基于红外图像信息融合的目标检测和识别技术研究
|
摘要
随着精确制导武器的发展，人们希望在小目标阶段，即目标与传感器相距较远时能检测和识别出目标，以进行有效的防御和攻击。而从红外图像传感器获得的图像经过预处理之后，得到的仅是一些点或斑点目标。此时如果只凭借于单一的图像传感器所获得的图像序列信息检测目标的可靠性往往不高，同时也很难准确地把拟攻击的真目标从各种虚假目标和随机干扰中区别出来。而小目标的高可靠性检测和识别对后续的目标捕获和跟踪是极其关键的。基于上述认识，本文对低信噪比小目标的高精度检测和识别进行了研究，提出了基于多传感器信息融合的小目标检测和识别算法。该算法的具体思路为：首先对来自两个不同波段(波长分别为3～5μm，8～12μm)的红外图像序列进行双模融合检测和跟踪，获得目标的速度信息和灰度信息；然后采用多传感器信息融合理论对小目标的双模图像信息进行了融合识别和分类处理，以识别出拟攻击的真目标。
论文的研究工作可概括如下：
第一章介绍了本课题的应用背景、多传感器信息融合理论及该理论在目标检测和识别中的应用；然后给出了几种传统的目标检测和融合识别方法。
第二章在详细讨论了各种单帧和多帧目标检测技术的基础上，提出了双波段融合检测方法，并对该算法的检测效果进行了理论分析和试验比较。
第三章提出了一种基于D-S证据理论的多传感器时间—空间信息融合识别算法，并采用该方法对实际的双波段图像进行目标融合识别仿真计算，较大地提高了强干扰条件下的红外小目标识效率和可靠性。
第四章在讨论模糊综合理论及其在多传感器决策级信息融合中的应用的基础上，提出了一种基于模糊综合的红外图像目标融合识别方法，然后采用该算法对双波段图像序列进行目标识别仿真试验，并给出了该算法的识别效率和改进方法。
在本文最后，对基于多红外双色图像信息融合的红外小目标检测和识别算法作了总结，并简单分析了融合检测和识别算法的实用性和应用前景。
As the development of the modern remote distance tacking weapon, we hope to recognize target in the stage of small target, that is the stage in which the distance between target and sensor is very far, so that to defend and tack effectively. But we can only get the information of kinematics and gray of the dot targets by pre-processing the image serials from IR sensors in the stage of target detection .It is very difficult to distinguish the real target from all kinds of virtual target and stochastic clutter if we use only the information from single sensor. But the precise recognition in the stage of small target is very important for the following target capturing and target tracking. The paper has done some works as the follows on the problem: As for the problem that how to improve the precision of IR small targets detection and the probability of recognition, has studied the imaging characteristic of IR dual band imaging system and presented the algorithm of detection and recognition for IR small tar
gets on the base of multi-sensor information fusion.The whole algorithm's idea consists of two aspects: the first, processed the IR image sequences from the two different IR bands by detecting on the fusion of dual band and precise tracking to get the information of velocity and grayscale of targets; the second, fusing the information of the small targets form IR dual band image using the theory for multi-sensor information fusion to identify the real target to attack at the high probability.
In the first chapter, a general introduction is given on the background for applying of the project and the theory for multi-sensor information fusion and the application of the theory to detection and recognition of IR small targets. Then given a concise presentation for several kinds of target detection and fusion recognition.
In the second chapter, a detailed discussion is presented here for the IR small detecting technique based on the data fusion of dual band IR image information, then to preprocess images using the algorithm. The results are satisfying.
In the third chapter, the method of IR small target based multi-sensor temporal-spatial information fusion(applying D-S evidence combination theory). And fused the real image sequences from the IR dual band imaging sensors using the method, improved the probability and performance of IR small target under strong clutter greatly.
In the fourth chapter,presented a method based on fuzzy combination IR image target fusion recognition ,then applied the method to the image sequences from the IR dual band imaging system for target recognition, and discussed the recognition performance and the improving ways for the method.
In the end of the paper, summarized the algorithms of IR small target detection and recognition based
论文的研究工作可概括如下：
第一章介绍了本课题的应用背景、多传感器信息融合理论及该理论在目标检测和识别中的应用；然后给出了几种传统的目标检测和融合识别方法。
第二章在详细讨论了各种单帧和多帧目标检测技术的基础上，提出了双波段融合检测方法，并对该算法的检测效果进行了理论分析和试验比较。
第三章提出了一种基于D-S证据理论的多传感器时间—空间信息融合识别算法，并采用该方法对实际的双波段图像进行目标融合识别仿真计算，较大地提高了强干扰条件下的红外小目标识效率和可靠性。
第四章在讨论模糊综合理论及其在多传感器决策级信息融合中的应用的基础上，提出了一种基于模糊综合的红外图像目标融合识别方法，然后采用该算法对双波段图像序列进行目标识别仿真试验，并给出了该算法的识别效率和改进方法。
在本文最后，对基于多红外双色图像信息融合的红外小目标检测和识别算法作了总结，并简单分析了融合检测和识别算法的实用性和应用前景。
As the development of the modern remote distance tacking weapon, we hope to recognize target in the stage of small target, that is the stage in which the distance between target and sensor is very far, so that to defend and tack effectively. But we can only get the information of kinematics and gray of the dot targets by pre-processing the image serials from IR sensors in the stage of target detection .It is very difficult to distinguish the real target from all kinds of virtual target and stochastic clutter if we use only the information from single sensor. But the precise recognition in the stage of small target is very important for the following target capturing and target tracking. The paper has done some works as the follows on the problem: As for the problem that how to improve the precision of IR small targets detection and the probability of recognition, has studied the imaging characteristic of IR dual band imaging system and presented the algorithm of detection and recognition for IR small tar
gets on the base of multi-sensor information fusion.The whole algorithm's idea consists of two aspects: the first, processed the IR image sequences from the two different IR bands by detecting on the fusion of dual band and precise tracking to get the information of velocity and grayscale of targets; the second, fusing the information of the small targets form IR dual band image using the theory for multi-sensor information fusion to identify the real target to attack at the high probability.
In the first chapter, a general introduction is given on the background for applying of the project and the theory for multi-sensor information fusion and the application of the theory to detection and recognition of IR small targets. Then given a concise presentation for several kinds of target detection and fusion recognition.
In the second chapter, a detailed discussion is presented here for the IR small detecting technique based on the data fusion of dual band IR image information, then to preprocess images using the algorithm. The results are satisfying.
In the third chapter, the method of IR small target based multi-sensor temporal-spatial information fusion(applying D-S evidence combination theory). And fused the real image sequences from the IR dual band imaging sensors using the method, improved the probability and performance of IR small target under strong clutter greatly.
In the fourth chapter,presented a method based on fuzzy combination IR image target fusion recognition ,then applied the method to the image sequences from the IR dual band imaging system for target recognition, and discussed the recognition performance and the improving ways for the method.
In the end of the paper, summarized the algorithms of IR small target detection and recognition based
引文
[1] Jiah-Yeu Chen , Irving S.Reed. A detection algorithm for optical targets in clutter. IEEE Transactions on Aerospace and Electronic Systems. AES-23(January 1987) .
[2] Margalit.A., Reed.I.S., and Gagliardi. R. M. Adaptive optical target detection using correlated images. IEEE Transactions on Aerospace and Electronic Systems. AES-21(May 1985) .
[3] Hunt.B.G , and Cannon.T.M. Nonstationary assumptions of Gaussian models of images. IEEE Transaction on Systems ,Man and Cybernetics.(Dec. 1976)
[4] Kelly.E.L. Adaptive Detection in non-stationary interference. IEEE Transactions on Aerospace and Electronic Systems. AES-21(May 1986) .
[5] Mohiy.M.H. ,and David WT The Two-Dimensional Adaptive LMS(TDLMS) Algorithm, IEEE Transactions on Circuits and Systems. Vol.3 5 ,No.5 ,(May 1988)
[6] Miller.K.S. Multidimentional Gaussian Distribution.. New York: Wiley , 1964
[7] W.K.Pratt. Digital Image Processing. New York: Wiley , 1978
[8] Papoulis.A. Probability,Random Variables ,and Stochastic Processes. New York: McGraw-Hill, 1965
[9] Cramer. H. Mathematical Methods of Statistics. Princeton ,N.J.: Princeton University Press, 1958
[10] PHTL IPL BOGLER .Shafer-Dempster Reasoning With Applications to Multi-Sensor Target Identification. IEEE Transaction On System,Man, And Cybernetics, Vol.NO.6,November 1987．
[11] RAMANARYANAN VISWANATHAN AND PRAMOD KVARSHNEY.Distributed Detection With Multiple Sensors:Part I--Fundamentals .Proceedings of The IEEE, Vol.85,NO.1,January 1997．
[12] HENDRIK JAMES ANTONIS SE. Unsupervised Credit Assignment in Knowledge-Based Sensor Fusion .Systems. IEEE Transactions On Systems,Man, Cybernetics, Vol.20． No. 5, September/October 1990．
[13] W.K Pratt.IR Target Detection and Clutter Rejection Using the 1MMPDAF
Proc.SPIE Conf.Signal and Data Processing of Small Targets,Orlando,FL. April, 1998．
[14] FRANCIS MARTINERIE THOMSON SINTRA ASM.Data Fusion and Tracking Using HMMs in a Distributed Sensor Network.lEEE Transactions On Aerospace and Electronic Systems Vol.33,No.l January 1997．
[15] Chee-Yee Chong Booz Allen&Hamilton,Inc.Shozo Mori and William H.Barker Architectures and Algorithms for Track Association and Fusion. IEEE AES Systems Magazine,January 2000．
[16] RAM-NANDAN P.SINGH,WILLIAM H.BAILEY.Fu^ Logic Applications to Multi-Sensor Multi-Target Corrleation.lEEE Transactions On Aerospace and Electronic Systems Vol.33,No.3 July 1997．
[17] LAWRENCE A.KLEIN A Boolean Algebra Approach to Multiple Sensor Voting Fusion.IEEE Transaction On Aerospace and Electronic Systems Vol.29,No.2 April 1993．
[18] REN C.LUO,MICHAEL G KAY Multi-Sensor Integration and Fusion in Intelligent Systems. IEEE Transactions On Systems,Man,Cybernetics, Vol. 19,No. 5, September/October 1989
[19] STEPHEN C.POHLIG.An Algorithm for Detection of Moving Optical Targets IEEE Transaction On Aerospace and Electronic Systems Vol.AES-25,No. 1 January 1989
[20] J.N. SANDERS-REED. Maximum Likelihood Detection of Unresolved Moving Targets.IEEE Transactions On Aerospace and Electronic Systems Vol.34,No.3 July 1998
[21] M.F.Fernandez,A. Aridgides,and D.Bray. Detecting and Tracking Low-Observable Targets Using IK SPIE Vol. 1305 Signal and Data Processing of Small Targets 1990/193
[22] Dennis M.Buede,and Paul Girardi. A Target Identification Comparison of Bayesian and Dempster-Shafer Multi-Sensor Fusion. IEEE Transactions On Systems,Man,Cybernetics,Vol.27． No.5,September 1997
[23] ZEINDDINE CHAIR AND PRAMOD K.VARSHNEY. Distributed Bayesian Hypothesis Testing With Distributed Data Fusion. IEEE Transactions On Systems,Man, Cybernetics, Vol. 18． No.5,September/October 1997
[24] Isabelle Bloch. Information Combination Operators for Data Fusion: A Comparative Review With Classification.IEEE Transactions On Systems, Man,
Cybernetics,Vol.26.No.1.January 1996
[25]HYOUNGJUN IM TEAJEONG KIM. Optimization of Multi-frame Target Detection Schemes. IEEE Transactions On Aerospace and Electronic Systems Vol.35,No.1 January 1999
[25]Belur V.Dasarathy. Decision Fusion Strategies in Multi-Sensor Environments IEEE Transactions On Systems, Man, Cybernetics,Vol.21.No.5.September/October 1991
[26]耿立恩 潘旭峰 李晓雷 祝嘉光“Dempster-Shafer证据理论在数据融合中的应用”，《北京理工大学学报》1997．2
[27]王慧频 孙仲康“D-S证据结合方法用于目标和诱饵的识别”，《系统工程与电子技术》1996。10
[28]王国宏 毛士艺 何友“基于模糊综合的目标识别时空数据融合算法”，《模式识别与人工智能》1997．1
[29]龚建勇 丁明跃 周成平“基于玫瑰线扫描双色信息融合目标识别方法”，《系统工程与电子技术》1998．7
[30]李宏 孙仲康“一种具有信息融合功能的模糊推理方法”，《系统工程与电子技术》1998．1
[31]廖朝佩，“自动目标识别器的发展现状及应用分析”，《红外与激光技术》1992．2
[32]杨应槐，“小目标探测技术的研究”，《红外与激光技术》1989．1
[33]张孝霖等，“红外背景中点与近点源目标探测的卷积减法滤波算法”，《红外与激光技术》1995．8
[34]张孝霖等，“在海域红外杂波背景中小目标探测的空间匹配滤波技术”，《红外与毫米波学报》1994．4
[35]曹建清等，“一种检测红外点目标的方法”，《红外与毫米波学报》1995．4
[36]彭嘉雄等，“弱目标检测的图像流法”，《红外与激光工程》1996．8
[37]刘建轶等，“低信噪比序列图像中目标检测、识别及跟踪算法的研究”，《红与激光工程》1996．8
[39]梁民等，“点目标自动识别”，《国防科技大学学报》1991．6
[40]宋柳平等，“红外制导系统中的点目标检测技术”，《国防科技大学学报》1994．1
[41]B．维德罗等，《自适应信号处理》，四川大学出版社，1989
[42]周概容，《概率论与数理统计》，北京高等教育出版社，1984
[43]孙即祥，《数字图像处理》，河北教育出版社，1993
[44]关华“红外图像中低信噪比小目标的检测与跟踪方法研究”，国防科技大学博士学位论文，1995．10
[45]李吉成“红外起伏背景条件下的小目标检测技术研究”，国防科技大学博士学位论文，1998．8
[46]何友 王国宏等《多传感器信息融合及其应用》电子工业出版社 2000
[47]刘同明 夏祖勋 解洪成《数据融合技术及其应用》国防工业出版社 1998
[48]康耀红《数据融合理论与应用》西安电子科技大学出版社 1997
[49]刘晋寅 吴孟达《模糊理论及其应用》国防科技大学出版社 1998
[50]朱允民《多传感器分布式统计判决》科学出版社 2000
[51]边肇祺 张学工《模式识别》清华大学出版社 2000
[52]陆正刚 杨杰“模糊逻辑及其在数据融合中的应用”，《红外与激光工程》，第29卷第5期2000．10
[53]刘源 谢维信“多传感器图像模糊融合算法在图像识别中的应用”，《西安电子科技大学学报(自然科学版)》第27卷第1期2000．2
[54]范凯 陶然 周思永“模糊数据融合在目标跟踪中的应用”，《北京理工大学学报》第20卷第3期2000．6
[55]徐振海 王雪松 肖顺平 庄钊文“基于模糊融合的目标检测算法研究”，《国防科技大学学报》第22卷第4期2000．8
[56]李保洲 邓雁萍 李介谷“基于模糊积分的信息融合研究”，红外与激光工程》第28卷第4期1999．8
[57]史彩成 赵保军 韩月秋 毛二可“基于模糊理论的红外图像滤波”，《激光与红外》第31卷第2期2001．4
[2] Margalit.A., Reed.I.S., and Gagliardi. R. M. Adaptive optical target detection using correlated images. IEEE Transactions on Aerospace and Electronic Systems. AES-21(May 1985) .
[3] Hunt.B.G , and Cannon.T.M. Nonstationary assumptions of Gaussian models of images. IEEE Transaction on Systems ,Man and Cybernetics.(Dec. 1976)
[4] Kelly.E.L. Adaptive Detection in non-stationary interference. IEEE Transactions on Aerospace and Electronic Systems. AES-21(May 1986) .
[5] Mohiy.M.H. ,and David WT The Two-Dimensional Adaptive LMS(TDLMS) Algorithm, IEEE Transactions on Circuits and Systems. Vol.3 5 ,No.5 ,(May 1988)
[6] Miller.K.S. Multidimentional Gaussian Distribution.. New York: Wiley , 1964
[7] W.K.Pratt. Digital Image Processing. New York: Wiley , 1978
[8] Papoulis.A. Probability,Random Variables ,and Stochastic Processes. New York: McGraw-Hill, 1965
[9] Cramer. H. Mathematical Methods of Statistics. Princeton ,N.J.: Princeton University Press, 1958
[10] PHTL IPL BOGLER .Shafer-Dempster Reasoning With Applications to Multi-Sensor Target Identification. IEEE Transaction On System,Man, And Cybernetics, Vol.NO.6,November 1987．
[11] RAMANARYANAN VISWANATHAN AND PRAMOD KVARSHNEY.Distributed Detection With Multiple Sensors:Part I--Fundamentals .Proceedings of The IEEE, Vol.85,NO.1,January 1997．
[12] HENDRIK JAMES ANTONIS SE. Unsupervised Credit Assignment in Knowledge-Based Sensor Fusion .Systems. IEEE Transactions On Systems,Man, Cybernetics, Vol.20． No. 5, September/October 1990．
[13] W.K Pratt.IR Target Detection and Clutter Rejection Using the 1MMPDAF
Proc.SPIE Conf.Signal and Data Processing of Small Targets,Orlando,FL. April, 1998．
[14] FRANCIS MARTINERIE THOMSON SINTRA ASM.Data Fusion and Tracking Using HMMs in a Distributed Sensor Network.lEEE Transactions On Aerospace and Electronic Systems Vol.33,No.l January 1997．
[15] Chee-Yee Chong Booz Allen&Hamilton,Inc.Shozo Mori and William H.Barker Architectures and Algorithms for Track Association and Fusion. IEEE AES Systems Magazine,January 2000．
[16] RAM-NANDAN P.SINGH,WILLIAM H.BAILEY.Fu^ Logic Applications to Multi-Sensor Multi-Target Corrleation.lEEE Transactions On Aerospace and Electronic Systems Vol.33,No.3 July 1997．
[17] LAWRENCE A.KLEIN A Boolean Algebra Approach to Multiple Sensor Voting Fusion.IEEE Transaction On Aerospace and Electronic Systems Vol.29,No.2 April 1993．
[18] REN C.LUO,MICHAEL G KAY Multi-Sensor Integration and Fusion in Intelligent Systems. IEEE Transactions On Systems,Man,Cybernetics, Vol. 19,No. 5, September/October 1989
[19] STEPHEN C.POHLIG.An Algorithm for Detection of Moving Optical Targets IEEE Transaction On Aerospace and Electronic Systems Vol.AES-25,No. 1 January 1989
[20] J.N. SANDERS-REED. Maximum Likelihood Detection of Unresolved Moving Targets.IEEE Transactions On Aerospace and Electronic Systems Vol.34,No.3 July 1998
[21] M.F.Fernandez,A. Aridgides,and D.Bray. Detecting and Tracking Low-Observable Targets Using IK SPIE Vol. 1305 Signal and Data Processing of Small Targets 1990/193
[22] Dennis M.Buede,and Paul Girardi. A Target Identification Comparison of Bayesian and Dempster-Shafer Multi-Sensor Fusion. IEEE Transactions On Systems,Man,Cybernetics,Vol.27． No.5,September 1997
[23] ZEINDDINE CHAIR AND PRAMOD K.VARSHNEY. Distributed Bayesian Hypothesis Testing With Distributed Data Fusion. IEEE Transactions On Systems,Man, Cybernetics, Vol. 18． No.5,September/October 1997
[24] Isabelle Bloch. Information Combination Operators for Data Fusion: A Comparative Review With Classification.IEEE Transactions On Systems, Man,
Cybernetics,Vol.26.No.1.January 1996
[25]HYOUNGJUN IM TEAJEONG KIM. Optimization of Multi-frame Target Detection Schemes. IEEE Transactions On Aerospace and Electronic Systems Vol.35,No.1 January 1999
[25]Belur V.Dasarathy. Decision Fusion Strategies in Multi-Sensor Environments IEEE Transactions On Systems, Man, Cybernetics,Vol.21.No.5.September/October 1991
[26]耿立恩 潘旭峰 李晓雷 祝嘉光“Dempster-Shafer证据理论在数据融合中的应用”，《北京理工大学学报》1997．2
[27]王慧频 孙仲康“D-S证据结合方法用于目标和诱饵的识别”，《系统工程与电子技术》1996。10
[28]王国宏 毛士艺 何友“基于模糊综合的目标识别时空数据融合算法”，《模式识别与人工智能》1997．1
[29]龚建勇 丁明跃 周成平“基于玫瑰线扫描双色信息融合目标识别方法”，《系统工程与电子技术》1998．7
[30]李宏 孙仲康“一种具有信息融合功能的模糊推理方法”，《系统工程与电子技术》1998．1
[31]廖朝佩，“自动目标识别器的发展现状及应用分析”，《红外与激光技术》1992．2
[32]杨应槐，“小目标探测技术的研究”，《红外与激光技术》1989．1
[33]张孝霖等，“红外背景中点与近点源目标探测的卷积减法滤波算法”，《红外与激光技术》1995．8
[34]张孝霖等，“在海域红外杂波背景中小目标探测的空间匹配滤波技术”，《红外与毫米波学报》1994．4
[35]曹建清等，“一种检测红外点目标的方法”，《红外与毫米波学报》1995．4
[36]彭嘉雄等，“弱目标检测的图像流法”，《红外与激光工程》1996．8
[37]刘建轶等，“低信噪比序列图像中目标检测、识别及跟踪算法的研究”，《红与激光工程》1996．8
[39]梁民等，“点目标自动识别”，《国防科技大学学报》1991．6
[40]宋柳平等，“红外制导系统中的点目标检测技术”，《国防科技大学学报》1994．1
[41]B．维德罗等，《自适应信号处理》，四川大学出版社，1989
[42]周概容，《概率论与数理统计》，北京高等教育出版社，1984
[43]孙即祥，《数字图像处理》，河北教育出版社，1993
[44]关华“红外图像中低信噪比小目标的检测与跟踪方法研究”，国防科技大学博士学位论文，1995．10
[45]李吉成“红外起伏背景条件下的小目标检测技术研究”，国防科技大学博士学位论文，1998．8
[46]何友 王国宏等《多传感器信息融合及其应用》电子工业出版社 2000
[47]刘同明 夏祖勋 解洪成《数据融合技术及其应用》国防工业出版社 1998
[48]康耀红《数据融合理论与应用》西安电子科技大学出版社 1997
[49]刘晋寅 吴孟达《模糊理论及其应用》国防科技大学出版社 1998
[50]朱允民《多传感器分布式统计判决》科学出版社 2000
[51]边肇祺 张学工《模式识别》清华大学出版社 2000
[52]陆正刚 杨杰“模糊逻辑及其在数据融合中的应用”，《红外与激光工程》，第29卷第5期2000．10
[53]刘源 谢维信“多传感器图像模糊融合算法在图像识别中的应用”，《西安电子科技大学学报(自然科学版)》第27卷第1期2000．2
[54]范凯 陶然 周思永“模糊数据融合在目标跟踪中的应用”，《北京理工大学学报》第20卷第3期2000．6
[55]徐振海 王雪松 肖顺平 庄钊文“基于模糊融合的目标检测算法研究”，《国防科技大学学报》第22卷第4期2000．8
[56]李保洲 邓雁萍 李介谷“基于模糊积分的信息融合研究”，红外与激光工程》第28卷第4期1999．8
[57]史彩成 赵保军 韩月秋 毛二可“基于模糊理论的红外图像滤波”，《激光与红外》第31卷第2期2001．4