合成孔径雷达图像上舰船目标的检测
|
摘要
随着合成孔径雷达(SAR,Synthetic Aperture Radar)成像技术的发展,SAR图像的分辨率有了大幅度的提高,利用SAR图像进行舰船目标检测已经成为海洋遥感领域的重要应用之一。
本文首先介绍了SAR图像的形成和解译原理,主要包括SAR图像的二维分辨原理、SAR图像中相干斑的形成机制以及SAR图像的解译依据。它们是本文的理论依据和研究基础。
本文接着分析了影响SAR图像上舰船目标检测的主要因素,研究了图像分割的基本知识与国内外近年来在SAR图像上舰船检测方面的研究成果。以计算机模拟仿真实验为手段,测试和比较了各种用于SAR图像上舰船目标检测的算法。
本文提出了一种改进的SAR图像上舰船目标的检测算法,以进一步减小相干斑对舰船目标检测的影响。与已有的算法相比,该算法的改进主要表现在以下两个方面:
1.改进的最大类熵和法。该算法针对SAR图像上海面背景与舰船目标的不同分布特性,对传统的最大类熵和法进行改进。算法的检测门限被选择以最大化背景灰度熵与目标灰度熵的加权和。由于加权系数的引入,该算法能针对实际情况,调整背景灰度熵与目标灰度熵在准则函数中所占的比例,给出恰当的检测门限。
2.自适应的形态学处理。该算法根据二值图像上噪声斑点面积的概率密度函数,计算出最恰当的形态学结构体大小,使形态学处理能够在平滑船体目标的同时消除大量的噪声斑点。
为了验证本文提出算法的有效性,研究过程中进行了计算机模拟仿真实验。仿真实验的对象是包含不同强度相干斑的SAR图像。实验结果证明本文提出的算法能够在高强度相干斑的影响下工作,符合SAR图像上舰船目标检测的实际情况,是一种有效的检测算法。
我国领海广阔,海洋资源丰富,研究SAR图像上舰船目标的检测无论是在军事上还是在民事上都有重要意义。希望本文对SAR图像上舰船目标检测的研究工作,能够为我国海洋目标检测与监视技术做出贡献。
With the development of the synthetic aperture radar (SAR) imaging techniques, the resolution of SAR images has been significantly improved. Using SAR images to detect ship targets has become the one of important applications in the field of ocean remote sensing.
First, this paper introduces the reconstruction and the interpretation of SAR images, including the two-dimensional resolving of SAR images, the mechanism of speckle in SAR images, and the interpretation signs of SAR images. They are the theoretical basis of further study.
Then, this paper analyses the factors influencing ship detection, discusses the basic knowledge about image segmentation and the basic methods of the ship detection in the SAR images, and tests and compares these methods by computer simulation.
This paper presents an improved algorithm for ship detection in SAR images to further decrease the effect of speckle. Compared with existing algorithms, it has the following two improvements:
1. Improved maximum entropy method. This method improves the original maximum entropy method according to the different distribution characteristics of ships and ocean surfaces in SAR images. The improved algorithm chooses the detection threshold to maximize the weighted sum of the background entropy and the target entropy. This method uses the weights to adjust the proportions of the background entropy and the target entropy in the criterion function, and yields a better detection threshold.
2. Adaptive morphological processing. This algorithm finds the optimal size of the structuring element according to the probability density function of the speckle size, and thus has a better performance in smoothing the ships and suppressing speckle.
SAR images with speckles with different intensities are simulated to evaluate the presented algorithm. The results show that this algorithm is effective even when the speckle is strong.
Our country has a large marginal sea and rich marine resources. Ship detection in SAR images plays an important role in civilian and military applications. I hope that my work about ship detection in SAR images can contribute to our country’s maritime surveillance and target detection technologies.
本文首先介绍了SAR图像的形成和解译原理,主要包括SAR图像的二维分辨原理、SAR图像中相干斑的形成机制以及SAR图像的解译依据。它们是本文的理论依据和研究基础。
本文接着分析了影响SAR图像上舰船目标检测的主要因素,研究了图像分割的基本知识与国内外近年来在SAR图像上舰船检测方面的研究成果。以计算机模拟仿真实验为手段,测试和比较了各种用于SAR图像上舰船目标检测的算法。
本文提出了一种改进的SAR图像上舰船目标的检测算法,以进一步减小相干斑对舰船目标检测的影响。与已有的算法相比,该算法的改进主要表现在以下两个方面:
1.改进的最大类熵和法。该算法针对SAR图像上海面背景与舰船目标的不同分布特性,对传统的最大类熵和法进行改进。算法的检测门限被选择以最大化背景灰度熵与目标灰度熵的加权和。由于加权系数的引入,该算法能针对实际情况,调整背景灰度熵与目标灰度熵在准则函数中所占的比例,给出恰当的检测门限。
2.自适应的形态学处理。该算法根据二值图像上噪声斑点面积的概率密度函数,计算出最恰当的形态学结构体大小,使形态学处理能够在平滑船体目标的同时消除大量的噪声斑点。
为了验证本文提出算法的有效性,研究过程中进行了计算机模拟仿真实验。仿真实验的对象是包含不同强度相干斑的SAR图像。实验结果证明本文提出的算法能够在高强度相干斑的影响下工作,符合SAR图像上舰船目标检测的实际情况,是一种有效的检测算法。
我国领海广阔,海洋资源丰富,研究SAR图像上舰船目标的检测无论是在军事上还是在民事上都有重要意义。希望本文对SAR图像上舰船目标检测的研究工作,能够为我国海洋目标检测与监视技术做出贡献。
With the development of the synthetic aperture radar (SAR) imaging techniques, the resolution of SAR images has been significantly improved. Using SAR images to detect ship targets has become the one of important applications in the field of ocean remote sensing.
First, this paper introduces the reconstruction and the interpretation of SAR images, including the two-dimensional resolving of SAR images, the mechanism of speckle in SAR images, and the interpretation signs of SAR images. They are the theoretical basis of further study.
Then, this paper analyses the factors influencing ship detection, discusses the basic knowledge about image segmentation and the basic methods of the ship detection in the SAR images, and tests and compares these methods by computer simulation.
This paper presents an improved algorithm for ship detection in SAR images to further decrease the effect of speckle. Compared with existing algorithms, it has the following two improvements:
1. Improved maximum entropy method. This method improves the original maximum entropy method according to the different distribution characteristics of ships and ocean surfaces in SAR images. The improved algorithm chooses the detection threshold to maximize the weighted sum of the background entropy and the target entropy. This method uses the weights to adjust the proportions of the background entropy and the target entropy in the criterion function, and yields a better detection threshold.
2. Adaptive morphological processing. This algorithm finds the optimal size of the structuring element according to the probability density function of the speckle size, and thus has a better performance in smoothing the ships and suppressing speckle.
SAR images with speckles with different intensities are simulated to evaluate the presented algorithm. The results show that this algorithm is effective even when the speckle is strong.
Our country has a large marginal sea and rich marine resources. Ship detection in SAR images plays an important role in civilian and military applications. I hope that my work about ship detection in SAR images can contribute to our country’s maritime surveillance and target detection technologies.
引文
[1] 王士俊 等著. 国防千里眼-雷达. 中国人民解放军战士出版社. 1979.
[2] 刘永坦 等著. 雷达成像技术. 哈尔滨工业大学出版社. 1999.
[3] 种劲松 等著. 合成孔径雷达舰船目标检测算法与应用研究[博士论文]. 中国科学院研究生院(电子学研究所). 2002.
[4] 种劲松 等著. 合成孔径雷达图像海洋目标检测. 海洋出版社. 2006.
[5] Skoelv A, etc. Simulation of SAR Imaging of Ship wakes. Proceedings of IGARSS’88 Symposium, Edinburgh, Scotland. 1988.
[6] Lyden. Analysis of Synthetic Aperture Radar Imagery of Surface Ship wakes. Proceedings of IGARSS’86, Zurich. 1986.
[7] Murphy L M. Linear Feature Detection and Enhancement in Noisy Images Via the Radon Transform. Pattern Recognition Letters. 1986.
[8] Eldhuset K. An automatic ship and ship wake detection system for spaceborne SAR images in coastal regions. IEEE Transactions on Geoscience and Remote Sensing. 1996(34): 1010～1019.
[9] Suller, etc. The Effect of Ship’s Screws on the Ship Wake and Its Implication for the Radar Image of the Wake. Proceedings of IGARSS’89 Symposium, Vancouver, Canada. 1989.
[10] Hughes B A, etc. Joint Canada-U.S. Ocean Wave Investigation Project: An Overview of the Georgia Strait Experiment. Journal of Geophysical Research. 1988(93).
[11] Tunaley J, etc. The Radar Image of the Turbulent Wake Generated by a Moving Ship. Proceedings of IGARSS’89 Symposium, Vancouver, Canada. 1989.
[12] Wahl T, Skoelv A. SAR Imaging of Ships and Ship Wakes During NORCEX’88. Proceedings of IGARSS’89 Symposium, Vancouver, Canada. 1989.
[13] Rey M T, etc. The Use of the Radon Transform for Wake Detection in Seasat Images. Proceedings of IGARSS’89 Symposium, Vancouver, Canada. 1989.
[14] Eldhuset. Principles and Performance of an Automatic Ship Detection System forSAR Image. Proceedings of IGARSS’89 Symposium, Vancouver, Canada. 1989.
[15] Yermy M. Ocean Surveillance with Polarimetric SAR. Canadian Journal of Remote Sensing. 2001(27).
[16] Wakerman C C, etc. Automatic Ship Detection of Ships in RADARSAT SAR Imagery. Canadian Journal of Remote Sensing. 2001(27).
[17] Feernandez D M, etc. Detection of Ships with Multi-Frequency and CODAR SeaSonde HF Radar Systems. Canadian Journal of Remote Sensing. 2001(27).
[18] Ponsford. Surveillance of the 200 Nautical Mile Exclusive Economic Zone (EEZ) Using High Frequency Surface Wave Radar (HFSWR). Canadian Journal of Remote Sensing. 2001(27).
[19] Stapleton N R. Ship Wakes in Radar Imagery. Journal of Remote Sensing. 1997(18).
[20] Ouchi K, etc. Multi-Frequency SAR Images of Ship – Generated Internal Waves. Journal of Remote Sensing. 1997(18).
[21] Vachon P W. Ship Detection by the RADARSAT SAR: Validation of Detection Model Predictions. Canadian Journal of Remote Sensing. 1997(23).
[22] Vachon P W, etc. Validation of Ship Detection by the RADARSAT Synthetic Aperture Radar and the Ocean Monitoring Workstation. Canadian Journal of Remote Sensing. 1997(13).
[23] Hendry A, etc. Vessel Detection with Wide Area Remote Sensing. Sea Technology. 1998(39).
[24] Hawkins R K, Murnaghan K P, Yeremy M, Rey M. Ship Detection Using Airborne Polarimetric SAR. In CEOS SAR Workship, Tokyo, Japan. 2001.
[25] Ch.Gierull, M.Ruppel. An End-to-End Synthetic Aperture Radar Simulator. EUSAR'96, Konigswinter, Germany. 1996: 569~572.
[26] Giorgio Franceschetti, Maurizio Migliaccio, Daniele Riccio. The SAR Simulation: an Overview. IEEE PROCEEDINGS OF IGARSS'95. 1995(3): 2283~2285.
[27] Richard Bamler, Hartmut Runge, Uirieh Steinbrecher. A Distributed Target SAR raw data simulator with arbitrary Doppler variation. IEEE PROCEEDINGS OF IGARSS'92. 1992: 287~290.
[28] Ulaby F T, Moore R K, Fung A K. 黄培康等译. 微波遥感. 科学出版社. 1988.
[29] 张澄波. 综合孔径雷达. 科学出版社. 1989: 3~8.
[30] Tomiyasu K, Tutorial Review of Synthetic Aperture Radar with Applications to Imaging of the Ocean Surface. Proc. of IEEE. 1978(5): 563~583.
[31] Sherwin C W, Ruina J P, Rawcliff R D. Some Early Developments in Synthetic Aperture Radar Systems. IRE Trans. on Military Electronics. 1962 (2): 111~115.
[32] 什科尔尼克 等著. 雷达系统导论. 电子工业出版社. 2006.
[33] 克拉特 等著. 雷达散射截面. 电子工业出版社. 1988.
[34] 斯克尔尼克主编. 雷达手册. 谢卓译. 国防工业出版社. 1978(6).
[35] Tomiyasu. Relationship Between and Measurement of differential Scattering Coefficient and Bidirectional Reflectance Distribution Function(BRDF). IEEE Trans. Geosci. Remote Sensing. 1988(5): 660~665.
[36] 伊伏斯,里迪著. 现代雷达原理. 卓荣邦,杨士毅,张金全等译. 电子工业出版社. 1991.
[37] Clapp. R. E. A Theoretical and Experimental Study of Radar Ground Return. MIT Radiation Lab. 1946.
[38] Franceschetti G., Migliaccio M, Riccio D, etc. SARAS: a synthetic aperture radar(SAR) raw signal simulator. IEEE Trans. on Geoscience and Remote Sensing. 1992 (1): 110~123.
[39] 梅安新 等著. 遥感导论. 高等教育出版社. 2002.
[40] 周成虎 等著. 遥感影像地学理解与分析. 科学出版社. 1999.
[41] 王润生. 图像理解. 国防科技大学出版社. 1995.
[42] Chris Oliver. Information from SAR Images. Journal of Physics D: Appl. Phys. 1991.
[43] 郭华东 等著. 雷达对地贯彻理论与应用. 科学出版社. 2000.
[44] 舒士畏, 赵立平. 雷达图像及其应用. 中国铁道出版社. 1988.
[45] 布鲁克纳 等著. 雷达技术. 国防工业出版社. 1984.
[46] Chris Oliver, Shaun Quegan. Understanding Synthetic Aperture Radar Images. Artech House Inc. London. 1998.
[47] 章毓晋. 图象分割. 科学出版社. 2001.
[48] 王新成. 高级图象处理技术. 中国科学技术出版社. 2000.
[49] 阮颖铮, 等著. 雷达截面与隐身技术. 国防工业出版社. 2001.
[50] Staples G C. Ship Detection using RADARSAT SAR Imagery. Geomatics in the Era of RADARSAT, Ottawa, Canada. 1997.
[51] Henderson FM, Lewis A J. Principles & Applications of Imaging Radar. Manual of Remote Sensing. Third Edition. 1998(2).
[52] 王小谟 等著. 雷达与探测-现代战争的火眼金睛. 国防工业出版社. 2000.
[53] Skolnik M. An Empirical Formula for the Radar Cross Section of Ships at Grazing Incidence. IEEE Trans. on Aerospace and Electronic System. 1974(10).
[54] Skolnik M. Introduce to Radar Systems. Second Edition. McGraw-Hill Book Company. 1980.
[55] 张杰 等著. 合成孔径雷达海洋信息处理与应用. 科学出版社. 2004.
[56] 迈特尔 等著. 合成孔径雷达图像处理. 电子工业出版社. 2005.
[57] Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics. 1979(9): 62~66.
[58] Kapur J N, Sahoo P K, Wong A K C. A new method of gray-level picture thresholding using the entropy of the histogram. Computer Vision, Graphics and Image Processing. 1985(29): 273~285.
[59] Kittler J, Illingworth J. Minimum error thresholding. Pattern Recognition. 1986(19): 41~47.
[60] Tsai W. Moment-preserving thresholding: A new approach. Computer Vision, Graphics and Image Processing. 1985(29): 377~393.
[61] Castleman K R. Digital Image Processing. Prentice Hall. 1998.
[62] Gonzalez R C, Woods R W, Eddins S L. Digital Image Processing Using MATLAB. Prentice Hall. 2003.
[63] Casaent D, Su Wei, Turaga Deepak, Narusawa Nobuhiko, Ashizawa Satoshi. SAR Ship Detection Using New Conditional Contrast Box Filter. In SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery VI. 1999(37): 274~284.
[64] Novak LM, etc. Effects of Polarization and Resolution on SAR ATR. IEEE Trans. on Aerospace and Electronic System. 1997( 33): 102~115.
[65] Jakeman E, Pusey P N. A Model for Non-Rayleigh Sea Echo. IEEE Trans. on Antennas Propagation. 1976(24): 806~814.
[66] Armstrong B C, etc. CFAR Detection of Fluctuating Targets in SpatiallyCorrelated K-Distributed Clutter. IEEE Proceedings-F. 1991(138).
[67] Robertson N, etc. Ship Surveillance Using RADARSAT ScanSAR Images. Ship Detecion in Coastal Waters Workshop 2000, NS, Canada. 2000.
[68] Gonzalez R C, Richard E Woods. Digital Image Processing. Second Edition. Prentice Hall. 2002.
[2] 刘永坦 等著. 雷达成像技术. 哈尔滨工业大学出版社. 1999.
[3] 种劲松 等著. 合成孔径雷达舰船目标检测算法与应用研究[博士论文]. 中国科学院研究生院(电子学研究所). 2002.
[4] 种劲松 等著. 合成孔径雷达图像海洋目标检测. 海洋出版社. 2006.
[5] Skoelv A, etc. Simulation of SAR Imaging of Ship wakes. Proceedings of IGARSS’88 Symposium, Edinburgh, Scotland. 1988.
[6] Lyden. Analysis of Synthetic Aperture Radar Imagery of Surface Ship wakes. Proceedings of IGARSS’86, Zurich. 1986.
[7] Murphy L M. Linear Feature Detection and Enhancement in Noisy Images Via the Radon Transform. Pattern Recognition Letters. 1986.
[8] Eldhuset K. An automatic ship and ship wake detection system for spaceborne SAR images in coastal regions. IEEE Transactions on Geoscience and Remote Sensing. 1996(34): 1010～1019.
[9] Suller, etc. The Effect of Ship’s Screws on the Ship Wake and Its Implication for the Radar Image of the Wake. Proceedings of IGARSS’89 Symposium, Vancouver, Canada. 1989.
[10] Hughes B A, etc. Joint Canada-U.S. Ocean Wave Investigation Project: An Overview of the Georgia Strait Experiment. Journal of Geophysical Research. 1988(93).
[11] Tunaley J, etc. The Radar Image of the Turbulent Wake Generated by a Moving Ship. Proceedings of IGARSS’89 Symposium, Vancouver, Canada. 1989.
[12] Wahl T, Skoelv A. SAR Imaging of Ships and Ship Wakes During NORCEX’88. Proceedings of IGARSS’89 Symposium, Vancouver, Canada. 1989.
[13] Rey M T, etc. The Use of the Radon Transform for Wake Detection in Seasat Images. Proceedings of IGARSS’89 Symposium, Vancouver, Canada. 1989.
[14] Eldhuset. Principles and Performance of an Automatic Ship Detection System forSAR Image. Proceedings of IGARSS’89 Symposium, Vancouver, Canada. 1989.
[15] Yermy M. Ocean Surveillance with Polarimetric SAR. Canadian Journal of Remote Sensing. 2001(27).
[16] Wakerman C C, etc. Automatic Ship Detection of Ships in RADARSAT SAR Imagery. Canadian Journal of Remote Sensing. 2001(27).
[17] Feernandez D M, etc. Detection of Ships with Multi-Frequency and CODAR SeaSonde HF Radar Systems. Canadian Journal of Remote Sensing. 2001(27).
[18] Ponsford. Surveillance of the 200 Nautical Mile Exclusive Economic Zone (EEZ) Using High Frequency Surface Wave Radar (HFSWR). Canadian Journal of Remote Sensing. 2001(27).
[19] Stapleton N R. Ship Wakes in Radar Imagery. Journal of Remote Sensing. 1997(18).
[20] Ouchi K, etc. Multi-Frequency SAR Images of Ship – Generated Internal Waves. Journal of Remote Sensing. 1997(18).
[21] Vachon P W. Ship Detection by the RADARSAT SAR: Validation of Detection Model Predictions. Canadian Journal of Remote Sensing. 1997(23).
[22] Vachon P W, etc. Validation of Ship Detection by the RADARSAT Synthetic Aperture Radar and the Ocean Monitoring Workstation. Canadian Journal of Remote Sensing. 1997(13).
[23] Hendry A, etc. Vessel Detection with Wide Area Remote Sensing. Sea Technology. 1998(39).
[24] Hawkins R K, Murnaghan K P, Yeremy M, Rey M. Ship Detection Using Airborne Polarimetric SAR. In CEOS SAR Workship, Tokyo, Japan. 2001.
[25] Ch.Gierull, M.Ruppel. An End-to-End Synthetic Aperture Radar Simulator. EUSAR'96, Konigswinter, Germany. 1996: 569~572.
[26] Giorgio Franceschetti, Maurizio Migliaccio, Daniele Riccio. The SAR Simulation: an Overview. IEEE PROCEEDINGS OF IGARSS'95. 1995(3): 2283~2285.
[27] Richard Bamler, Hartmut Runge, Uirieh Steinbrecher. A Distributed Target SAR raw data simulator with arbitrary Doppler variation. IEEE PROCEEDINGS OF IGARSS'92. 1992: 287~290.
[28] Ulaby F T, Moore R K, Fung A K. 黄培康等译. 微波遥感. 科学出版社. 1988.
[29] 张澄波. 综合孔径雷达. 科学出版社. 1989: 3~8.
[30] Tomiyasu K, Tutorial Review of Synthetic Aperture Radar with Applications to Imaging of the Ocean Surface. Proc. of IEEE. 1978(5): 563~583.
[31] Sherwin C W, Ruina J P, Rawcliff R D. Some Early Developments in Synthetic Aperture Radar Systems. IRE Trans. on Military Electronics. 1962 (2): 111~115.
[32] 什科尔尼克 等著. 雷达系统导论. 电子工业出版社. 2006.
[33] 克拉特 等著. 雷达散射截面. 电子工业出版社. 1988.
[34] 斯克尔尼克主编. 雷达手册. 谢卓译. 国防工业出版社. 1978(6).
[35] Tomiyasu. Relationship Between and Measurement of differential Scattering Coefficient and Bidirectional Reflectance Distribution Function(BRDF). IEEE Trans. Geosci. Remote Sensing. 1988(5): 660~665.
[36] 伊伏斯,里迪著. 现代雷达原理. 卓荣邦,杨士毅,张金全等译. 电子工业出版社. 1991.
[37] Clapp. R. E. A Theoretical and Experimental Study of Radar Ground Return. MIT Radiation Lab. 1946.
[38] Franceschetti G., Migliaccio M, Riccio D, etc. SARAS: a synthetic aperture radar(SAR) raw signal simulator. IEEE Trans. on Geoscience and Remote Sensing. 1992 (1): 110~123.
[39] 梅安新 等著. 遥感导论. 高等教育出版社. 2002.
[40] 周成虎 等著. 遥感影像地学理解与分析. 科学出版社. 1999.
[41] 王润生. 图像理解. 国防科技大学出版社. 1995.
[42] Chris Oliver. Information from SAR Images. Journal of Physics D: Appl. Phys. 1991.
[43] 郭华东 等著. 雷达对地贯彻理论与应用. 科学出版社. 2000.
[44] 舒士畏, 赵立平. 雷达图像及其应用. 中国铁道出版社. 1988.
[45] 布鲁克纳 等著. 雷达技术. 国防工业出版社. 1984.
[46] Chris Oliver, Shaun Quegan. Understanding Synthetic Aperture Radar Images. Artech House Inc. London. 1998.
[47] 章毓晋. 图象分割. 科学出版社. 2001.
[48] 王新成. 高级图象处理技术. 中国科学技术出版社. 2000.
[49] 阮颖铮, 等著. 雷达截面与隐身技术. 国防工业出版社. 2001.
[50] Staples G C. Ship Detection using RADARSAT SAR Imagery. Geomatics in the Era of RADARSAT, Ottawa, Canada. 1997.
[51] Henderson FM, Lewis A J. Principles & Applications of Imaging Radar. Manual of Remote Sensing. Third Edition. 1998(2).
[52] 王小谟 等著. 雷达与探测-现代战争的火眼金睛. 国防工业出版社. 2000.
[53] Skolnik M. An Empirical Formula for the Radar Cross Section of Ships at Grazing Incidence. IEEE Trans. on Aerospace and Electronic System. 1974(10).
[54] Skolnik M. Introduce to Radar Systems. Second Edition. McGraw-Hill Book Company. 1980.
[55] 张杰 等著. 合成孔径雷达海洋信息处理与应用. 科学出版社. 2004.
[56] 迈特尔 等著. 合成孔径雷达图像处理. 电子工业出版社. 2005.
[57] Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics. 1979(9): 62~66.
[58] Kapur J N, Sahoo P K, Wong A K C. A new method of gray-level picture thresholding using the entropy of the histogram. Computer Vision, Graphics and Image Processing. 1985(29): 273~285.
[59] Kittler J, Illingworth J. Minimum error thresholding. Pattern Recognition. 1986(19): 41~47.
[60] Tsai W. Moment-preserving thresholding: A new approach. Computer Vision, Graphics and Image Processing. 1985(29): 377~393.
[61] Castleman K R. Digital Image Processing. Prentice Hall. 1998.
[62] Gonzalez R C, Woods R W, Eddins S L. Digital Image Processing Using MATLAB. Prentice Hall. 2003.
[63] Casaent D, Su Wei, Turaga Deepak, Narusawa Nobuhiko, Ashizawa Satoshi. SAR Ship Detection Using New Conditional Contrast Box Filter. In SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery VI. 1999(37): 274~284.
[64] Novak LM, etc. Effects of Polarization and Resolution on SAR ATR. IEEE Trans. on Aerospace and Electronic System. 1997( 33): 102~115.
[65] Jakeman E, Pusey P N. A Model for Non-Rayleigh Sea Echo. IEEE Trans. on Antennas Propagation. 1976(24): 806~814.
[66] Armstrong B C, etc. CFAR Detection of Fluctuating Targets in SpatiallyCorrelated K-Distributed Clutter. IEEE Proceedings-F. 1991(138).
[67] Robertson N, etc. Ship Surveillance Using RADARSAT ScanSAR Images. Ship Detecion in Coastal Waters Workshop 2000, NS, Canada. 2000.
[68] Gonzalez R C, Richard E Woods. Digital Image Processing. Second Edition. Prentice Hall. 2002.