. Am. Statist. Assoc., 69: 730-737(1974).
[28] H.W. Lilliefors, On the Kolmogorov-Smirnov test for normality with mean and variance unknown, J. Am. Statist. Assoc., 64:387-389(1969).
[29] J. Durbin, Kolmogorov-Smirnov tests whuen parameters are estimated with applications to tests of exponentiality and tests on spacings, Biometrika, 62:5-22(1975).
[30] R.C. Littell, J.T. McClave, and W.W. Often, Goodness-of-Fit tests for the two parameter weibull distribution, Commun. Statist., B8: 257-269(1979).
[31] R.B.D. Agostino, and M.A. Stephens, Goodness-of-fit tests, Marcel Dekker, New York(1986).
[32] H. Akaike, Information theory and an extension of maximum likelihood principle, 2rid International Symposium on Information Theory, eds. B.N. Petrov and F. Csaki, 1973, Akademiai Kiado, Budapest, 267-281.
[33] S. Sayama, H. Sekine, Weibull, log-Weibull and K-distributed ground clutter modeling analyzed by AIC, IEEE Transactions on Aerospace and Electrortic Systems, Vol. 37(3), July 2001, 1108-1113.
[34] P.P. Gandhi, and S.A. Kassam, Analysis of CFAR processors in nonhomogeneous background, IEEE Transactions on Aerospace and Electronic Systems, 24,4(1988), 427-445.
[35] 何友、关键、彭应宁、陆大著，雷达自动检测与恒虚警处理，清华大学出版社，1999年5月．
[36] Gandhi P P, and Kassam S A, Analysis of CFAR processors in nonhomogeneous background [J]. IEEE Transactions on Aerospace and Electronic Systems, 1988, 24(4): 427-445.
[37] Khalighi M A, Bastani M H. Adaptive CFAR Processor for Nonhomogenedous Environments[J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(3): 889-896.
[38] [4] Michael E Smith, Pramod K Varshney, Intelligent CFAR Processor Based on Data Variability [J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(3): 837-846.
[39] D.T. Kuan, A.A. Sawchuk, T.C. Strand, P. Chavel. Adaptive restoration of images with speckle. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7(2), 1985.
[40] Novak L M, Owirka G J, and Netishen C M. Performance of a High-resolution Polarimetric SAR Automatic Target Recognition System [J]. Lincoln Laboratory Journal, 1993, 6(1): 11-23.
[41] Billingsley J B, Farina A, Gini F. Statistical Analyses of Measured Radar Ground Clutter Data[J]. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(2): 579-592.
[42] Lombardo P and Sciotti M. Segmentation-based Technique for Ship Detection in SAR Images [J], IEE Pro.-Radar, Sonar Navig., 2001, 148(3): 147-159.
[43] Lance M Kaplan. Improved SAR Target Detection via Extended Fractal Features[J]. IEEE
Transactions on Aerospace and Electronic Systems, 2001, 37(2): 436-451.
[44] Touzi R, Lopes A, Bousquet P, A Statistical and Geometrical Edge Detector for SAR Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 26(6): 64-773.
[45] Oliver C, Quegan S, Understanding Synthetic Aperture Radar Images [M]. Norwood: Artech House Inc., 1998.
[46] J. C. Mossing, T. D. Ross, "An Evaluation of SAR ATR Algorithm Performance Sensitivity to MSTAR Extended Operating Conditions", pp.554-565, Proc. SPIE, vol 3370, Algorithms for Synthetic Aperture Radar Imagery Ⅴ, April 1998
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