SAR图像舰船目标检测方法研究
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摘要
近年来,利用SAR图像进行舰船目标检测是实现海洋监视、渔业管控的重要手段。我国领海广阔,海洋资源丰富,开展SAR图像舰船目标检测研究具有重要意义。从已有的舰船目标检测系统和检测算法可知,如何在保持检测率的条件下尽可能地减少虚警和提高算法效率是开展SAR图像舰船目标检测研究的重要指标。当前最常用的一类SAR图像舰船目标检测算法是基于统计模型的CFAR方法,本文即围绕此类方法开展研究。
分析海洋杂波的统计特性是基于统计模型的CFAR检测方法的基础。本文首先研究了SAR图像海洋杂波的统计特性,给出统计建模的一般步骤和拟合优度评价准则,评估了各种分布模型对遥感卫星一号SAR图像海杂波分布的拟合性能。
基于统计模型的CFAR是一种能够根据局部杂波统计特性计算检测阈值的自适应检测方法。根据背景杂波的统计特性,其检测性能可以从杂波分布模型和检测器两方面得以改进,本文首先总结了基于各种分布模型和不同检测器的CFAR检测方法,其次对基于K分布的CA-CFAR检测算法作了重点分析,并对比了几种基于统计模型CFAR方法在仿真K分布杂波环境中的理论性能。最后,针对遥感卫星一号海洋图像,将基于K分布的CA-CFAR和基于高斯、韦布尔、对数正态分布的CFAR检测算法作了对比实验。实验结果表明,基于K分布的CA-CFAR方法在保证检测率的同时有效地减少了虚警,检测性能优于其他几种基于统计模型的CFAR方法。
实际的舰船目标检测系统要求检测算法具有近实时性,本文对提高检测算法的效率也进行了研究。在分析已有快速检测算法的处理思路基础上,结合前文的工作,本文提出了一种基于两级CFAR级联的舰船目标快速检测算法,分析了第一级CFAR中分别采用高斯、韦布尔、对数正态分布时快速算法的检测性能和算法效率,并由对实测图像的实验验证了其检测性能。基于两级CFAR级联的舰船目标快速算法与基于K分布的CA-CFAR方法检测性能相当,但算法效率更能满足检测系统的实时性要求。
Recently, ship detection on Synthetic Aperture Radar (SAR) imagery is an important way of ocean surveillance and fishing monitoring. China has large ocean which is full of resources. It is of grate significance in developing ship detection researchment on SAR imagery. There are two criterions of developing researches of ship detection on SAR imagery: how to reduce the false alarms and raise detection efficiency under a certain detection rate. While the algorithms of Constant False Alarm Rate (CFAR) based on statistical models are widely used in SAR imagery ship detection, this paper mainly studies this kind of methods.
Statistical characteristic analyze is the basic of CFAR detection algorithms based on statistic models. This paper studies the statistical characteristics of SAR sea clutter, presents the setup of statistical modeling and the criterions of model fit performance, and assesses several models’modeling performances to SAR imagery of Remote Sensing-1.
The method of CFAR based on statistical model has the ability of automatic calculating detection threshold according to sea clutter’s local statistical characteristics. The detection performance can be improved from both using more accurate sea clutter statistical model and more suited detector. Therefore, the CFAR detection algorithms based on varies statistical models and detectors are summarized. After that, particular analyses are made on the CA-CFAR detection algorithm based on K distribution. Its theritical performance is compared with CFAR methods based on several other statistical models. Finally, the CFAR algorithms based on K, Gauss, Weibull and Log-normal are demonstrated on Remote Sensing-1 SAR imagery. The results review that the CA-CFAR detection algorithm based on K distribution not only guarantees detection rate but also generates fewer false alarms. It performs better than CFAR methods based on other statistical models.
Detection algorithms employed by practical ship detection systems are always demanded for real-time calculation. Therefore, this paper also investigates on improving detection algorithms’efficiencies. A fast detection algorithm based on two-stage conjuncted CFAR is presented. While gauss, Weibull and Log-normal are used in the first stage CFAR separately, performance of the fast algorithm is explicated. Experiment results on field SAR imagery review that, while it satisfies the real-time demand of practical systems better, the proposed fast algorithm has almost the same detection performance with the CA-CFAR detection algorithm based on K distribution.
分析海洋杂波的统计特性是基于统计模型的CFAR检测方法的基础。本文首先研究了SAR图像海洋杂波的统计特性,给出统计建模的一般步骤和拟合优度评价准则,评估了各种分布模型对遥感卫星一号SAR图像海杂波分布的拟合性能。
基于统计模型的CFAR是一种能够根据局部杂波统计特性计算检测阈值的自适应检测方法。根据背景杂波的统计特性,其检测性能可以从杂波分布模型和检测器两方面得以改进,本文首先总结了基于各种分布模型和不同检测器的CFAR检测方法,其次对基于K分布的CA-CFAR检测算法作了重点分析,并对比了几种基于统计模型CFAR方法在仿真K分布杂波环境中的理论性能。最后,针对遥感卫星一号海洋图像,将基于K分布的CA-CFAR和基于高斯、韦布尔、对数正态分布的CFAR检测算法作了对比实验。实验结果表明,基于K分布的CA-CFAR方法在保证检测率的同时有效地减少了虚警,检测性能优于其他几种基于统计模型的CFAR方法。
实际的舰船目标检测系统要求检测算法具有近实时性,本文对提高检测算法的效率也进行了研究。在分析已有快速检测算法的处理思路基础上,结合前文的工作,本文提出了一种基于两级CFAR级联的舰船目标快速检测算法,分析了第一级CFAR中分别采用高斯、韦布尔、对数正态分布时快速算法的检测性能和算法效率,并由对实测图像的实验验证了其检测性能。基于两级CFAR级联的舰船目标快速算法与基于K分布的CA-CFAR方法检测性能相当,但算法效率更能满足检测系统的实时性要求。
Recently, ship detection on Synthetic Aperture Radar (SAR) imagery is an important way of ocean surveillance and fishing monitoring. China has large ocean which is full of resources. It is of grate significance in developing ship detection researchment on SAR imagery. There are two criterions of developing researches of ship detection on SAR imagery: how to reduce the false alarms and raise detection efficiency under a certain detection rate. While the algorithms of Constant False Alarm Rate (CFAR) based on statistical models are widely used in SAR imagery ship detection, this paper mainly studies this kind of methods.
Statistical characteristic analyze is the basic of CFAR detection algorithms based on statistic models. This paper studies the statistical characteristics of SAR sea clutter, presents the setup of statistical modeling and the criterions of model fit performance, and assesses several models’modeling performances to SAR imagery of Remote Sensing-1.
The method of CFAR based on statistical model has the ability of automatic calculating detection threshold according to sea clutter’s local statistical characteristics. The detection performance can be improved from both using more accurate sea clutter statistical model and more suited detector. Therefore, the CFAR detection algorithms based on varies statistical models and detectors are summarized. After that, particular analyses are made on the CA-CFAR detection algorithm based on K distribution. Its theritical performance is compared with CFAR methods based on several other statistical models. Finally, the CFAR algorithms based on K, Gauss, Weibull and Log-normal are demonstrated on Remote Sensing-1 SAR imagery. The results review that the CA-CFAR detection algorithm based on K distribution not only guarantees detection rate but also generates fewer false alarms. It performs better than CFAR methods based on other statistical models.
Detection algorithms employed by practical ship detection systems are always demanded for real-time calculation. Therefore, this paper also investigates on improving detection algorithms’efficiencies. A fast detection algorithm based on two-stage conjuncted CFAR is presented. While gauss, Weibull and Log-normal are used in the first stage CFAR separately, performance of the fast algorithm is explicated. Experiment results on field SAR imagery review that, while it satisfies the real-time demand of practical systems better, the proposed fast algorithm has almost the same detection performance with the CA-CFAR detection algorithm based on K distribution.
引文
[1]张澄波.综合孔径雷达原理、系统分析与应用[M].北京:科学出版社, 1989.
[2]保铮,邢孟道,王彤.雷达成像技术[M].北京:电子工业出版社, 2005.
[3]张直中.机载和星载合成孔径雷达导论[M].北京:电子工业出版社, 2003.
[4] I. S. Robison. Satellite oceanography: An introduction for oceanographers and remote sensing scientists [M]. Chichester, UK: Ellis Horwood Ltd., 1985.
[5] F. T. Ulaby, R. K. Moore, A. K. Fung. Microwave remote sensing: Active and passive, volume ii: Radar remote sensing and surface scattering and emission theory [M]. Massachusetts, USA: Addison-Wesley, 1982.
[6] F. Askari, B. Zerr. Automatic approach to ship detection in spaceborne synthetic aperture radar imagery: An assessment of ship detection capability using radarsat [R], SACLANT Undersea Research Centre, La Spezia (Italy), December 2000
[7] M. Skolnik. An empiritical formula for the radar cross section of ships at grazing incidence [J], IEEE Trans on Aerospace and Electronic System, 1974. 10
[8] P. Vachon. Ship detection by the radarsat sar: Validation of detection model predictions [J], Canadian Journal of Remote Sensing, 1997. 23 (1)
[9] P. W. Vachon, R. B. Olsen. Radarsat sar mode selection for marine applications [J], Backscatter, 1995. 6 (3)
[10] N. Kourti, I. Shepherd, G. Schwartz, et al. Integrating spaceborne sar imagery into operational systems for fisheries monitoring [J], Canadian Journal of Remote Sensing, 2001. 27 (4)
[11] J. J. v. d. Sanden, P. Budkewitsch, D. Flett, et al. Applications potential of planned c-band sar satellites: Leading to radarsat-2 [C]. IEEE 2001 International Geoscience and Remote Sensing Symposium(IGARSS'01), 2001. 488-492
[12] M. Yeremy, J. W. M. Campbell, K. Mattar, et al. Ocean surveillance with polarimetric sar [J], Canadian Journal of Remote Sensing, 2001. 27 (4): 328-344
[13] C. Oliver, S. Quegan. Understanding synthetic aperture radar imagery [M]. Norwood, Massachusetts, USA: Artech House, 1998.
[14] D. J. Crisp. The state-of-the-art in ship detection in synthetic aperture radar imagery [R], Austrilian Defence Science and Technology Organisation, Edinburgh,South Austrilia, May 2004, 013-053
[15]种劲松,欧阳越,朱敏慧.合成孔径雷达海洋目标检测[M].北京:海洋出版社, 2006.
[16]张风丽,张磊,吴炳方.欧盟船舶遥感探测技术与系统研究的进展[J],遥感学报, 2007. 11 (4): 552-562
[17] P. W. Vachon, P. Adlakha, H. Edel, et al. Canadian progress toward marine and coastal applications of synthetic aperture radar [J], Johns Hopkins APL Technical Digest, 2000. 21 (1): 33-40
[18] M. D. Henschel, P. A. Hoyt, J. H. Stockhausen, et al. Vessel detection with wide area remote sensing [J], Sea Technology, 1998. 39 (9): 63-68
[19] C. C. Wackerman, K. S. Friedman, W. G. Pichel, et al. Automatic detection ofships in radarsat-1 sar imagery [J], Canadian Journal of Remote Sensing, 2001. 27 (5): 568-577
[20] W. G. Pichel, P. Clemente-Colon. Noaa coastwatch sar applications and demonstration [J], Johns Hopkins APL Technical Digest, 2000. 21 (1): 49-57
[21] N. Kourti, I. Shepard, J. Verborgh. Fishing boat detection by using sar imagery [C]. Ship Detection in Coastal Waters Three Day Workshop, 2000,
[22] G. Schwartz, M. Alvarez, A. Varfis, et al. Elimination of false positives in vessels detection and identification by remote sensing [C]. IEEE 2002 International Geoscience and Remote Sensing Symposium (IGARSS'02), 2002, 1. 116-118
[23] Qinetiq's ship detection system—maritime surveillance tool (mast) web site, http://www.Qinetiq.Com/casestudies/2002/case_study54.Html
[24] K. F. Dagestad. User manual for sartool [R], 2004
[25] K. Eldhuset. Principles and performance of an automated ship detection system for sar images [C]. IEEE 1989 International Geoscience and Remote Sensing Symposium (IGARSS'89), 1989. 358-361
[26] N. Robertson, P. Bird, C. Brownsword. Ship surveillance using radarsat scansar images [C]. Alliance for Marine Remote Sensing (AMRS) Workshop on Ship Detection in Coastal Waters, 2000,
[27] M. Cusano, J. Lichtenegger, P. Lombardo, et al. A real time operational scheme for ship traffic monitoring using quick look ers sar images [C]. IEEE 2000 International Geoscience and Remote Sensing Symposium (IGARSS' 00), 2000, 7. 2918-20
[28] P. Lombardo, M. Sciotii. Segmentation-based technique for ship detection in sar images [C]. IEE Proceedings: Radar, Sonar & Navigation, 2001, 3: 147-59
[29] M. N. Ferrara, A. Gallon, A. Torre. Improvement in automatic detection and recognition of moving targets in alenia aerospazio activity [C]. Proceedings of SPIE -EUROPTO Conference on Image and Signal Processing for Remote Sensing, 1998, 3500: 96-103
[30] I.-I. Lin, L. K. Kwoh, Y.-C. Lin, et al. Ship and ship wake detection in the ers sar imagery using computer-based algorithm [C]. IEEE 1997 International Geoscience and Remote Sensing Symposium (IGARSS' 97), 1997. 151-3
[31]张亮. Sar图像舰船目标检测方法研究[D],长沙:国防科技大学, 2007
[32] M. D. Henschel, P. A. Hoyt, J. H. Stockhausen, et al. Vessel detection with wide area remote sensing. [J], Sea
[33] Technology, 1998. 39 (9): 63-68
[34] M. T. Rey, J. Campbell, D. Petrovic. A comparison of ocean clutter distribution estimators for cfar-based ship detection in radarsat imagery [R], Defence Research Establishment Ottawa, Ottawa, 1998, 1340
[35] C. Satlantic Inc. The ocean monitoring workstation pc, version 2001.1, user manual revision a, http://www.Satlantic.Com/products/byname/omw/omw_manual.Pdf. [J], March 2001.
[36] R. Touzi, F. Charbonneau, R. K. Hawkins, et al. Ship detection and characterization using polarimetric sar [J], Canadian Journal of Remote Sensing, 2003.
[37] Http://www.Satlantic.Com
[38] K. S. Friedman, C. Wackerman, F. Funk, et al. Validation of a cfar vessel detection algorithm using known vessel locations [C]. IEEE 2001 International Geoscience and Remote Sensing Symposium (IGARSS'), 2001, 4. 1804-6
[39] K. S. Friedman, C. Wackerman, F. Funk, et al. Validation of an automatic vessel detection algorithm using sar data and known vessel fleet distributions [C]. IEEE 2000 International Geoscience and Remote Sensing Symposium (IGARSS'), 2000, 5. 2071-3
[40] Joint research centre (jrc) of the european community—impast and declims web site, http://intelligence.Jrc.Cec.Eu.Int/marine/fish/index.Htm.
[41] N. Kourti, I. Shepherd, G. Schwartz, et al. Integrating spaceborne sar imagery into operational systems for fisheries monitoring [J], Canadian Journal of Remote Sensing, 2001. 27 (4): 291-305
[42] G. Schwartz, M. Alvarez, A. Varfis, et al. Elimination of false positives in vessels detection and identification by remote sensing [C]. IEEE 2002 International Geoscience and Remote Sensing Symposium (IGARSS' 02), 2002, 1. 116-18
[43] H. Greidanus, N. Kourti. Declimswps status and plans [R], The Fifth Meeting of the DECLIMS Project, Farnborough,UK, 2005
[44] T. Wahl, K. Eldhuset, K. Sk?elv. Ship traffic monitoring using the ers-1 sar [C]. Proceedings First ERS-1 Symposium - Space at the Service of our Environment, Cannes, France, 1993: 823-828
[45] T. Wahl, K. Eldhuset, K. Aksnes. Sar detection of ships and ship wakes [C]. Proceedings of the SAR Applications Workshop, Frascati, Italy, 1986: 61-65
[46] R. B. Olsen, T. Wahl. The role of wide swath sar in high-latitude coastal management [J], Johns Hopkins APL Technical Digest, 2000. 21 (1): 136-140
[47] P. W. Vachon, R. B. Olsen. Ship detection with satellite-based sensors [J], Backscatter, 2000. 23-26
[48] M. Sciotti, P. Lombardo. Ship detection in sar images: A segmentation-based approach [C]. Proceedings of the 2001 IEEE Radar Conference (RADAR' 01), 2001: 81-6
[49] M. Sciotti, D. Pastina, P. Lombardo. Exploiting the polarimetric information for the detection of ship targets in non-homogeneous sar images [C]. IEEE 2002 International Geoscience and Remote Sensing Symposium (IGARSS' 02), 2002, 3. 1911-13
[50] M. Sciotti, D. Pastina, P. Lombardo. Polarimetric detectors of extended targets for ship detection in sar images [C]. IEEE 2001 International Geoscience and Remote Sensing Symposium (IGARSS' 01), 2001, 7. 3132-4
[51] M. N. Ferrara, A. Torre. Automatic moving targets detection using a rule-based system: Comparison between different study cases [C]. IEEE 1998 International Gescience and Remote Sensing Symposium (IGARSS' 98), 1998, 3. 1593–1595
[52] M. N. Ferrara, A. Torre. Alenia aerospazio activity on sar data analysis and information extraction [C]. Proceedings of SPIE, 1997, 3217: 67-75
[53] I.-I. Lin, V. Khoo. Computer-based algorithm for ship detection from ers-xsar imagery [C]. Proceedings of the 3rd ERS Scientific Symposium, Florence, Italy, 1997: 17-21
[54] I.-I. Lin, L. K. Kwoh, Y.-C. Lin, et al. Ship and ship wake detection in the ers sar imagery using computer-based algorithm [C]. IEEE 1997 InternationalGeoscience and Remote Sensing Symposium (IGARSS’97), 1997. 151-153
[55] K. Ouchi, S. I. Hwang, H. P. Wang, et al. Ability of detecting small fishing boats by alos-palsar based on cfar and multi-look cross-correlation techniques [J], 2007.
[56] H. Greidanus, G. Lemoine. Ship surveillance with terrasar-x scansar [R], Institute for the Protection and Security of the Citizen, 2007
[57] M. V. d. S. Sim?es. Ship detection performance predictions for next generation spaceborne synthetic [D],Monterey, California: NAVAL POSTGRADUATE SCHOOL, 2001
[58]闵莉,黄莎白,史泽林, et al.一种基于形状约束势能的主动轮廓跟踪算法[J],模式识别与人工智能, 2006. 19 (2): 161-166
[59] A. Gelas, O. Bernard, D. Friboulet, et al. Compactly supported radial basis functions based collocation method for level-set evolution in image segmentation [J], IEEE TRANSACTIONS ON IMAGE PROCESSING, 2007. 16 (7): 1873-1887
[60] K. Eldhuset. An automatic ship and ship wake detection system for spaceborne sar images in coastal regions [J], IEEE Transactions on Geoscience & Remote Sensing, 1996. 34 (4)
[61] A. C. Copeland. Localized radon transform-based detection of ship wakes in sar images [J], IEEE Transactions on Geoscience & Remote Sensing, 1995. 33 (1): 35-45
[62]邹焕新,郑键,周石琳, et al.一种sar海洋图像舰船尾迹检测与判别方法[J],信号处理, 2006.
[63] J. J. v. d. Sanden, S. G. Ross. Applications potential of radarsat-2: A preview [R], 2001
[64] G. Schwartz, M. Alvarez, A. Varfis, et al. Elimination of false positives in vessels detection and identification by remote sensing [C]. IEEE 2002 International Geoscience and Remote Sensing Symposium (IGARSS' 02), 2002. 116-118
[65]匡纲要,高贵,蒋咏梅, et al.合成孔径雷达目标检测理论、算法及应用[M].长沙:国防科学技术大学出版社, 2007.
[66] M. Brizi, P. Lombardo, D. Pastina. Exploiting the shadow information to increase the target detection performance in sar images. [C]. International Conference on Radar Systems, RADAR' 99,
[67] P. Lombardo, M. Sciotti, L. M. Kaplan. Sar prescreening using both target and shadow information [C]. IEEE National Radar Conference - Proceedings, 2001: 147-152
[68] L. Gagnon, H. Oppenheim, P. Valin. R&d activities in airborne sar image processing/analysis at lockheed martin canada [C]. Proceeding of SPIE, 3491: 998-1003
[69] T. Marivi, L.-M. Carlos, J. M. Jordi, et al. Automatic detection of spots and extraction of frontiers in sar images by means of the wavelet transform: Application to ship and coastline detection [J], IEEE, 2006.
[70] J. K. Hsiao. On the optimisation of mti clutter rejection [J], IEEE Transactions on Aerospace and Electronic Systems, 1974. 10 (5): 622-629
[71] L. M. Novak, M. C. Burl. Optimal speckle reduction in polarimetric sar imagery [J], IEEE Transactions on Aerospace and Electronic Systems, 1990. 26(2): 293-305
[72] S. R. Cloude, E. Pottier. An entropy based classification scheme for land applications of polarimetric sar [J], IEEE Transactions on Geoscience and Remote Sensing, 1997. 35: 68-78
[73] S. R. Cloude, E. Pottier. A review of target decomposition theorems in radar polarimetry [J], IEEE Transactions on Geoscience and Remote Sensing, 1996. 34: 498-518
[74] W. L. Cameron, N. N. Youssef, L. K. Leung. Simulated polarimetric signatures of primitive geometrical shapes [J], IEEE Transactions on Geoscience and Remote Sensing, 1996. 34: 793-803
[75] R. Touzi, F. Charbonneau. Characterization of target symmetric scattering using polarimetric sars [J], IEEE Transactions on Geoscience and Remote Sensing, 2002. 40: 2507-2516
[76] R. Touzi, F. Charbonneau, R. K. Hawkins, et al. Ship-sea contrast optimisation when using polarimetric sars [C]. IEEE 2001 International Geoscience and Remote Sensing Symposium (IGARSS' 01), 2001, 1. 426-428
[77] R. Touzi. On the use of polarimetric sar data for ship detection [C]. IEEE 1999 International Geoscience and Remote Sensing Symposium (IGARSS' 99), 1999, 2. 812-814
[78] W. L. Cameron, L. K. Leung. Feature motivated polarization scattering matrix decomposition [C]. Proceedings of the IEEE 1990 International Radar Conference (Radar' 90), 1990: 549-557
[79] J. W. M. Campbell, A. L. Gray, K. E. Mattar, et al. Ocean surface feature detection with the ccrs along-track insar [J], Canadian Journal of Remote Sensing, 1997. 23 (1): 24-37
[80] K. Schulz, U. Soergel, U. Thoennessen, et al. Elimination of across-track phase components in airborne along-track interferometry data to improve object velocity measurements [C]. IEEE 2003 International Geoscience and Remote Sensing Symposium (IGARSS' 03), 2003,
[81] K. Schulz, U. Soergel, U. Thoennessen. Segmentation of moving objects in sar-mti data [C]. Proceedings of SPIE - Algorithms for Synthetic Aperture Radar Imagery VIII,, 2001, 4382: 174-181
[82] M. Iehara, K. Ouchi, I. Takami, et al. Detection of ships using cross-correlation of split-look sar images [C]. IEEE 2001 International Geoscience and Remote Sensing Symposium (IGARSS' 01), 2001, 4. 1807-1809
[83] K. Ouchi, H. Yaguchi. Simulation on the extraction of ships' images embedded in speckle using cross-correlation of multilook sar images and applications to radarsat data. [C]. IEEE 2002 International Geoscience and Remote Sensing Symposium (IGARSS’02), 2002, 4. 2498-2500
[84] K. Ouchi, S. Tamaki, H. Yaguchi, et al. Ship detection based on coherence images derived from cross correlation of multilook sar images [J], IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2004. 1 (3): 184-187
[85] G. Benelli, A. Garzelli, A. Mecocci. Complete processing system that uses fuzzy logic for ship detection in sar images [J], IEE Proceedings: Radar, Sonar & Navigation, 1994. 141 (4): 181-186
[86] F. Argenti, G. Benelli, A. Garzelli, et al. Automatic ship detection in sar images [C]. IEE International Conference Radar 92, 1992. 465-468
[87] H. Osman, S. D. Blostein. Probabilistic winner-take-all segmentation of imageswith application to ship detection [J], IEEE Transactions on Systems, Man, & Cybernetics, Part B: Cybernetics, 2000. 30 (3): 485-490
[88] H. Arseoault, G. April. Properties of speckle integrated with finite aperture and logarithmically transformed [J], J.Opt.Soc.Am., 1976. 66 (11): 1160-1163
[89] K. D. Ward. Compound representation of high resolution sea clutter [J], Electron Lett., 1981. (7): 561-565
[90] A. C. Frery. A model for extremely heterogeneous clutter [J], IEEE Trans. GRS, 1997. 35 (3): 648-659
[91] A. Farina, A. Russo, F. Scannapieco, et al. Theory of radar detection in coherent weibull clutter [J], IEE Proc., 1987. 134 (2)
[92] I. Antipov. Analysis of sea clutter data [R], Defence Science and Technology Organization, Canberra, 1998, 0647
[93] H. C. Chan. Radar sea clutter at low grazing angles [J], IEE Proc. Radar and Signal Processing, 1990. 137 (2): 102-112
[94]胡睿,孙进平,王文光.基于alpha稳态分布的sar图像目标检测算法[J],中国图象图形学报, 2009. 14 (1): 25-29
[95] A. Farina, A. Russo, F. A. Studer. Coherent radar detection in log-normal clutter [J], IEE Proc., 1986. 133 (Pt. F, No 1): 39-54
[96] M. V. Menon. Estimation of the shape and scale parameters of the weibull distribution [J], Technometrics, 1963. 15: 175-182
[97] R. Ravid, N. Levanon. Maximum-likelihood cfar for weibull background [J], IEE Proc., June 1992. 139 (3): 256-264
[98] R. Rifkin. Analysis of cfar performance in weibull clutter [J], IEEE Trans. AES, Apr. 1994. 30 (2): 315-328
[99] S. F. George. The detection of nonfluctuating targets in log-normal clutter [R], NRL Report, 1968
[100] A. C. Frery. Alternative distributions for the multiplication model in sar images [C]. Int. Geosci. Remote Sensing Symp., Florence, Italy, 1995, 1. 169-171
[101] C. L. Martinez. Polarimetric sar speckle noise model [J], IEEE Trans. GRS, 2003. 41 (10): 2232-2242
[102]高贵. Sar图像目标roi自动获取技术研究国防科技大学, 2007
[103] R. S. Raghavan. A method for estimating parameters of k-distributed clutter [J], IEEE Trans. AES, 1991. 27 (2): 238-246
[104] S. Watts. A practical approach to the prediction and assessment of radar performance in sea clutter [C]. IEEE International Radar Conference, 1995. 181- 186
[105] S. Watts. Radar detection prediction in k-distributed sea clutter and thermal noise [J], IEEE Trans. AES, 1987. 23 (1): 40-45
[106] S. Watts. Radar detection prediction in sea clutter using the compound k distribution model [J], IEE Proc., 1985. 132 (2)
[107] K. D. Ward. A radar sea clutter model and its application to performance assessment [J], Radar-82: 203-207
[108] E. Conte, M. Longo. On a coherent model for log-normal clutter [J], 1987. 134 (2): 198-201
[109]扈罗全,林乐科,朱洪波.三种重拖尾分布海杂波的比较与分析[J],电波科学学报, 2007. 22 (6)
[110] D. Blacknell, J. A. Tough. Parameter estimation for the k-distribution based onzlog(z) [J], IEE Proc.-F, 2001. 148 (6): 309-312
[111] S. S. Rappaport, L. Kurz. An optimal nonlinear detectorfor digital data transmission through non-gaussian channels [J], IEEE Trans. Comm. Techn., 1966. COM-14: 266-274
[112] J. H. Miller, J. B. Thomas. Detectors for discrete-time signals in non-gaussian noise [J], IEEE Trans. Inform. Theory, 1972. IT-18: 241-250
[113] ------. Performance of optimal and suboptimal receivers in the presence of impulsive noise modeled as an alpha-stable process [J], IEEE Trans. Commun., 1995. 43: 904-914
[114] ------. On the detection of stochastic impulsive transients over back-ground noise [J], Signal Processing, 1995. 41: 175-190
[115] C. L. Nikias, M. Shao. Signal processing with alpha-stable distributions and applications [M]. New York: Wiley, 1995.
[116] J. G. Fleischaman, S. Ayasli, E. M. Adams, et al. Foliage penetration experiment: Part i:Foliage attenuation and backscatter analysis of sar imagery [J], IEEE Trans on Aerospace and Electronic System, 1996. 32 (1): 134-144
[117] ------. On symmetric stable models for impulsive noise [J], USC-SIPI, 1993. 231
[118] P. Levy. Calcul des probabilites [M]. Paris: Gauthier-Villards, 1925.
[119] R. Kapoor, A. Banerjee, G. A. Tsihrintzis, et al. Uwb radar detection of targets in foliage using alpha-stable clutter models [J], IEEE Trans on Aerospace and Electronic Systems, 1999. 35 (3): 819-834
[120] G. A. Tsihritzis, M. Shao, C. L. Nikias. Recent results in application and processing of alpha-stable distributed time series [J], Journal of the Franklin Institute, 1996. 333B: 467-497
[121] M. Shao, C. L. Nikias. Signal processing with fractional lower-order moments:Stable processes and their applications [C]. Proceedings of the IEEE, 1993, 81: 986-1010
[122] M. T. Rey, A. Drosopoulos, D. Petrovic. A search procedure for ships in radar-sat imagery [R], Defence Research Establishment Ottawa, 1996
[123] I. R. Joughin, D. B. Percival, D. P. Winebrenner. Maximum likelihood estimation of k-distribution parameters for sar data [J], IEEE Trans. Geosci. Remote Sensing, 1993. 31: 989-999
[124] J. Zhu, Y. Su. A new parameter estimation for k distribution clutter [C]. 9th International Conference on Signal Processing, Beijing, 2008, 2. 1103-1107
[125]郝程鹏,侯朝焕,鄢锦.一种新的k分布形状参数估计器[J],电子与信息学报, 2005. 27 (9): 1404-1407
[126] D.Blacknell. Comparison of parameter estimators for k-distribution [J], IEE Proc. Radar, Sonar, Navigation, 1994. 141 (1): 45-52
[127] D. R. Ikkander, A. M. Zoubir. Estimating the parameters of k distribution using higher order and factional moments [J], IEEE Trans on Aerospace and Electronic System, 1999. 35 (4)
[128] W. DuMouchel. Stable distributions in statistical interference [D],New Haven: Yale, 1971
[129] E. Fama, R. Roll. Parameter estimation for symmetric stable distributions [J], Journal of American Statistical Association, 1971. 66: 331-338
[130] X. Ma, C. L. Nikias. On blind channel identification for impulsive signal environments [C]. In Proceedings of the International Conference onAcoustics,Speech,and Signal Processing(ICASSP' 95), Detroit,MI, 1995,
[131] I. Koutrouvelis. Regression-type estimation of the parameters of stable laws [J], Journal of the American Statistical Association, 1980. 66: 918-928
[132] G. A. Tsihrintzis, C. L. .Nikias. Fast estimation of the parameters of alpha-stable impulsive interference [J], IEEE Transactions on Signal Processing, 1996. 44 (6): 1492-1503
[133]陈高波.对称alpha稳定分布的最小绝对偏差参数估计[J],统计与决策, 2008. (10): 162-163
[134] L. B. Christopher, M. Z. Abdelhak. On the estimation of the parameters of alpha-stable distributions using linear regression in the characteristic function domain [J], IEEE, 1988.
[135] G. Moser. Sar amplitude probability density function edtimation based on generalized gaussian scattering model [C]. SPIE, 2004, 5573. 307-318
[136]邹焕新. Sar图像舰船目标与航迹检测方法研究[D],长沙:国防科学技术大学, 2003
[137]张琦.基于统计模型的高分辨sar图像车辆目标检测方法[D],长沙:国防科技大学, 2005
[138] Y. Li, K. Ji, Y. Su. Quantitative analysis of statistical models for typical terrains in sar data [C]. Proceedings of 2006 CIE International Conference on Radar, 1: 696-699
[139] J. B. Billingsley, A. Farina. Statistical analyses of measured radar ground clutter data [J], IEEE Transactions on Aerospace and Electronic Systems, 1999. 35 (2): 579-593
[140] D. D. Michael, A. O. S. Joseph. Quantitative statistical assessment of conditional models for synthetic aperture radar, [J], IEEE Transactions On Image Processing, 13: 113-125
[141] D. D. Michael. Statistical assessment of model fit for synthetic aperture radar data [C]. SPIE, 2001, 4382. 379-388
[142] R. B. D'Agostino. A suggestion for using powerful and informative tests of normality [J], The American Statistician, 1990. 44 (4): 316-321
[143]方学立. Uwb-sar图像中的目标检测与鉴别[D],长沙:国防科技大学, 2005
[144] J. P. Nolan. Http://academic2.American.Edu/~jpnolan
[145] J. P. Nolan. Numberical calculation of stable densities and distribution functions [J], Communications in Statistics-Stochastic Models, 1997. 13: 759-774
[146]郝鹏程,刘斌,闫晟, et al.基于有序统计量和自动删除平均的最大选择恒虚警检测器[J],信号处理, 2008. 24 (4): 578-581
[147] S. Erfanian, V. T. Vakili. Introducing excision switching-cfar in k distributed sea clutter [J], Signal Processing, 2009. 89 (6): 1023-1031
[148]何友,关键,彭应宁, et al.雷达自动检测与恒虚警处理[M].北京:清华大学出版社, 1999.
[149] L. M. Novak. The automatic target-recognition system in saip [J], The Lincoln Laboratory Journal, 1997. 10 (2): 187-202
[150] J. Ritcey. An order-statistics-based cfar for sar applications [D],Seattle: University of Washington, 1990
[151] M. E. Smith, P. K. Varshney. Vi-cfar: A novel cfar algorithm based on data variability [C]. IEEE national Radar Conference, 1997. 263-268
[152] M. E. Smith, P. K. Varshney. Intelligent cfar processor based on data variability [J], IEEE Trans on Aerospace and Electronic System, 2000. 36 (3): 837-847
[153] P. W. Vachon. Validation of ship detection by the radarsat synthetic aperture radar and the ocean monitoring workstation [J], Canadian Journal of Remote Sensing, 2000. 26 (3): 200-212
[2]保铮,邢孟道,王彤.雷达成像技术[M].北京:电子工业出版社, 2005.
[3]张直中.机载和星载合成孔径雷达导论[M].北京:电子工业出版社, 2003.
[4] I. S. Robison. Satellite oceanography: An introduction for oceanographers and remote sensing scientists [M]. Chichester, UK: Ellis Horwood Ltd., 1985.
[5] F. T. Ulaby, R. K. Moore, A. K. Fung. Microwave remote sensing: Active and passive, volume ii: Radar remote sensing and surface scattering and emission theory [M]. Massachusetts, USA: Addison-Wesley, 1982.
[6] F. Askari, B. Zerr. Automatic approach to ship detection in spaceborne synthetic aperture radar imagery: An assessment of ship detection capability using radarsat [R], SACLANT Undersea Research Centre, La Spezia (Italy), December 2000
[7] M. Skolnik. An empiritical formula for the radar cross section of ships at grazing incidence [J], IEEE Trans on Aerospace and Electronic System, 1974. 10
[8] P. Vachon. Ship detection by the radarsat sar: Validation of detection model predictions [J], Canadian Journal of Remote Sensing, 1997. 23 (1)
[9] P. W. Vachon, R. B. Olsen. Radarsat sar mode selection for marine applications [J], Backscatter, 1995. 6 (3)
[10] N. Kourti, I. Shepherd, G. Schwartz, et al. Integrating spaceborne sar imagery into operational systems for fisheries monitoring [J], Canadian Journal of Remote Sensing, 2001. 27 (4)
[11] J. J. v. d. Sanden, P. Budkewitsch, D. Flett, et al. Applications potential of planned c-band sar satellites: Leading to radarsat-2 [C]. IEEE 2001 International Geoscience and Remote Sensing Symposium(IGARSS'01), 2001. 488-492
[12] M. Yeremy, J. W. M. Campbell, K. Mattar, et al. Ocean surveillance with polarimetric sar [J], Canadian Journal of Remote Sensing, 2001. 27 (4): 328-344
[13] C. Oliver, S. Quegan. Understanding synthetic aperture radar imagery [M]. Norwood, Massachusetts, USA: Artech House, 1998.
[14] D. J. Crisp. The state-of-the-art in ship detection in synthetic aperture radar imagery [R], Austrilian Defence Science and Technology Organisation, Edinburgh,South Austrilia, May 2004, 013-053
[15]种劲松,欧阳越,朱敏慧.合成孔径雷达海洋目标检测[M].北京:海洋出版社, 2006.
[16]张风丽,张磊,吴炳方.欧盟船舶遥感探测技术与系统研究的进展[J],遥感学报, 2007. 11 (4): 552-562
[17] P. W. Vachon, P. Adlakha, H. Edel, et al. Canadian progress toward marine and coastal applications of synthetic aperture radar [J], Johns Hopkins APL Technical Digest, 2000. 21 (1): 33-40
[18] M. D. Henschel, P. A. Hoyt, J. H. Stockhausen, et al. Vessel detection with wide area remote sensing [J], Sea Technology, 1998. 39 (9): 63-68
[19] C. C. Wackerman, K. S. Friedman, W. G. Pichel, et al. Automatic detection ofships in radarsat-1 sar imagery [J], Canadian Journal of Remote Sensing, 2001. 27 (5): 568-577
[20] W. G. Pichel, P. Clemente-Colon. Noaa coastwatch sar applications and demonstration [J], Johns Hopkins APL Technical Digest, 2000. 21 (1): 49-57
[21] N. Kourti, I. Shepard, J. Verborgh. Fishing boat detection by using sar imagery [C]. Ship Detection in Coastal Waters Three Day Workshop, 2000,
[22] G. Schwartz, M. Alvarez, A. Varfis, et al. Elimination of false positives in vessels detection and identification by remote sensing [C]. IEEE 2002 International Geoscience and Remote Sensing Symposium (IGARSS'02), 2002, 1. 116-118
[23] Qinetiq's ship detection system—maritime surveillance tool (mast) web site, http://www.Qinetiq.Com/casestudies/2002/case_study54.Html
[24] K. F. Dagestad. User manual for sartool [R], 2004
[25] K. Eldhuset. Principles and performance of an automated ship detection system for sar images [C]. IEEE 1989 International Geoscience and Remote Sensing Symposium (IGARSS'89), 1989. 358-361
[26] N. Robertson, P. Bird, C. Brownsword. Ship surveillance using radarsat scansar images [C]. Alliance for Marine Remote Sensing (AMRS) Workshop on Ship Detection in Coastal Waters, 2000,
[27] M. Cusano, J. Lichtenegger, P. Lombardo, et al. A real time operational scheme for ship traffic monitoring using quick look ers sar images [C]. IEEE 2000 International Geoscience and Remote Sensing Symposium (IGARSS' 00), 2000, 7. 2918-20
[28] P. Lombardo, M. Sciotii. Segmentation-based technique for ship detection in sar images [C]. IEE Proceedings: Radar, Sonar & Navigation, 2001, 3: 147-59
[29] M. N. Ferrara, A. Gallon, A. Torre. Improvement in automatic detection and recognition of moving targets in alenia aerospazio activity [C]. Proceedings of SPIE -EUROPTO Conference on Image and Signal Processing for Remote Sensing, 1998, 3500: 96-103
[30] I.-I. Lin, L. K. Kwoh, Y.-C. Lin, et al. Ship and ship wake detection in the ers sar imagery using computer-based algorithm [C]. IEEE 1997 International Geoscience and Remote Sensing Symposium (IGARSS' 97), 1997. 151-3
[31]张亮. Sar图像舰船目标检测方法研究[D],长沙:国防科技大学, 2007
[32] M. D. Henschel, P. A. Hoyt, J. H. Stockhausen, et al. Vessel detection with wide area remote sensing. [J], Sea
[33] Technology, 1998. 39 (9): 63-68
[34] M. T. Rey, J. Campbell, D. Petrovic. A comparison of ocean clutter distribution estimators for cfar-based ship detection in radarsat imagery [R], Defence Research Establishment Ottawa, Ottawa, 1998, 1340
[35] C. Satlantic Inc. The ocean monitoring workstation pc, version 2001.1, user manual revision a, http://www.Satlantic.Com/products/byname/omw/omw_manual.Pdf. [J], March 2001.
[36] R. Touzi, F. Charbonneau, R. K. Hawkins, et al. Ship detection and characterization using polarimetric sar [J], Canadian Journal of Remote Sensing, 2003.
[37] Http://www.Satlantic.Com
[38] K. S. Friedman, C. Wackerman, F. Funk, et al. Validation of a cfar vessel detection algorithm using known vessel locations [C]. IEEE 2001 International Geoscience and Remote Sensing Symposium (IGARSS'), 2001, 4. 1804-6
[39] K. S. Friedman, C. Wackerman, F. Funk, et al. Validation of an automatic vessel detection algorithm using sar data and known vessel fleet distributions [C]. IEEE 2000 International Geoscience and Remote Sensing Symposium (IGARSS'), 2000, 5. 2071-3
[40] Joint research centre (jrc) of the european community—impast and declims web site, http://intelligence.Jrc.Cec.Eu.Int/marine/fish/index.Htm.
[41] N. Kourti, I. Shepherd, G. Schwartz, et al. Integrating spaceborne sar imagery into operational systems for fisheries monitoring [J], Canadian Journal of Remote Sensing, 2001. 27 (4): 291-305
[42] G. Schwartz, M. Alvarez, A. Varfis, et al. Elimination of false positives in vessels detection and identification by remote sensing [C]. IEEE 2002 International Geoscience and Remote Sensing Symposium (IGARSS' 02), 2002, 1. 116-18
[43] H. Greidanus, N. Kourti. Declimswps status and plans [R], The Fifth Meeting of the DECLIMS Project, Farnborough,UK, 2005
[44] T. Wahl, K. Eldhuset, K. Sk?elv. Ship traffic monitoring using the ers-1 sar [C]. Proceedings First ERS-1 Symposium - Space at the Service of our Environment, Cannes, France, 1993: 823-828
[45] T. Wahl, K. Eldhuset, K. Aksnes. Sar detection of ships and ship wakes [C]. Proceedings of the SAR Applications Workshop, Frascati, Italy, 1986: 61-65
[46] R. B. Olsen, T. Wahl. The role of wide swath sar in high-latitude coastal management [J], Johns Hopkins APL Technical Digest, 2000. 21 (1): 136-140
[47] P. W. Vachon, R. B. Olsen. Ship detection with satellite-based sensors [J], Backscatter, 2000. 23-26
[48] M. Sciotti, P. Lombardo. Ship detection in sar images: A segmentation-based approach [C]. Proceedings of the 2001 IEEE Radar Conference (RADAR' 01), 2001: 81-6
[49] M. Sciotti, D. Pastina, P. Lombardo. Exploiting the polarimetric information for the detection of ship targets in non-homogeneous sar images [C]. IEEE 2002 International Geoscience and Remote Sensing Symposium (IGARSS' 02), 2002, 3. 1911-13
[50] M. Sciotti, D. Pastina, P. Lombardo. Polarimetric detectors of extended targets for ship detection in sar images [C]. IEEE 2001 International Geoscience and Remote Sensing Symposium (IGARSS' 01), 2001, 7. 3132-4
[51] M. N. Ferrara, A. Torre. Automatic moving targets detection using a rule-based system: Comparison between different study cases [C]. IEEE 1998 International Gescience and Remote Sensing Symposium (IGARSS' 98), 1998, 3. 1593–1595
[52] M. N. Ferrara, A. Torre. Alenia aerospazio activity on sar data analysis and information extraction [C]. Proceedings of SPIE, 1997, 3217: 67-75
[53] I.-I. Lin, V. Khoo. Computer-based algorithm for ship detection from ers-xsar imagery [C]. Proceedings of the 3rd ERS Scientific Symposium, Florence, Italy, 1997: 17-21
[54] I.-I. Lin, L. K. Kwoh, Y.-C. Lin, et al. Ship and ship wake detection in the ers sar imagery using computer-based algorithm [C]. IEEE 1997 InternationalGeoscience and Remote Sensing Symposium (IGARSS’97), 1997. 151-153
[55] K. Ouchi, S. I. Hwang, H. P. Wang, et al. Ability of detecting small fishing boats by alos-palsar based on cfar and multi-look cross-correlation techniques [J], 2007.
[56] H. Greidanus, G. Lemoine. Ship surveillance with terrasar-x scansar [R], Institute for the Protection and Security of the Citizen, 2007
[57] M. V. d. S. Sim?es. Ship detection performance predictions for next generation spaceborne synthetic [D],Monterey, California: NAVAL POSTGRADUATE SCHOOL, 2001
[58]闵莉,黄莎白,史泽林, et al.一种基于形状约束势能的主动轮廓跟踪算法[J],模式识别与人工智能, 2006. 19 (2): 161-166
[59] A. Gelas, O. Bernard, D. Friboulet, et al. Compactly supported radial basis functions based collocation method for level-set evolution in image segmentation [J], IEEE TRANSACTIONS ON IMAGE PROCESSING, 2007. 16 (7): 1873-1887
[60] K. Eldhuset. An automatic ship and ship wake detection system for spaceborne sar images in coastal regions [J], IEEE Transactions on Geoscience & Remote Sensing, 1996. 34 (4)
[61] A. C. Copeland. Localized radon transform-based detection of ship wakes in sar images [J], IEEE Transactions on Geoscience & Remote Sensing, 1995. 33 (1): 35-45
[62]邹焕新,郑键,周石琳, et al.一种sar海洋图像舰船尾迹检测与判别方法[J],信号处理, 2006.
[63] J. J. v. d. Sanden, S. G. Ross. Applications potential of radarsat-2: A preview [R], 2001
[64] G. Schwartz, M. Alvarez, A. Varfis, et al. Elimination of false positives in vessels detection and identification by remote sensing [C]. IEEE 2002 International Geoscience and Remote Sensing Symposium (IGARSS' 02), 2002. 116-118
[65]匡纲要,高贵,蒋咏梅, et al.合成孔径雷达目标检测理论、算法及应用[M].长沙:国防科学技术大学出版社, 2007.
[66] M. Brizi, P. Lombardo, D. Pastina. Exploiting the shadow information to increase the target detection performance in sar images. [C]. International Conference on Radar Systems, RADAR' 99,
[67] P. Lombardo, M. Sciotti, L. M. Kaplan. Sar prescreening using both target and shadow information [C]. IEEE National Radar Conference - Proceedings, 2001: 147-152
[68] L. Gagnon, H. Oppenheim, P. Valin. R&d activities in airborne sar image processing/analysis at lockheed martin canada [C]. Proceeding of SPIE, 3491: 998-1003
[69] T. Marivi, L.-M. Carlos, J. M. Jordi, et al. Automatic detection of spots and extraction of frontiers in sar images by means of the wavelet transform: Application to ship and coastline detection [J], IEEE, 2006.
[70] J. K. Hsiao. On the optimisation of mti clutter rejection [J], IEEE Transactions on Aerospace and Electronic Systems, 1974. 10 (5): 622-629
[71] L. M. Novak, M. C. Burl. Optimal speckle reduction in polarimetric sar imagery [J], IEEE Transactions on Aerospace and Electronic Systems, 1990. 26(2): 293-305
[72] S. R. Cloude, E. Pottier. An entropy based classification scheme for land applications of polarimetric sar [J], IEEE Transactions on Geoscience and Remote Sensing, 1997. 35: 68-78
[73] S. R. Cloude, E. Pottier. A review of target decomposition theorems in radar polarimetry [J], IEEE Transactions on Geoscience and Remote Sensing, 1996. 34: 498-518
[74] W. L. Cameron, N. N. Youssef, L. K. Leung. Simulated polarimetric signatures of primitive geometrical shapes [J], IEEE Transactions on Geoscience and Remote Sensing, 1996. 34: 793-803
[75] R. Touzi, F. Charbonneau. Characterization of target symmetric scattering using polarimetric sars [J], IEEE Transactions on Geoscience and Remote Sensing, 2002. 40: 2507-2516
[76] R. Touzi, F. Charbonneau, R. K. Hawkins, et al. Ship-sea contrast optimisation when using polarimetric sars [C]. IEEE 2001 International Geoscience and Remote Sensing Symposium (IGARSS' 01), 2001, 1. 426-428
[77] R. Touzi. On the use of polarimetric sar data for ship detection [C]. IEEE 1999 International Geoscience and Remote Sensing Symposium (IGARSS' 99), 1999, 2. 812-814
[78] W. L. Cameron, L. K. Leung. Feature motivated polarization scattering matrix decomposition [C]. Proceedings of the IEEE 1990 International Radar Conference (Radar' 90), 1990: 549-557
[79] J. W. M. Campbell, A. L. Gray, K. E. Mattar, et al. Ocean surface feature detection with the ccrs along-track insar [J], Canadian Journal of Remote Sensing, 1997. 23 (1): 24-37
[80] K. Schulz, U. Soergel, U. Thoennessen, et al. Elimination of across-track phase components in airborne along-track interferometry data to improve object velocity measurements [C]. IEEE 2003 International Geoscience and Remote Sensing Symposium (IGARSS' 03), 2003,
[81] K. Schulz, U. Soergel, U. Thoennessen. Segmentation of moving objects in sar-mti data [C]. Proceedings of SPIE - Algorithms for Synthetic Aperture Radar Imagery VIII,, 2001, 4382: 174-181
[82] M. Iehara, K. Ouchi, I. Takami, et al. Detection of ships using cross-correlation of split-look sar images [C]. IEEE 2001 International Geoscience and Remote Sensing Symposium (IGARSS' 01), 2001, 4. 1807-1809
[83] K. Ouchi, H. Yaguchi. Simulation on the extraction of ships' images embedded in speckle using cross-correlation of multilook sar images and applications to radarsat data. [C]. IEEE 2002 International Geoscience and Remote Sensing Symposium (IGARSS’02), 2002, 4. 2498-2500
[84] K. Ouchi, S. Tamaki, H. Yaguchi, et al. Ship detection based on coherence images derived from cross correlation of multilook sar images [J], IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2004. 1 (3): 184-187
[85] G. Benelli, A. Garzelli, A. Mecocci. Complete processing system that uses fuzzy logic for ship detection in sar images [J], IEE Proceedings: Radar, Sonar & Navigation, 1994. 141 (4): 181-186
[86] F. Argenti, G. Benelli, A. Garzelli, et al. Automatic ship detection in sar images [C]. IEE International Conference Radar 92, 1992. 465-468
[87] H. Osman, S. D. Blostein. Probabilistic winner-take-all segmentation of imageswith application to ship detection [J], IEEE Transactions on Systems, Man, & Cybernetics, Part B: Cybernetics, 2000. 30 (3): 485-490
[88] H. Arseoault, G. April. Properties of speckle integrated with finite aperture and logarithmically transformed [J], J.Opt.Soc.Am., 1976. 66 (11): 1160-1163
[89] K. D. Ward. Compound representation of high resolution sea clutter [J], Electron Lett., 1981. (7): 561-565
[90] A. C. Frery. A model for extremely heterogeneous clutter [J], IEEE Trans. GRS, 1997. 35 (3): 648-659
[91] A. Farina, A. Russo, F. Scannapieco, et al. Theory of radar detection in coherent weibull clutter [J], IEE Proc., 1987. 134 (2)
[92] I. Antipov. Analysis of sea clutter data [R], Defence Science and Technology Organization, Canberra, 1998, 0647
[93] H. C. Chan. Radar sea clutter at low grazing angles [J], IEE Proc. Radar and Signal Processing, 1990. 137 (2): 102-112
[94]胡睿,孙进平,王文光.基于alpha稳态分布的sar图像目标检测算法[J],中国图象图形学报, 2009. 14 (1): 25-29
[95] A. Farina, A. Russo, F. A. Studer. Coherent radar detection in log-normal clutter [J], IEE Proc., 1986. 133 (Pt. F, No 1): 39-54
[96] M. V. Menon. Estimation of the shape and scale parameters of the weibull distribution [J], Technometrics, 1963. 15: 175-182
[97] R. Ravid, N. Levanon. Maximum-likelihood cfar for weibull background [J], IEE Proc., June 1992. 139 (3): 256-264
[98] R. Rifkin. Analysis of cfar performance in weibull clutter [J], IEEE Trans. AES, Apr. 1994. 30 (2): 315-328
[99] S. F. George. The detection of nonfluctuating targets in log-normal clutter [R], NRL Report, 1968
[100] A. C. Frery. Alternative distributions for the multiplication model in sar images [C]. Int. Geosci. Remote Sensing Symp., Florence, Italy, 1995, 1. 169-171
[101] C. L. Martinez. Polarimetric sar speckle noise model [J], IEEE Trans. GRS, 2003. 41 (10): 2232-2242
[102]高贵. Sar图像目标roi自动获取技术研究国防科技大学, 2007
[103] R. S. Raghavan. A method for estimating parameters of k-distributed clutter [J], IEEE Trans. AES, 1991. 27 (2): 238-246
[104] S. Watts. A practical approach to the prediction and assessment of radar performance in sea clutter [C]. IEEE International Radar Conference, 1995. 181- 186
[105] S. Watts. Radar detection prediction in k-distributed sea clutter and thermal noise [J], IEEE Trans. AES, 1987. 23 (1): 40-45
[106] S. Watts. Radar detection prediction in sea clutter using the compound k distribution model [J], IEE Proc., 1985. 132 (2)
[107] K. D. Ward. A radar sea clutter model and its application to performance assessment [J], Radar-82: 203-207
[108] E. Conte, M. Longo. On a coherent model for log-normal clutter [J], 1987. 134 (2): 198-201
[109]扈罗全,林乐科,朱洪波.三种重拖尾分布海杂波的比较与分析[J],电波科学学报, 2007. 22 (6)
[110] D. Blacknell, J. A. Tough. Parameter estimation for the k-distribution based onzlog(z) [J], IEE Proc.-F, 2001. 148 (6): 309-312
[111] S. S. Rappaport, L. Kurz. An optimal nonlinear detectorfor digital data transmission through non-gaussian channels [J], IEEE Trans. Comm. Techn., 1966. COM-14: 266-274
[112] J. H. Miller, J. B. Thomas. Detectors for discrete-time signals in non-gaussian noise [J], IEEE Trans. Inform. Theory, 1972. IT-18: 241-250
[113] ------. Performance of optimal and suboptimal receivers in the presence of impulsive noise modeled as an alpha-stable process [J], IEEE Trans. Commun., 1995. 43: 904-914
[114] ------. On the detection of stochastic impulsive transients over back-ground noise [J], Signal Processing, 1995. 41: 175-190
[115] C. L. Nikias, M. Shao. Signal processing with alpha-stable distributions and applications [M]. New York: Wiley, 1995.
[116] J. G. Fleischaman, S. Ayasli, E. M. Adams, et al. Foliage penetration experiment: Part i:Foliage attenuation and backscatter analysis of sar imagery [J], IEEE Trans on Aerospace and Electronic System, 1996. 32 (1): 134-144
[117] ------. On symmetric stable models for impulsive noise [J], USC-SIPI, 1993. 231
[118] P. Levy. Calcul des probabilites [M]. Paris: Gauthier-Villards, 1925.
[119] R. Kapoor, A. Banerjee, G. A. Tsihrintzis, et al. Uwb radar detection of targets in foliage using alpha-stable clutter models [J], IEEE Trans on Aerospace and Electronic Systems, 1999. 35 (3): 819-834
[120] G. A. Tsihritzis, M. Shao, C. L. Nikias. Recent results in application and processing of alpha-stable distributed time series [J], Journal of the Franklin Institute, 1996. 333B: 467-497
[121] M. Shao, C. L. Nikias. Signal processing with fractional lower-order moments:Stable processes and their applications [C]. Proceedings of the IEEE, 1993, 81: 986-1010
[122] M. T. Rey, A. Drosopoulos, D. Petrovic. A search procedure for ships in radar-sat imagery [R], Defence Research Establishment Ottawa, 1996
[123] I. R. Joughin, D. B. Percival, D. P. Winebrenner. Maximum likelihood estimation of k-distribution parameters for sar data [J], IEEE Trans. Geosci. Remote Sensing, 1993. 31: 989-999
[124] J. Zhu, Y. Su. A new parameter estimation for k distribution clutter [C]. 9th International Conference on Signal Processing, Beijing, 2008, 2. 1103-1107
[125]郝程鹏,侯朝焕,鄢锦.一种新的k分布形状参数估计器[J],电子与信息学报, 2005. 27 (9): 1404-1407
[126] D.Blacknell. Comparison of parameter estimators for k-distribution [J], IEE Proc. Radar, Sonar, Navigation, 1994. 141 (1): 45-52
[127] D. R. Ikkander, A. M. Zoubir. Estimating the parameters of k distribution using higher order and factional moments [J], IEEE Trans on Aerospace and Electronic System, 1999. 35 (4)
[128] W. DuMouchel. Stable distributions in statistical interference [D],New Haven: Yale, 1971
[129] E. Fama, R. Roll. Parameter estimation for symmetric stable distributions [J], Journal of American Statistical Association, 1971. 66: 331-338
[130] X. Ma, C. L. Nikias. On blind channel identification for impulsive signal environments [C]. In Proceedings of the International Conference onAcoustics,Speech,and Signal Processing(ICASSP' 95), Detroit,MI, 1995,
[131] I. Koutrouvelis. Regression-type estimation of the parameters of stable laws [J], Journal of the American Statistical Association, 1980. 66: 918-928
[132] G. A. Tsihrintzis, C. L. .Nikias. Fast estimation of the parameters of alpha-stable impulsive interference [J], IEEE Transactions on Signal Processing, 1996. 44 (6): 1492-1503
[133]陈高波.对称alpha稳定分布的最小绝对偏差参数估计[J],统计与决策, 2008. (10): 162-163
[134] L. B. Christopher, M. Z. Abdelhak. On the estimation of the parameters of alpha-stable distributions using linear regression in the characteristic function domain [J], IEEE, 1988.
[135] G. Moser. Sar amplitude probability density function edtimation based on generalized gaussian scattering model [C]. SPIE, 2004, 5573. 307-318
[136]邹焕新. Sar图像舰船目标与航迹检测方法研究[D],长沙:国防科学技术大学, 2003
[137]张琦.基于统计模型的高分辨sar图像车辆目标检测方法[D],长沙:国防科技大学, 2005
[138] Y. Li, K. Ji, Y. Su. Quantitative analysis of statistical models for typical terrains in sar data [C]. Proceedings of 2006 CIE International Conference on Radar, 1: 696-699
[139] J. B. Billingsley, A. Farina. Statistical analyses of measured radar ground clutter data [J], IEEE Transactions on Aerospace and Electronic Systems, 1999. 35 (2): 579-593
[140] D. D. Michael, A. O. S. Joseph. Quantitative statistical assessment of conditional models for synthetic aperture radar, [J], IEEE Transactions On Image Processing, 13: 113-125
[141] D. D. Michael. Statistical assessment of model fit for synthetic aperture radar data [C]. SPIE, 2001, 4382. 379-388
[142] R. B. D'Agostino. A suggestion for using powerful and informative tests of normality [J], The American Statistician, 1990. 44 (4): 316-321
[143]方学立. Uwb-sar图像中的目标检测与鉴别[D],长沙:国防科技大学, 2005
[144] J. P. Nolan. Http://academic2.American.Edu/~jpnolan
[145] J. P. Nolan. Numberical calculation of stable densities and distribution functions [J], Communications in Statistics-Stochastic Models, 1997. 13: 759-774
[146]郝鹏程,刘斌,闫晟, et al.基于有序统计量和自动删除平均的最大选择恒虚警检测器[J],信号处理, 2008. 24 (4): 578-581
[147] S. Erfanian, V. T. Vakili. Introducing excision switching-cfar in k distributed sea clutter [J], Signal Processing, 2009. 89 (6): 1023-1031
[148]何友,关键,彭应宁, et al.雷达自动检测与恒虚警处理[M].北京:清华大学出版社, 1999.
[149] L. M. Novak. The automatic target-recognition system in saip [J], The Lincoln Laboratory Journal, 1997. 10 (2): 187-202
[150] J. Ritcey. An order-statistics-based cfar for sar applications [D],Seattle: University of Washington, 1990
[151] M. E. Smith, P. K. Varshney. Vi-cfar: A novel cfar algorithm based on data variability [C]. IEEE national Radar Conference, 1997. 263-268
[152] M. E. Smith, P. K. Varshney. Intelligent cfar processor based on data variability [J], IEEE Trans on Aerospace and Electronic System, 2000. 36 (3): 837-847
[153] P. W. Vachon. Validation of ship detection by the radarsat synthetic aperture radar and the ocean monitoring workstation [J], Canadian Journal of Remote Sensing, 2000. 26 (3): 200-212