复杂背景下红外弱小目标检测算法研究
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摘要
红外弱小目标检测技术是红外预警系统中的核心技术,这类武器系统对国家安全起着极为重要的作用。为此,本文就红外弱小目标检测问题进行了深入的分析与讨论,提出了新的检测方法与算法研究思路,并从系统实现角度对武器系统的设计进行了详细的论述,提出了两种红外搜索与跟踪系统的组网构想,为红外预警系统的设计提供了相关依据。
本文从不同角度对红外弱小目标图像进行分析,提出了新的弱小目标检测方法:
利用图像方差加权信息熵对红外弱小目标图像进行分析,并将其作为对图像复杂度定量描述,深入讨论了不同类别区域复杂度特征值的本质成因,并基于此构造了新的图像预处理方法与自适应门限分割方法,进而实现了对红外弱小目标图像的自适应门限分割完成目标检测。
利用模糊分类的思想对红外弱小目标图像进行分析,根据红外弱小目标图像中不同区域灰度分布情况,将图像分为多个类别区域,并定义了类别特征矢量,同时依据模糊分类准则定义了类别相似系数,进而对图像中不同区域进行类属性判别,最后通过对弱小目标类别进行提取从而实现弱小目标检测。
对基于分类模型的模糊分类弱小目标检测方法进行扩展:重新构造分类模型,根据类别核及其类别特征矢量,结合模糊分类理论定义了类别相似系数,并定义了类别贴近度来实现不同类别区域的类别归并,从而解决了因红外弱小目标图像中包含本文建立的类别区域不全而带来的误分类问题,得到了一种全新的弱小目标检测思路。对上述两种基于模糊分类目标检测方法进行了进一步的推广,得到了基于分类算法的算法框图。
对传统的空域背景抑制算法对复杂背景抑制效果较差的本质原因进行了分析,首次将区域方向直方图概念引入弱小目标检测领域,并基于此构造了背景抑制改进方法:根据背景区域的灰度分布特点针对性的构造背景抑制算法,引入了区域方向直方图作为不同区域的类别判别依据,并定义了每类类别共性模板与共性背景预测系数模板,将局部二值模式(LBP)作为类别区域的结构表达方式,利用其对不同区域的背景预测系数模板进行修正以得到适应每个区域的系数模板,由此,实现了对传统背景抑制算法的改进。
最后,从系统实现角度出发,对弱小目标检测系统的组成与设计进行了较为深入的研究,给出了适用于实时弱小目标检测系统的软硬件工程化设计与开发方法,并提出了预警与火控系统组网的两种构想,为该系统在实际的应用提供了设计依据。
The detection of dim target is the core technique in infrared surveillance system (IRSS), which takes a significant role in national security safeguarding. Accordingly, in this thesis, profound analysis and discussion on the detection technique above, and several novel detection approaches and research ideas are proposed; with detailed descriptions on the design of weapon system form the angles of realization. Moreover, two networking composition on infrared search and tracking system are established, providing relevant basis for the construction of infrared surveillance systems.
Based on the analysis from different points of view on query images of dim targets, several novel approaches on detection of dim target are put forward.
Firstly, information entropy weighted by image variance is introduced to analyze the images above and describe image complexity, and substantial causes of the complexity features in classified regions are discussed, where based a novel image preprocessing method and a self-adaptive threshold acquisition method are constructed, so that the dim targets can be finally detected with self-adaptive threshold processing. Secondly, fuzzy classification theory is proposed to analyze the infrared query images above, regions with class feature vectors are classified and defined based on the grey distribution, and class similar coefficients are defined according to the fuzzy classification, so that the target detection is achieved by reserving a dim target class.
Thirdly, the fuzzy classification method described above is extended with classification models re-definition, class kernels are accordingly defined combined with class feature vectors, and class similarity degrees are defined to merge classes, so that the problems on mis-classification caused by incomplete class regions included in images of infrared dim targets are solved, where based a novel approach on dim target detection is constructed; an algorithm diagram is given based on the generalization of the two fuzzy detection approaches above.
In addition, based on the traditional approaches of background suppression, analysis are made on the causes of poor suppression performance, and for the first time, regional direction histograms are introduced into the fields of dim target detection, where based an improved background suppression approach is proposed. The principle of traditional background suppression approach is discussed, and then the causes of poor suppression performance on traditional background suppression approaches are obtained. Accordingly, improvements are proposed that regional background suppression should be conducted on the basis of the regional grey features in an image. And therefore, regional direction histograms are introduced for local grey classification, while general models and general background coefficient models are defined with LBP as the structure expression for each regional class, via which background coefficient models are modified to match each region of the query images, so that the improved suppression is achieved.
From the angles of system realization, research on the construction and design of the dim target detection system is profoundly conducted, with the decryptions of hardware & software engineering design and development on real-time application. Moreover, two networking composition on infrared search and tracking system are put forward, providing some basis for practical system application.
本文从不同角度对红外弱小目标图像进行分析,提出了新的弱小目标检测方法:
利用图像方差加权信息熵对红外弱小目标图像进行分析,并将其作为对图像复杂度定量描述,深入讨论了不同类别区域复杂度特征值的本质成因,并基于此构造了新的图像预处理方法与自适应门限分割方法,进而实现了对红外弱小目标图像的自适应门限分割完成目标检测。
利用模糊分类的思想对红外弱小目标图像进行分析,根据红外弱小目标图像中不同区域灰度分布情况,将图像分为多个类别区域,并定义了类别特征矢量,同时依据模糊分类准则定义了类别相似系数,进而对图像中不同区域进行类属性判别,最后通过对弱小目标类别进行提取从而实现弱小目标检测。
对基于分类模型的模糊分类弱小目标检测方法进行扩展:重新构造分类模型,根据类别核及其类别特征矢量,结合模糊分类理论定义了类别相似系数,并定义了类别贴近度来实现不同类别区域的类别归并,从而解决了因红外弱小目标图像中包含本文建立的类别区域不全而带来的误分类问题,得到了一种全新的弱小目标检测思路。对上述两种基于模糊分类目标检测方法进行了进一步的推广,得到了基于分类算法的算法框图。
对传统的空域背景抑制算法对复杂背景抑制效果较差的本质原因进行了分析,首次将区域方向直方图概念引入弱小目标检测领域,并基于此构造了背景抑制改进方法:根据背景区域的灰度分布特点针对性的构造背景抑制算法,引入了区域方向直方图作为不同区域的类别判别依据,并定义了每类类别共性模板与共性背景预测系数模板,将局部二值模式(LBP)作为类别区域的结构表达方式,利用其对不同区域的背景预测系数模板进行修正以得到适应每个区域的系数模板,由此,实现了对传统背景抑制算法的改进。
最后,从系统实现角度出发,对弱小目标检测系统的组成与设计进行了较为深入的研究,给出了适用于实时弱小目标检测系统的软硬件工程化设计与开发方法,并提出了预警与火控系统组网的两种构想,为该系统在实际的应用提供了设计依据。
The detection of dim target is the core technique in infrared surveillance system (IRSS), which takes a significant role in national security safeguarding. Accordingly, in this thesis, profound analysis and discussion on the detection technique above, and several novel detection approaches and research ideas are proposed; with detailed descriptions on the design of weapon system form the angles of realization. Moreover, two networking composition on infrared search and tracking system are established, providing relevant basis for the construction of infrared surveillance systems.
Based on the analysis from different points of view on query images of dim targets, several novel approaches on detection of dim target are put forward.
Firstly, information entropy weighted by image variance is introduced to analyze the images above and describe image complexity, and substantial causes of the complexity features in classified regions are discussed, where based a novel image preprocessing method and a self-adaptive threshold acquisition method are constructed, so that the dim targets can be finally detected with self-adaptive threshold processing. Secondly, fuzzy classification theory is proposed to analyze the infrared query images above, regions with class feature vectors are classified and defined based on the grey distribution, and class similar coefficients are defined according to the fuzzy classification, so that the target detection is achieved by reserving a dim target class.
Thirdly, the fuzzy classification method described above is extended with classification models re-definition, class kernels are accordingly defined combined with class feature vectors, and class similarity degrees are defined to merge classes, so that the problems on mis-classification caused by incomplete class regions included in images of infrared dim targets are solved, where based a novel approach on dim target detection is constructed; an algorithm diagram is given based on the generalization of the two fuzzy detection approaches above.
In addition, based on the traditional approaches of background suppression, analysis are made on the causes of poor suppression performance, and for the first time, regional direction histograms are introduced into the fields of dim target detection, where based an improved background suppression approach is proposed. The principle of traditional background suppression approach is discussed, and then the causes of poor suppression performance on traditional background suppression approaches are obtained. Accordingly, improvements are proposed that regional background suppression should be conducted on the basis of the regional grey features in an image. And therefore, regional direction histograms are introduced for local grey classification, while general models and general background coefficient models are defined with LBP as the structure expression for each regional class, via which background coefficient models are modified to match each region of the query images, so that the improved suppression is achieved.
From the angles of system realization, research on the construction and design of the dim target detection system is profoundly conducted, with the decryptions of hardware & software engineering design and development on real-time application. Moreover, two networking composition on infrared search and tracking system are put forward, providing some basis for practical system application.
引文
[1] B.S.Denney and R.J.P de Figueiredo. Optimal Point Target Detection Using Adaptive Auto Regressive Background Prediction. Signal and Data Processing of Small Targets[C]. Proc. SPIE, 2000, 4048: 46~57.
[2]彭嘉雄,周文琳.红外背景抑制与小目标分割检测[J].电子学报,1999, 27(12): 47~51.
[3] Wang T,Wang C L. A new two dimensional block adaptive FIR filtering algorithm and its application to image restoration [J]. IEEE Trans. On Image Processing, 1998, 7(2): 777~780.
[4] Ohki M. and Hashiguchi S. Two-dimensional LMS adaptive filters [J]. IEEE Trans. Consum. Electron. 1991, 37(1): 66~73.
[5] Soni T., Zeidler J.R. and Ku W.H. Performance evaluation of 2-D adaptive prediction filters for detection of small targets in image data [J]. IEEE Trans. Image Proc. 1993, 2(3): 327~339.
[6]朱红,赵亦工.基于背景自适应预测的红外弱小目标检测[J].红外与毫米波学报, 1999, 18(4): 305-310.
[7] Liu, Jin; Ji, Hong-Bing. Improved Background Prediction Algorithm for IRSmall Targets Detection [J]. IEEE Industrial Electronics & Applications, 2009, 394 ~ 398.
[8]聂洪山,杨卫平,沈振康.基于Wiener滤波的小目标检测方法[J].红外与激光工程, 2006, 32(5): 476~478.
[9] Xu Kaiyu; Hu Wenhua; Zhou Weina; Zheng Huayao. Target Detection Based on The Artificial Neural Network Technology [J]. IEEE, Control, Automation, Robotics and Vision, 2006, 1~5.
[10]尹超,向健勇,韩建栋.一种基于区域背景预测的红外弱小目标检测方法[J].红外技术, 2004, 26(6): 62~65.
[11]徐军,向健华,粱昌洪.最大化背景模型用于检测红外图像中的弱小目标[J].光子学报, 2002, 31(12): 1483~1486
[12]何伟,晋兆虎,张玲.一种改进的利用背景检测弱小目标的方法[J].重庆大学学报, 2005, 28(7): 64~66.
[13]李欣,赵亦工,陈冰.基于方向直方图区域分类的弱小目标检测方法.兵工学报. (一审已通过)
[14] Tom V.T, Peli.T, Leung M, et.al. Morphology-based algorithm for point target detection in infrared backgrounds [J]. SPIE. 1993, 1954: 2~11.
[15]叶斌,彭嘉雄,卢汉清.基于顺序形态滤波的红外运动小目标检测[J].数据采集与处理, 2001,16(3): 315~319.
[16]叶斌,彭嘉雄.基于形态学Top-hat算子的小目标检测方法[J].中国图像图形学报,2002, 7(7): 638~642.
[17]韩建涛,张月,陈曾平.天文图像序列中弱目标的实时检测算法[J].光电工程, 2005, 32(12): 1~4.
[18]苏新主,姬红兵,高新波.一种基于数学形态学的红外弱小目标检测方法[J].红外与激光工程, 2004,33(3): 307~310.
[19]吴巍,彭嘉雄,王海晖.红外图像序列小目标的特性分析与检测[J].红外与激光工程, 2002,31(2): 146~149.
[20]程德杰,李晓峰,李在铭.基于场景运动分析的弱小目标形态学检测方法[J].电子测量与仪器学报, 2006, 20(3): 1~5.
[21]曾明,李建勋.基于自适应形态学Top-Hat滤波器的红外弱小目标检测方法[J].上海交通大学学报, 2006, 40(1): 90~93.
[22] F. Cheng and A. N. Venetsanopoulos. Adaptive Morphological Filter for Image Processing [J]. IEEE Transactions on Image Processing. 1992. 1(4): 533-539.
[23] Jian-Nan Chi, Ping Fu, Dong-Shu Wang, Xin-He Xu. A Detection Method of Infrared Image Small Target Based on Order Morphology Transformation and Image Entropy Difference [J]. IEEE Machine Learning and Cybernetics, 2005, 5111~5116.
[24] G.B. Giannakos and M.K. Tsatsanis. Signal detection and classification using matched filtering and higher-order statistics [J], IEEE Trans. Acoustics, Speech, Signal Processing, 1990, 38: 1284 ~ 1296.
[25] Bin Wu, Hongbing Ji. A Novel Algorithm for Point-Target Detection Based on Third-Order Cumulant in Infrared Image [J]. 2006, IEEE Proc. ICSP,
[26]吕雁.基于高阶累积量的红外图像时域检测[J].激光与红外, 2007, 37(2): 178~180.
[27] SMITH S M. Edge Thinning Used in the SUSAN Edge Detector: Internal Technical Reports TR95SMS5[R]. Farnborough, Hampshire, UK: Defence Research Agency, 1995.
[28] SMITH.S.M, BRADY.J.M. SUSAN-A New Approach to Low Level Image Processing: Internal Technical Report TR95SMS1c[R]. Farnborough, Hampshire, UK: Defence Research Agency, 1995.
[29]毕务忠,严高师.基于改进SUSAN原则的小目标检测算法[J].激光与红外, 2006, 36(6): 504~507.
[30]周进,吴钦章.深空大视场弱小目标的实时检测方法[J].光学技术, 2006, 32(1): 134~137
[31]袁慧晶,王涌天.基于改进SUSAN原则的小目标检测算法[J].激光与红外, 2006, 36(6): 504~507.
[32]郑敏,张启衡.弱小目标检测与跟踪算法[J].光电工程, 2002, 29(4): 10~12.
[33] Zeidler J R, Soni T, Ku W H. Recursive Estimation Techniques For Detection Of Small Objects In Infrared Image Data [J]. IEEE Proceedings International Conference on Acoustics Speech and Signal Processing. 1992, 581~584.
[34] Thomas V, De Mars J. Adaptive dim point target detection and tracking in infrared images [J]. 1988, AD-A204 845.
[35] Himayat N, Kassam S A. A Structure for Adaptive Order Statistics Filtering [J].IEEE Transactions on Image Processing. 1994, 3(3): 265~280.
[36] Kotropoulos C, Pitas I. Adaptive LMS L-Filters for Noise Suppression in Images [J].IEEE Transactions on Image Processing 1996. 5(12): 1596~1609.
[37] Muller M. Saliency Measures in Cluttered IR Images for ATR [J]. SPIE, 1999. 3699: 150~154.
[38] Barnett J. Statistical analysis of media subtraction filtering with application to point target detection in infrared backgrounds [J]. Proc. SPIE, 1989, 1050~1018.
[39] Muller M. Saliency Measures in Cluttered IR Images for ATR [J]. SPIE, 1999, 3699: 150~154.
[40] Scharfand L L, Friedlander B. Matched subspace detectors [J]. IEEE Trans. On Sig.Proc. 1994, 42(8): 2146~2156.
[41] Deshpande S D, Er M H, Ronda V. Max-mean and max-median filters for detection of small targets [J]. SPIE. 1999, 3809: 74-83.
[42] Li Junwei, Zhu Zhenfu, Liu Zhongling. Small target detection from image sequences using improved recursive max-mean filter [J]. SPIE, 2001, 4554: 20~24.
[43] Reed T S, Gagliardi R M, Shao H M. Application of three dimensional filtering to moving target detection [J]. IEEE Trans. on Aerospace and Electronic Systems, 1983, AES-19(6): 123~126.
[44] Reed I. S. Gagliardi R. M. and Stotts L. Optical moving target detection with 3-D match filtering [J]. IEEE Trans. on Aerospace and Electronic Systems, 1988, 24(4): 327~336.
[45]刘金根,吉会云,周灿,刘兴建,姜德生.复杂背景下图像中弱小目标的分割和提取算法[J].武汉理工大学学报, 2006,28(12): 127~129.
[46]张天序,赵广州,王飞,朱光喜.一种快速递归红外舰船图像分割新算法[J].红外毫米波学报, 2006, 25(4): 295~300.
[47]刘建华,毕笃彦,叶广强.基于目标模型的红外弱小目标预检测[J].空军工程大学学报, 2006, 7(5): 36~38.
[48] Bronskill J F, Hepburn J S A, Au W K. A knowledge-based approach to the detection, tracking, and classification of target formations, in infrared image sequences [J]. Proc. IEEE CVPR. 1989, 153~158.
[49] Wang G etal. A pipeline algorithm for detection and tracking of pixel-sized target trajectories [J]. SPIE, 1990, 1305: 167~178.
[50] Lavagetto F. Automatic target detection in infrared sequences through semantic labeling [J]. SPIE Proc. 1990, 1349: 476~485.
[51] Diani M., Corsini G and Baldacci A. Space-time processing for the detection of airborne targets in IR image sequences [J]. IEEE Proc. vision, Image Signal Proc., 2001, 148(3): 151~157.
[52]韩客松,王永成.红外序列图像中缓动点目标的流水线检测算法[J].系统工程与电子技术, 2000, 22(8):66~67.
[53] LOUISL. SCHARF, And HOWARD ELLIOT. Aspects of Dynamic Programming in Signal and Image Processing [J]. IEEE Trans. Automatic ATIC Control, 1981, 26(5): 1018~1029.
[54] Barniv Y. Dymamic programming solution for detection dim moving targets [J]. IEEE Trans. on Aerospace and Electronic Systems. 1985, 21(1): 144~156.
[55] Arnold J. and Shaw S. Efficient target tracking using dynamic programming [J]. IEEE Trans. on Aerospace and Electronic Systems. 1993, 29(1): 44~56.
[56] Tonissen S M, Evans R J. Performance of dynamic programming technique for track before detect [J]. IEEE Trans. on Aerospace and Electronic Systems, 1996, (10):1440~1451.
[57] Zhenfu Zhu, Zhongling Li, Haochen Liang, Bo Song and Anjun Pan. Grayscale morphological filter for small target detection [J]. SPIE Proc. 2000, 4130: 28~32.
[58] Johnston L A, Krishnamurthy V. Performance analysis of a dynamic programming track before detect algorithm [J] .IEEE Trans. on Aerospace and Electronic Systems, 2002, 38(1):228~242.
[59]陈华明,孙广富,卢焕章,陈尚峰.基于动态规划和置信度检验的小目标检测[J].系统工程与电子技术, 2003, 25(4): 472~476.
[60]田园,冯珊,周凯波.基于动态规划的多目标跟踪算法及实现[J].计算机应用与软件, 2000, 17(5): 31~35.
[61]孙广富,张兵,卢焕章.基于窗口预测匹配的序列图像点目标轨迹检测算法[J].国防科技大学学报, 2004, 26(2): 25~29.
[62] Blostein S. D. and Huang T. S. Detection small, moving targets in image sequences using sequential hypothesis testing [J]. IEEE Trans. Signal Proc., 1991, 39(7): 1611~1629.
[63] Blostein S. D. and Richardson H. S. A sequential detection approach to target tracking [J]. IEEE Trans. on Aerospace and Electronic Systems. 1991, 30(1):197~211.
[64]李红艳,吴成柯.一种快速序列图像低信噪比点目标的检测与跟踪方法[J].西安电子科技大学学报, 1999, 26(6): 732~736.
[65]廖斌,杨卫平,沈振康.基于多帧移位叠加的红外小目标检测方法[J].激光与红外工程, 2002, 31(2): 150~153.
[66] L. Yang, J. Yang and K. Yang. Adaptive Detection for Infrared Small Target under Sea-sky Complex Background [J]. Electronics Letters. 2004, 40(17): 1083~1085.
[67] Lei Yang, Jie Yang and Jianguo Ling. New Criterion to Evaluate the Complex Degree of Sea-sky Infrared Backgrounds [J]. Optical Engineering, 2005, 44(12): 126~401.
[68]李欣,赵亦工,郭伟.基于复杂度的自适应门限弱小目标检测方法.光子学报. 2009, 38(8): 2144~2149.
[69] Casasent D. P. and Smokelin J. S. Real, imaginary, and clutter Gabor filter fusion for detection with reduced false alarms [J]. Opt. Eng., 1994,33(7): 2255~2263.
[70] Robin N. S. and He H. Wavelet transform methods for object detection and recovery [J]. IEEE Trans. Image Proc. 1997, 6(1): 724-735.
[71]李红艳,吴成柯.一种基于小波和遗传算法的小目标检测算法[J].电子学报,2001, 29(4): 439~442.
[72] Boccignone G et al. Small target detection using wavelets [J]. IEEE Conf., ICPR 1998, 1776~1778.
[73]李国宽,彭嘉雄.基于小波变换的红外成像弱小目标检测方法[J].华中理工大学学报, 2000, 28(5): 69~71.
[74]刘钢,翟林培,贾新宇,刘明,匡海鹏.采用小波能量方法的海空背景中多目标检测与跟踪[J].光电工程, 2004, 31(11):16~19.
[75]牟松涛,苏锦鑫,吴建东.基于小波变换的红外图像弱小目标检测研究[J].红外与激光工程. 2004, 33(5):488~492.
[76]吕雁,史林,苏新主.基于小波和高阶累积量的红外弱小目标检测[J].红外技术, 2006, 28(12): 713~716.
[77]徐韶华,李红.基于小波提升框架及小波能量的红外弱目标检测方法[J].红外技术, 2006, 28(11): 669~672.
[78]朱梦宇,赵保军,韩月秋.一种实用的红外弱小目标检测跟踪处理机研究[J].激光与红外, 2002, 32(6): 407~409
[79]李欣,赵亦工,陈冰.基于分类的红外云层背景弱小目标检测方法[J].光学学报. 2009, 29(11): 3036~3042.
[80]李欣,赵亦工,陈冰.基于模糊分类的弱小目标检测方法[J].光学精密工程. 2009, 17(9): 2311~2320.
[81]刘涛,吕江北,王书宏,卢焕章.基于TDRNN的大气层外弹道式空间红外目标识别[J].电子与信息学报, 2010,32(1):80~85.
[82] DAV IDSON J L, HUMMER F. Morphology neural network: an introduction with applications [J]. IEEE Trans. on Signal Processing, 1993, 12(2): 177~210.
[83] YU Nong, WU Chang-yong, LI Fan-ming, et al. Morphological Neural Networks for Automatic Target Detection by Simulated Annealing Learning Algorithm [J]. Science in China, 2003, 46 (4): 262~288.
[84]李士民,郭立,朱俊株.复杂背景下弱小点目标的检测算法[J].电路与系统学报, 2002, 7(4):26~30.
[85] Mukesh A. Zaveri S. N. Merchant Uday B. Desai. Arbitrary Trajectories Tracking using Multiple Model Based Particle Filtering in Infrared Image Sequence [J]. Proceedings of IEEE International Conference on Information Technology: Coding and Computing. 2004, 603~607.
[86] Gaoyu Zhang, Jimin Liang, Heng Zhao and Wanhai Yang. Sequential Monte Carlo Implementation for Infrared/Radar Maneuvering Target Tracking [J]. Proceedings of IEEE the 6th World Congress on Intelligent Controland Automatio. 2006, 5066~5069.
[87]康莉,谢维信,黄敬雄.基于unscented粒子滤波的红外弱小目标跟踪[J].系统工程与电子技术, 2007, 29(1): 1~4.
[88] Sheng Zheng, Jian Liu, .Tin-Wen Tian. An SVM-Based Small Target Segmentation and Clustering Approach [J]. Proceedings of IEEE International Conference on Machine Learning and Cybernetics, 2004, 3318~3323
[89]郭伟,赵亦工,谢振华,李欣.基于非参数统计的云层背景描述与红外弱小目标检测[J].红外与毫米波学报. 2008, 27(5):383~388.
[90]杨磊.复杂背景条件下的红外弱小目标检测与跟踪算法研究.上海交通大学博士论文. 2006.
[91] Rafael C. Gonzalez and Richard E. Woods.数字图像处理,第二版.北京:电子工业出版社, 2003.
[92]胡文江.红外序列图像中弱小运动目标的检测算法研究.西安电子科技大学硕士论文. 2007.
[93] Chan D. S. K., Langman D. A. and Staver D. A. Spatial processing techniques for detection of small targets in IR clutter [J]. Proc. SPIE, 1990,1305: 53~62.
[94] Lily Rui Liang, Carl G.Loonry.Competitive fuzzy edge detection [J].Applied Soft Computing, 2003, 1(3): 123~137.
[95]朱红,赵亦工.基于背景自适应预测的红外弱小运动目标检测[J].红外与毫米波学报, 1999,18(4): 305~310.
[96]徐军,向健华,粱昌洪.最大化背景模型用于检测红外图像中的弱小目标[J].光子学报,2002, 32(12): 1483~1486
[97] Dalal N, Triggs B. Histograms of oriented gradients for human detection [J]. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2005, 886~893.
[98] Qiang Zhu, Mei-Chen Yeh, Kwang-Ting Cheng, Avidan, S. Fast Human Detection Using a Cascade of Histograms of Oriented Gradients[J]. Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2006, 1491~ 1498.
[99] Cheng-Hsiung Chuang, Shih-Shinh Huang, Li-Chen Fu, Pei-Yung Hsiao. Monocular multi-human detection using Augmented Histograms of Oriented Gradients [J]. International Conference on Pattern Recognition, IEEE, 2008, 1~4.
[100] Timo Ojala, Matti Pieti?inen, Senior Member, IEEE, and Topi M?enp?? Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns [J]. IEEE, Trans. on pattern analysis and machine intelligence. 2002, 24(7): 971~987.
[101]郭伟.复杂背景下红外目标检测与跟踪.西安电子科技大学博士论文.. 2009.
[102] Analog Devices. ADSP-TS101 Datasheet. Revision A. USA: Analog Devices Inc, 2003.
[103] Analog Devices. ADSP-TS101 TigerSHARC Processor Hardware Reference. Revision 1.1. USA: Analog Devices Inc, 2004.
[104] Analog Devices. ADSP-TS101 TigerSHARC Processor Programming Reference. Revision 1.0. USA: Analog Devices Inc, 2003.
[105] ALEXIS P. TZANNES DANA H. BROOKS. Detecting Small Moving Objects Using Temporal Hypothesis Testing [J], Trans. on Aerospace and ElectronicSystems, 2002, 38(2): 57~586.
[106] Biyin Zhang, Tianxu Zhang, Kun Zhang, Zhao Cheng, Zhiguo Cao. Adaptive Rectification Filter for Detecting Small IR Targets [J]. Aerospace and Electronic Systems Magazine, IEEE, 2007, 22(8): 20 ~26.
[107] Xu Ying. Small Moving Target Detection in Infrared Image Sequences [J].Infrared Technology. 2002, 24(6): 27～30.
[108] Zhang Xiaoping, Mita D D. Segmentation of bright targets using wavelets and adaptive shareholding [J]. IEEE Trans. on Image Processing. 2001,10(7): 1020～1030.
[109] Cao,Yuan; Liu, Ruiming; Yang, Jie Small Target Detection Using Two-Dimensional Least Mean Square (TDLMS) Filter Based on Neighborhood Analysis [J]. International Journal of Infrared and Millimeter Waves, 2007, 29(2): 188~200.
[110] W. Zhang, M. Y. Cong, L. P. Wang. Algorithm for optical weak small targets detection and tracking: Review [J]. IEEE International Conference on Neural Networks & Signal Processing. 2003: 643~647.
[111] Chapple, P.B. Bertilone, D.C. Caprari, R.S. Newsam, G.N.; Stochastic Model-Based Processing for Detection of Small Targets in Non-Gaussian Natural Imagery [J]. Trans. On image processing, IEEE, 2001, 10(4): 554~564.
[112] LEONOV S. Nonparametric methods for clutter removal [J]. IEEE Trans. on Aerospace and Electronic Systems, 2001, 37(3): 832~847.
[113] L. A. Johnston, V. Krishnamuthy. Performance of a dynamic programming track before detect algorithm [J]. IEEE Trans. on Aerospace and Electronic Systems.2002, 38(1): 228~42.
[114] V.Ronda, W.L.New, M.H.Tan, M.H.Er. Adaptive threshold-based spatio-temporal filtering techniques for detection of small targets [J]. IEEE Transactions on Aerospace and Electronic Systems, 2001, 37(3): 832~848.
[115] Gao Yinghui, Li Jicheng, Shen Zhenkang. Detection of Moving Small Target in IR Clutter Background Containing Sea and Sky Areas [J]. Proceedings of SPIE, 2005, 5640: 341~349.
[116] Mukesh A. Zaveri, S.N. Merchant and Uday B. Desai: Multiple single pixel dim target detection in infrared image sequence [J]. Proc. IEEE International Symposiumon Circuits and Systems, 2003(2): 380~383
[2]彭嘉雄,周文琳.红外背景抑制与小目标分割检测[J].电子学报,1999, 27(12): 47~51.
[3] Wang T,Wang C L. A new two dimensional block adaptive FIR filtering algorithm and its application to image restoration [J]. IEEE Trans. On Image Processing, 1998, 7(2): 777~780.
[4] Ohki M. and Hashiguchi S. Two-dimensional LMS adaptive filters [J]. IEEE Trans. Consum. Electron. 1991, 37(1): 66~73.
[5] Soni T., Zeidler J.R. and Ku W.H. Performance evaluation of 2-D adaptive prediction filters for detection of small targets in image data [J]. IEEE Trans. Image Proc. 1993, 2(3): 327~339.
[6]朱红,赵亦工.基于背景自适应预测的红外弱小目标检测[J].红外与毫米波学报, 1999, 18(4): 305-310.
[7] Liu, Jin; Ji, Hong-Bing. Improved Background Prediction Algorithm for IRSmall Targets Detection [J]. IEEE Industrial Electronics & Applications, 2009, 394 ~ 398.
[8]聂洪山,杨卫平,沈振康.基于Wiener滤波的小目标检测方法[J].红外与激光工程, 2006, 32(5): 476~478.
[9] Xu Kaiyu; Hu Wenhua; Zhou Weina; Zheng Huayao. Target Detection Based on The Artificial Neural Network Technology [J]. IEEE, Control, Automation, Robotics and Vision, 2006, 1~5.
[10]尹超,向健勇,韩建栋.一种基于区域背景预测的红外弱小目标检测方法[J].红外技术, 2004, 26(6): 62~65.
[11]徐军,向健华,粱昌洪.最大化背景模型用于检测红外图像中的弱小目标[J].光子学报, 2002, 31(12): 1483~1486
[12]何伟,晋兆虎,张玲.一种改进的利用背景检测弱小目标的方法[J].重庆大学学报, 2005, 28(7): 64~66.
[13]李欣,赵亦工,陈冰.基于方向直方图区域分类的弱小目标检测方法.兵工学报. (一审已通过)
[14] Tom V.T, Peli.T, Leung M, et.al. Morphology-based algorithm for point target detection in infrared backgrounds [J]. SPIE. 1993, 1954: 2~11.
[15]叶斌,彭嘉雄,卢汉清.基于顺序形态滤波的红外运动小目标检测[J].数据采集与处理, 2001,16(3): 315~319.
[16]叶斌,彭嘉雄.基于形态学Top-hat算子的小目标检测方法[J].中国图像图形学报,2002, 7(7): 638~642.
[17]韩建涛,张月,陈曾平.天文图像序列中弱目标的实时检测算法[J].光电工程, 2005, 32(12): 1~4.
[18]苏新主,姬红兵,高新波.一种基于数学形态学的红外弱小目标检测方法[J].红外与激光工程, 2004,33(3): 307~310.
[19]吴巍,彭嘉雄,王海晖.红外图像序列小目标的特性分析与检测[J].红外与激光工程, 2002,31(2): 146~149.
[20]程德杰,李晓峰,李在铭.基于场景运动分析的弱小目标形态学检测方法[J].电子测量与仪器学报, 2006, 20(3): 1~5.
[21]曾明,李建勋.基于自适应形态学Top-Hat滤波器的红外弱小目标检测方法[J].上海交通大学学报, 2006, 40(1): 90~93.
[22] F. Cheng and A. N. Venetsanopoulos. Adaptive Morphological Filter for Image Processing [J]. IEEE Transactions on Image Processing. 1992. 1(4): 533-539.
[23] Jian-Nan Chi, Ping Fu, Dong-Shu Wang, Xin-He Xu. A Detection Method of Infrared Image Small Target Based on Order Morphology Transformation and Image Entropy Difference [J]. IEEE Machine Learning and Cybernetics, 2005, 5111~5116.
[24] G.B. Giannakos and M.K. Tsatsanis. Signal detection and classification using matched filtering and higher-order statistics [J], IEEE Trans. Acoustics, Speech, Signal Processing, 1990, 38: 1284 ~ 1296.
[25] Bin Wu, Hongbing Ji. A Novel Algorithm for Point-Target Detection Based on Third-Order Cumulant in Infrared Image [J]. 2006, IEEE Proc. ICSP,
[26]吕雁.基于高阶累积量的红外图像时域检测[J].激光与红外, 2007, 37(2): 178~180.
[27] SMITH S M. Edge Thinning Used in the SUSAN Edge Detector: Internal Technical Reports TR95SMS5[R]. Farnborough, Hampshire, UK: Defence Research Agency, 1995.
[28] SMITH.S.M, BRADY.J.M. SUSAN-A New Approach to Low Level Image Processing: Internal Technical Report TR95SMS1c[R]. Farnborough, Hampshire, UK: Defence Research Agency, 1995.
[29]毕务忠,严高师.基于改进SUSAN原则的小目标检测算法[J].激光与红外, 2006, 36(6): 504~507.
[30]周进,吴钦章.深空大视场弱小目标的实时检测方法[J].光学技术, 2006, 32(1): 134~137
[31]袁慧晶,王涌天.基于改进SUSAN原则的小目标检测算法[J].激光与红外, 2006, 36(6): 504~507.
[32]郑敏,张启衡.弱小目标检测与跟踪算法[J].光电工程, 2002, 29(4): 10~12.
[33] Zeidler J R, Soni T, Ku W H. Recursive Estimation Techniques For Detection Of Small Objects In Infrared Image Data [J]. IEEE Proceedings International Conference on Acoustics Speech and Signal Processing. 1992, 581~584.
[34] Thomas V, De Mars J. Adaptive dim point target detection and tracking in infrared images [J]. 1988, AD-A204 845.
[35] Himayat N, Kassam S A. A Structure for Adaptive Order Statistics Filtering [J].IEEE Transactions on Image Processing. 1994, 3(3): 265~280.
[36] Kotropoulos C, Pitas I. Adaptive LMS L-Filters for Noise Suppression in Images [J].IEEE Transactions on Image Processing 1996. 5(12): 1596~1609.
[37] Muller M. Saliency Measures in Cluttered IR Images for ATR [J]. SPIE, 1999. 3699: 150~154.
[38] Barnett J. Statistical analysis of media subtraction filtering with application to point target detection in infrared backgrounds [J]. Proc. SPIE, 1989, 1050~1018.
[39] Muller M. Saliency Measures in Cluttered IR Images for ATR [J]. SPIE, 1999, 3699: 150~154.
[40] Scharfand L L, Friedlander B. Matched subspace detectors [J]. IEEE Trans. On Sig.Proc. 1994, 42(8): 2146~2156.
[41] Deshpande S D, Er M H, Ronda V. Max-mean and max-median filters for detection of small targets [J]. SPIE. 1999, 3809: 74-83.
[42] Li Junwei, Zhu Zhenfu, Liu Zhongling. Small target detection from image sequences using improved recursive max-mean filter [J]. SPIE, 2001, 4554: 20~24.
[43] Reed T S, Gagliardi R M, Shao H M. Application of three dimensional filtering to moving target detection [J]. IEEE Trans. on Aerospace and Electronic Systems, 1983, AES-19(6): 123~126.
[44] Reed I. S. Gagliardi R. M. and Stotts L. Optical moving target detection with 3-D match filtering [J]. IEEE Trans. on Aerospace and Electronic Systems, 1988, 24(4): 327~336.
[45]刘金根,吉会云,周灿,刘兴建,姜德生.复杂背景下图像中弱小目标的分割和提取算法[J].武汉理工大学学报, 2006,28(12): 127~129.
[46]张天序,赵广州,王飞,朱光喜.一种快速递归红外舰船图像分割新算法[J].红外毫米波学报, 2006, 25(4): 295~300.
[47]刘建华,毕笃彦,叶广强.基于目标模型的红外弱小目标预检测[J].空军工程大学学报, 2006, 7(5): 36~38.
[48] Bronskill J F, Hepburn J S A, Au W K. A knowledge-based approach to the detection, tracking, and classification of target formations, in infrared image sequences [J]. Proc. IEEE CVPR. 1989, 153~158.
[49] Wang G etal. A pipeline algorithm for detection and tracking of pixel-sized target trajectories [J]. SPIE, 1990, 1305: 167~178.
[50] Lavagetto F. Automatic target detection in infrared sequences through semantic labeling [J]. SPIE Proc. 1990, 1349: 476~485.
[51] Diani M., Corsini G and Baldacci A. Space-time processing for the detection of airborne targets in IR image sequences [J]. IEEE Proc. vision, Image Signal Proc., 2001, 148(3): 151~157.
[52]韩客松,王永成.红外序列图像中缓动点目标的流水线检测算法[J].系统工程与电子技术, 2000, 22(8):66~67.
[53] LOUISL. SCHARF, And HOWARD ELLIOT. Aspects of Dynamic Programming in Signal and Image Processing [J]. IEEE Trans. Automatic ATIC Control, 1981, 26(5): 1018~1029.
[54] Barniv Y. Dymamic programming solution for detection dim moving targets [J]. IEEE Trans. on Aerospace and Electronic Systems. 1985, 21(1): 144~156.
[55] Arnold J. and Shaw S. Efficient target tracking using dynamic programming [J]. IEEE Trans. on Aerospace and Electronic Systems. 1993, 29(1): 44~56.
[56] Tonissen S M, Evans R J. Performance of dynamic programming technique for track before detect [J]. IEEE Trans. on Aerospace and Electronic Systems, 1996, (10):1440~1451.
[57] Zhenfu Zhu, Zhongling Li, Haochen Liang, Bo Song and Anjun Pan. Grayscale morphological filter for small target detection [J]. SPIE Proc. 2000, 4130: 28~32.
[58] Johnston L A, Krishnamurthy V. Performance analysis of a dynamic programming track before detect algorithm [J] .IEEE Trans. on Aerospace and Electronic Systems, 2002, 38(1):228~242.
[59]陈华明,孙广富,卢焕章,陈尚峰.基于动态规划和置信度检验的小目标检测[J].系统工程与电子技术, 2003, 25(4): 472~476.
[60]田园,冯珊,周凯波.基于动态规划的多目标跟踪算法及实现[J].计算机应用与软件, 2000, 17(5): 31~35.
[61]孙广富,张兵,卢焕章.基于窗口预测匹配的序列图像点目标轨迹检测算法[J].国防科技大学学报, 2004, 26(2): 25~29.
[62] Blostein S. D. and Huang T. S. Detection small, moving targets in image sequences using sequential hypothesis testing [J]. IEEE Trans. Signal Proc., 1991, 39(7): 1611~1629.
[63] Blostein S. D. and Richardson H. S. A sequential detection approach to target tracking [J]. IEEE Trans. on Aerospace and Electronic Systems. 1991, 30(1):197~211.
[64]李红艳,吴成柯.一种快速序列图像低信噪比点目标的检测与跟踪方法[J].西安电子科技大学学报, 1999, 26(6): 732~736.
[65]廖斌,杨卫平,沈振康.基于多帧移位叠加的红外小目标检测方法[J].激光与红外工程, 2002, 31(2): 150~153.
[66] L. Yang, J. Yang and K. Yang. Adaptive Detection for Infrared Small Target under Sea-sky Complex Background [J]. Electronics Letters. 2004, 40(17): 1083~1085.
[67] Lei Yang, Jie Yang and Jianguo Ling. New Criterion to Evaluate the Complex Degree of Sea-sky Infrared Backgrounds [J]. Optical Engineering, 2005, 44(12): 126~401.
[68]李欣,赵亦工,郭伟.基于复杂度的自适应门限弱小目标检测方法.光子学报. 2009, 38(8): 2144~2149.
[69] Casasent D. P. and Smokelin J. S. Real, imaginary, and clutter Gabor filter fusion for detection with reduced false alarms [J]. Opt. Eng., 1994,33(7): 2255~2263.
[70] Robin N. S. and He H. Wavelet transform methods for object detection and recovery [J]. IEEE Trans. Image Proc. 1997, 6(1): 724-735.
[71]李红艳,吴成柯.一种基于小波和遗传算法的小目标检测算法[J].电子学报,2001, 29(4): 439~442.
[72] Boccignone G et al. Small target detection using wavelets [J]. IEEE Conf., ICPR 1998, 1776~1778.
[73]李国宽,彭嘉雄.基于小波变换的红外成像弱小目标检测方法[J].华中理工大学学报, 2000, 28(5): 69~71.
[74]刘钢,翟林培,贾新宇,刘明,匡海鹏.采用小波能量方法的海空背景中多目标检测与跟踪[J].光电工程, 2004, 31(11):16~19.
[75]牟松涛,苏锦鑫,吴建东.基于小波变换的红外图像弱小目标检测研究[J].红外与激光工程. 2004, 33(5):488~492.
[76]吕雁,史林,苏新主.基于小波和高阶累积量的红外弱小目标检测[J].红外技术, 2006, 28(12): 713~716.
[77]徐韶华,李红.基于小波提升框架及小波能量的红外弱目标检测方法[J].红外技术, 2006, 28(11): 669~672.
[78]朱梦宇,赵保军,韩月秋.一种实用的红外弱小目标检测跟踪处理机研究[J].激光与红外, 2002, 32(6): 407~409
[79]李欣,赵亦工,陈冰.基于分类的红外云层背景弱小目标检测方法[J].光学学报. 2009, 29(11): 3036~3042.
[80]李欣,赵亦工,陈冰.基于模糊分类的弱小目标检测方法[J].光学精密工程. 2009, 17(9): 2311~2320.
[81]刘涛,吕江北,王书宏,卢焕章.基于TDRNN的大气层外弹道式空间红外目标识别[J].电子与信息学报, 2010,32(1):80~85.
[82] DAV IDSON J L, HUMMER F. Morphology neural network: an introduction with applications [J]. IEEE Trans. on Signal Processing, 1993, 12(2): 177~210.
[83] YU Nong, WU Chang-yong, LI Fan-ming, et al. Morphological Neural Networks for Automatic Target Detection by Simulated Annealing Learning Algorithm [J]. Science in China, 2003, 46 (4): 262~288.
[84]李士民,郭立,朱俊株.复杂背景下弱小点目标的检测算法[J].电路与系统学报, 2002, 7(4):26~30.
[85] Mukesh A. Zaveri S. N. Merchant Uday B. Desai. Arbitrary Trajectories Tracking using Multiple Model Based Particle Filtering in Infrared Image Sequence [J]. Proceedings of IEEE International Conference on Information Technology: Coding and Computing. 2004, 603~607.
[86] Gaoyu Zhang, Jimin Liang, Heng Zhao and Wanhai Yang. Sequential Monte Carlo Implementation for Infrared/Radar Maneuvering Target Tracking [J]. Proceedings of IEEE the 6th World Congress on Intelligent Controland Automatio. 2006, 5066~5069.
[87]康莉,谢维信,黄敬雄.基于unscented粒子滤波的红外弱小目标跟踪[J].系统工程与电子技术, 2007, 29(1): 1~4.
[88] Sheng Zheng, Jian Liu, .Tin-Wen Tian. An SVM-Based Small Target Segmentation and Clustering Approach [J]. Proceedings of IEEE International Conference on Machine Learning and Cybernetics, 2004, 3318~3323
[89]郭伟,赵亦工,谢振华,李欣.基于非参数统计的云层背景描述与红外弱小目标检测[J].红外与毫米波学报. 2008, 27(5):383~388.
[90]杨磊.复杂背景条件下的红外弱小目标检测与跟踪算法研究.上海交通大学博士论文. 2006.
[91] Rafael C. Gonzalez and Richard E. Woods.数字图像处理,第二版.北京:电子工业出版社, 2003.
[92]胡文江.红外序列图像中弱小运动目标的检测算法研究.西安电子科技大学硕士论文. 2007.
[93] Chan D. S. K., Langman D. A. and Staver D. A. Spatial processing techniques for detection of small targets in IR clutter [J]. Proc. SPIE, 1990,1305: 53~62.
[94] Lily Rui Liang, Carl G.Loonry.Competitive fuzzy edge detection [J].Applied Soft Computing, 2003, 1(3): 123~137.
[95]朱红,赵亦工.基于背景自适应预测的红外弱小运动目标检测[J].红外与毫米波学报, 1999,18(4): 305~310.
[96]徐军,向健华,粱昌洪.最大化背景模型用于检测红外图像中的弱小目标[J].光子学报,2002, 32(12): 1483~1486
[97] Dalal N, Triggs B. Histograms of oriented gradients for human detection [J]. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2005, 886~893.
[98] Qiang Zhu, Mei-Chen Yeh, Kwang-Ting Cheng, Avidan, S. Fast Human Detection Using a Cascade of Histograms of Oriented Gradients[J]. Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2006, 1491~ 1498.
[99] Cheng-Hsiung Chuang, Shih-Shinh Huang, Li-Chen Fu, Pei-Yung Hsiao. Monocular multi-human detection using Augmented Histograms of Oriented Gradients [J]. International Conference on Pattern Recognition, IEEE, 2008, 1~4.
[100] Timo Ojala, Matti Pieti?inen, Senior Member, IEEE, and Topi M?enp?? Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns [J]. IEEE, Trans. on pattern analysis and machine intelligence. 2002, 24(7): 971~987.
[101]郭伟.复杂背景下红外目标检测与跟踪.西安电子科技大学博士论文.. 2009.
[102] Analog Devices. ADSP-TS101 Datasheet. Revision A. USA: Analog Devices Inc, 2003.
[103] Analog Devices. ADSP-TS101 TigerSHARC Processor Hardware Reference. Revision 1.1. USA: Analog Devices Inc, 2004.
[104] Analog Devices. ADSP-TS101 TigerSHARC Processor Programming Reference. Revision 1.0. USA: Analog Devices Inc, 2003.
[105] ALEXIS P. TZANNES DANA H. BROOKS. Detecting Small Moving Objects Using Temporal Hypothesis Testing [J], Trans. on Aerospace and ElectronicSystems, 2002, 38(2): 57~586.
[106] Biyin Zhang, Tianxu Zhang, Kun Zhang, Zhao Cheng, Zhiguo Cao. Adaptive Rectification Filter for Detecting Small IR Targets [J]. Aerospace and Electronic Systems Magazine, IEEE, 2007, 22(8): 20 ~26.
[107] Xu Ying. Small Moving Target Detection in Infrared Image Sequences [J].Infrared Technology. 2002, 24(6): 27～30.
[108] Zhang Xiaoping, Mita D D. Segmentation of bright targets using wavelets and adaptive shareholding [J]. IEEE Trans. on Image Processing. 2001,10(7): 1020～1030.
[109] Cao,Yuan; Liu, Ruiming; Yang, Jie Small Target Detection Using Two-Dimensional Least Mean Square (TDLMS) Filter Based on Neighborhood Analysis [J]. International Journal of Infrared and Millimeter Waves, 2007, 29(2): 188~200.
[110] W. Zhang, M. Y. Cong, L. P. Wang. Algorithm for optical weak small targets detection and tracking: Review [J]. IEEE International Conference on Neural Networks & Signal Processing. 2003: 643~647.
[111] Chapple, P.B. Bertilone, D.C. Caprari, R.S. Newsam, G.N.; Stochastic Model-Based Processing for Detection of Small Targets in Non-Gaussian Natural Imagery [J]. Trans. On image processing, IEEE, 2001, 10(4): 554~564.
[112] LEONOV S. Nonparametric methods for clutter removal [J]. IEEE Trans. on Aerospace and Electronic Systems, 2001, 37(3): 832~847.
[113] L. A. Johnston, V. Krishnamuthy. Performance of a dynamic programming track before detect algorithm [J]. IEEE Trans. on Aerospace and Electronic Systems.2002, 38(1): 228~42.
[114] V.Ronda, W.L.New, M.H.Tan, M.H.Er. Adaptive threshold-based spatio-temporal filtering techniques for detection of small targets [J]. IEEE Transactions on Aerospace and Electronic Systems, 2001, 37(3): 832~848.
[115] Gao Yinghui, Li Jicheng, Shen Zhenkang. Detection of Moving Small Target in IR Clutter Background Containing Sea and Sky Areas [J]. Proceedings of SPIE, 2005, 5640: 341~349.
[116] Mukesh A. Zaveri, S.N. Merchant and Uday B. Desai: Multiple single pixel dim target detection in infrared image sequence [J]. Proc. IEEE International Symposiumon Circuits and Systems, 2003(2): 380~383