多源遥感图像舰船目标特征提取与融合技术研究
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摘要
综合利用多源遥感图像可抽取更具价值的信息,提高感兴趣区域或目标解译的可靠性和准确性。因此,研究面向目标解译的多源遥感图像融合技术不仅具有重要的理论意义,同时也具有实用价值。舰船是一种重要的军事目标,具有可移动特性,利用多源遥感图像可以获取此类目标更为完全的信息。针对面向海上舰船目标的多源遥感图像融合解译问题,本文主要研究了遥感图像目标解译的基础性技术——目标特征提取,以及融合应用中的关键技术——目标特征关联和融合检测,以提高对海战场环境的综合感知能力。
论文在分析成像畸变误差的基础上,针对海域光学遥感图像数据,提出了基于仿射几何理论的扩展质心-凸包(CH-EC)目标全局不变特征提取方法。该方法利用仿射几何中的特征区域面积比的不变性质构造仿射不变量,在处理速度上具有较为明显的优势。在求取目标图像的CH-EC的基础上,构造了均匀有序的三角形特征区域,增强了特征区域的稳定性,从而以此计算的仿射不变量也更稳定。
针对复杂背景的光学遥感图像数据,提出了基于尺度空间理论的MS-Gabor目标局部不变特征提取方法。该方法利用Gabor滤波器的带通特性和多通道特性可从目标图像中提取更符合视觉特性、更具物理直观性的特征点,增强了不变特征提取方法在照度变化、噪声和背景干扰下的稳健性;基于尺度空间理论设计了多尺度Gabor滤波器组,从而可使提取的特征点具有尺度不变性,增强了对遥感图像复杂几何畸变的适应性。
实现多源遥感图像融合的先决条件是将不同时间、不同空间获取的遥感图像中来自同一目标的信息对应起来,即目标关联。基于目标图像不变特征研究结果,论文提出了一种基于ACM最优化的多源遥感图像目标关联方法。该方法首先利用目标图像特征匹配结果来构造多目标关联代价矩阵(ACM);然后利用特定的目标函数对ACM进行最优化建模,并在关联准则的约束下引入模拟退火算法加速求解ACM最优状态,有效消除了多目标之间对应关系的模糊性。在考虑序列遥感图像的时态信息的基础上,提出了一种目标多特征融合跟踪方法,该方法利用目标图像特征和状态特征的互补特性来改善跟踪性能。
多源遥感图像融合技术不仅可以利用图像间的冗余信息,还可以利用图像间的互补信息来改善目标解译性能。针对异质图像互补信息融合问题,论文研究了基于SAR与光学图像的目标融合检测方法。在总结遥感图像目标检测基本框架的基础上,提出了一种基于STDM的光学图像目标检测方法,该方法利用视觉注意机制中的局部统计量来刻画与局部邻域显著不同的目标,在光学图像中取得了稳健的检测结果。在系统分析SAR图像与光学图像中目标特性的基础上,提出了基于加权马氏距离的特征层融合检测方法和基于D-S证据理论的决策层融合检测方法,两种融合检测方法分别在不同层次上充分利用了异质图像的互补信息,较大程度地降低了单源图像的检测虚警,提升了目标检测性能。
Integrating information from multiple remote sensing images can improve the reliability and accuracy of target interpretation. In this sense, multiple remote sensing image fusion is a focus of attention in military remote sensing field. Warships are kinds of most important military targets. Due to their mobile and relocatable attribution, it is needed and also possible to use multiple images to acquire more complete information of this kind of targets. In order to detect and classify ships in a marine battlefield, the basic technique of target characteristic analysis, feature extraction and some key techniques for image fusion applications, including multi-target association and fusion detection, are studied systematically in this thesis.
Typical remote imaging distortions are analyzed firstly. For sea area optical images, a Convex Hull of Extended Centroids (CH-EC) target global invariant feature extraction method is proposed based on affine geometry theory. This method utilizes the invariant properties of affine geometry to calculate the affine invariant, which has a more fast process speed. At the same time, this method finds the CH-EC to construct uniformly distributed sequential triangle regions, which enhances the stability of feature regions greatly.
For a ship target in a complex background of optical images, a MS-Gabor local invariant feature extraction method based on Scale-Space theory is proposed. This method uses the band-pass characteristic and multiple channels characteristic of Gabor filter to find invariant feature points in target images, which is more intuitional and more accordant with vision perceptive model, hence make the invariant feature is more robust under the change of illumination and the disturbance of noise and background. At the same time, based on Scale-Space theory, this method designed Multiple Scale Gabor filter banks, therefore the feature points are scale invariant, which makes the invariant feature extraction method more adaptable to imaging geometric distortion.
One of the absolutely necessary pre-condition of multiple remote sensing images fusion is target association, which is to determine if the information from two or more images are related to the same target and should be fused together. Based on the result of image invariant feature extraction, a novel multiple targets association method based on Association Cost Matrix optimization is proposed. Firstly, this method constructs ACM based on the dissimilarities of image invariant feature matching between target pairs from two images respectively, which avoids the bottleneck that the time-dependent kinematic parameters cannot be estimated from sparse remote sensing images. Secondly, the method modeled ACM as a specific object function, and then under the restricted rules of association, the simulated annealing algorithm is introduced to accelerate the process of estimating global optimal ACM, which can distinguish the ambiguous relations among multi-target pairs and has good association performance in dense targets scenarios. Considering the time information of sequential remote sensing images, a multiple feature fusion tracking method is also proposed under the global optimal association frame. The kernel of this method is combining the complementary results of kinematic feature matching and image feature matching to improve the accuracy of multi-target tracking effectively.
Multiple remote sensing image fusion can not only use redundant information, but also use complementary imformation among images to improve the performace of target interpretation. The target fusion detection technique based on heterogeneous remote sensing images, SAR (Synthetic Aperture Radar) and optical images, is studied. Firstly, the general frame of auto target detection in remote sensing images is concluded, and then a Standard Deviation Map (STDM) based optical image target detection method is proposed. This method uses local statistic to characterize the object that is obvious different from its neighboring area, so it performes robust detection in optical images. Secondly, after analysed the different target characteristics in SAR and optical images, two target fusion detection methods based on weighted M-distance fusion algorithm and D-S evidential theory fusion algorithm are proposed. The two methods can make full use of complementary information between SAR and optical images at feature level and decision level respectively, both reducing the false alarms effectively.
论文在分析成像畸变误差的基础上,针对海域光学遥感图像数据,提出了基于仿射几何理论的扩展质心-凸包(CH-EC)目标全局不变特征提取方法。该方法利用仿射几何中的特征区域面积比的不变性质构造仿射不变量,在处理速度上具有较为明显的优势。在求取目标图像的CH-EC的基础上,构造了均匀有序的三角形特征区域,增强了特征区域的稳定性,从而以此计算的仿射不变量也更稳定。
针对复杂背景的光学遥感图像数据,提出了基于尺度空间理论的MS-Gabor目标局部不变特征提取方法。该方法利用Gabor滤波器的带通特性和多通道特性可从目标图像中提取更符合视觉特性、更具物理直观性的特征点,增强了不变特征提取方法在照度变化、噪声和背景干扰下的稳健性;基于尺度空间理论设计了多尺度Gabor滤波器组,从而可使提取的特征点具有尺度不变性,增强了对遥感图像复杂几何畸变的适应性。
实现多源遥感图像融合的先决条件是将不同时间、不同空间获取的遥感图像中来自同一目标的信息对应起来,即目标关联。基于目标图像不变特征研究结果,论文提出了一种基于ACM最优化的多源遥感图像目标关联方法。该方法首先利用目标图像特征匹配结果来构造多目标关联代价矩阵(ACM);然后利用特定的目标函数对ACM进行最优化建模,并在关联准则的约束下引入模拟退火算法加速求解ACM最优状态,有效消除了多目标之间对应关系的模糊性。在考虑序列遥感图像的时态信息的基础上,提出了一种目标多特征融合跟踪方法,该方法利用目标图像特征和状态特征的互补特性来改善跟踪性能。
多源遥感图像融合技术不仅可以利用图像间的冗余信息,还可以利用图像间的互补信息来改善目标解译性能。针对异质图像互补信息融合问题,论文研究了基于SAR与光学图像的目标融合检测方法。在总结遥感图像目标检测基本框架的基础上,提出了一种基于STDM的光学图像目标检测方法,该方法利用视觉注意机制中的局部统计量来刻画与局部邻域显著不同的目标,在光学图像中取得了稳健的检测结果。在系统分析SAR图像与光学图像中目标特性的基础上,提出了基于加权马氏距离的特征层融合检测方法和基于D-S证据理论的决策层融合检测方法,两种融合检测方法分别在不同层次上充分利用了异质图像的互补信息,较大程度地降低了单源图像的检测虚警,提升了目标检测性能。
Integrating information from multiple remote sensing images can improve the reliability and accuracy of target interpretation. In this sense, multiple remote sensing image fusion is a focus of attention in military remote sensing field. Warships are kinds of most important military targets. Due to their mobile and relocatable attribution, it is needed and also possible to use multiple images to acquire more complete information of this kind of targets. In order to detect and classify ships in a marine battlefield, the basic technique of target characteristic analysis, feature extraction and some key techniques for image fusion applications, including multi-target association and fusion detection, are studied systematically in this thesis.
Typical remote imaging distortions are analyzed firstly. For sea area optical images, a Convex Hull of Extended Centroids (CH-EC) target global invariant feature extraction method is proposed based on affine geometry theory. This method utilizes the invariant properties of affine geometry to calculate the affine invariant, which has a more fast process speed. At the same time, this method finds the CH-EC to construct uniformly distributed sequential triangle regions, which enhances the stability of feature regions greatly.
For a ship target in a complex background of optical images, a MS-Gabor local invariant feature extraction method based on Scale-Space theory is proposed. This method uses the band-pass characteristic and multiple channels characteristic of Gabor filter to find invariant feature points in target images, which is more intuitional and more accordant with vision perceptive model, hence make the invariant feature is more robust under the change of illumination and the disturbance of noise and background. At the same time, based on Scale-Space theory, this method designed Multiple Scale Gabor filter banks, therefore the feature points are scale invariant, which makes the invariant feature extraction method more adaptable to imaging geometric distortion.
One of the absolutely necessary pre-condition of multiple remote sensing images fusion is target association, which is to determine if the information from two or more images are related to the same target and should be fused together. Based on the result of image invariant feature extraction, a novel multiple targets association method based on Association Cost Matrix optimization is proposed. Firstly, this method constructs ACM based on the dissimilarities of image invariant feature matching between target pairs from two images respectively, which avoids the bottleneck that the time-dependent kinematic parameters cannot be estimated from sparse remote sensing images. Secondly, the method modeled ACM as a specific object function, and then under the restricted rules of association, the simulated annealing algorithm is introduced to accelerate the process of estimating global optimal ACM, which can distinguish the ambiguous relations among multi-target pairs and has good association performance in dense targets scenarios. Considering the time information of sequential remote sensing images, a multiple feature fusion tracking method is also proposed under the global optimal association frame. The kernel of this method is combining the complementary results of kinematic feature matching and image feature matching to improve the accuracy of multi-target tracking effectively.
Multiple remote sensing image fusion can not only use redundant information, but also use complementary imformation among images to improve the performace of target interpretation. The target fusion detection technique based on heterogeneous remote sensing images, SAR (Synthetic Aperture Radar) and optical images, is studied. Firstly, the general frame of auto target detection in remote sensing images is concluded, and then a Standard Deviation Map (STDM) based optical image target detection method is proposed. This method uses local statistic to characterize the object that is obvious different from its neighboring area, so it performes robust detection in optical images. Secondly, after analysed the different target characteristics in SAR and optical images, two target fusion detection methods based on weighted M-distance fusion algorithm and D-S evidential theory fusion algorithm are proposed. The two methods can make full use of complementary information between SAR and optical images at feature level and decision level respectively, both reducing the false alarms effectively.
引文
[1]张志龙.基于遥感图像的重要目标特征提取与识别方法研究.博士学位论文.国防科学技术大学,2005.
[2]Joint Vision 2010.http://www.dtic.mil/jv2010/jvpub.htm,1996.
[3]康耀红.数据融合理论与应用.西安:西安电子科技大学出版社.1997.
[4]David L Hall,James Llinas.Handbook of multisensor data fusion.New York,USA:CRC Press,2001.
[5]Edward L Waltz,James Llinas.Mulitsensor Data Fusion.Norwood,MA:Artech House,1990.
[6]何友,王国宏等.多传感器信息融合及应用.北京:电子工业出版社,2000.
[7]王润生.信息融合.北京:科学出版社,2007.
[8]Fabrice Marre.Automatic vessel detection system on SPOT-5 optical imagery:A neuron-genetic approach.The 4th Meeting of the DECLMS Project,Toulouse,France,2004.
[9]Kreithen D E,Halversen S D,Oqirka G J.Discriminating targets from clutter.The Lincoln Laboratory Journal,1993,6(1):25-51.
[10]Leslie M Novak,Shawn D Halversen.Effects of polarization and resolution on the Performance of a SAR Automatic Target Recognition System.The Lincoln Laboratory Journal,1995,8(1):49-68.
[11]王晓红.矩技术及其在图像处理与识别中的应用研究.博士学位论文.西北工业大学,2001.4.
[12]Heynen M.Coastal mapping and ship detection from VHR satellite imagery.The 5th Meeting of the DECLMS Project,Famborough,UK,2005.
[13]李禹.SAR图像机动目标检测与鉴别技术研究.博士学位论文.国防科学技术大学,2007.
[14]周烽,封举富,石青云.一种新的基于局部傅立叶级数的纹理描述子.中国图象图形学报,2001,6(10):993-997.
[15]Skoelv A.Simulation of SAR imaging of ship wakes.Proceedings of IGARSS'88 Symposium,Edinburgh,Scotland,1988:13-16.
[16]Zhijian Wu.On the estimation of a moving ship's velocity and hull geometry information from its wave spectra.Ph.D.Dissertation.The University of Michigan,1991.
[17]Whal T.SAR detection of ship and ship wakes.SAR Applications Workshop,Frascati,Italy,1986.
[18]孙即祥等.模式识别中的特征提取与计算机视觉不变量.长沙:国防工业出版社,2001.
[19]Esa Rahtu,Mikko Salo,Janne Heikkil(a|¨).Affine Invariant Pattern Recognition Using Multiscale Autoconvolution.IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(6):908-918.
[20]Singer R A,Sea R G.New results in optimizing surveillance system tracking and data correlation performance in dense multitarget environments.IEEE Transactions on automatic control,1973,18(6):571-582.
[21]Singer R A,Sea R G,Housewright K B.Derivation and evaluation of improved tracking filters for use in dense multitarget environments.IEEE Transactions on information theory,1974,20(7):423-432.
[22]Bar Shalom Y,Tse E.Tracking in a cluttered environment with probabilistic data association.Automatica,1975,11(5):451-460.
[23]Bar Shalom Y.Extension of the probabilistic data association filter in multi-target tracking.Proceedings of the 5th symposium on nonlinear estimation,1974:16-21.
[24]Reid D B.An algorithm for tracking multiple targets.IEEE Transactions on Automatic Control,1978,24(6):843-854.
[25]Brown L G.A survey of image registration techniques.ACM Computing Surveys,1992,24(4):325-376.
[26]Hanaizumi H,Fujimura S.An automated method for registration of satellite remote sensing images.Proceedings of the International Geoscience and Remote Sensing Symposium IGARSS'93,Tokyo,Japan,1993:1348-1350.
[27]Wolberg G,Zokai S.Robust image registration using log-polar transform.Proceedings of the IEEE International Conference on Image Processing,Canada,September 2000.
[28]Reddy B S,Chatterji B N.An FFT-based technique for translation,rotation and scale-invariant image registration.IEEE Transactions on Image Processing,1996,5:1266-1271.
[29]Thevenaz P,Unser M.An efficient mutual information optimizer for multiresolution image registration.Proceedings of the IEEE International Conference on Image Processing ICIP'98,Chicago,IL,1998:833-837.
[30]Goshtasby A,Stockman G C.Point pattern matching using convex hull edges.IEEE Transactions on Systems,Man and Cybernetics,1985,15:631-637.
[31]Borgefors G.Hierarchical chamfer matching:a parametric edge matching algorithm.IEEE Transactions on Pattern Analysis and Machine Intelligence,1988,10:849-865.
[32]Sester M,Hild H,Fritsch D.Definition of ground control features for image registration using GIS data.Proceedings of the Symposium on Object Recognition and Scene Classification from Multispectral and Multisensor Pixels,CD-ROM,Columbus,Ohio,1998:7.
[33]Shekhar C,Govindu V,Chellapa R.Multisensor image registration by feature consensus.Pattern Recognition,1999,32:39-52.
[34]Barbara Zitova,Jan Flusser.Image registration methods:a survey.Image and Vision Computing,2003,21:977-1000.
[35]吉书鹏,张桂林,丁晓青.地面复杂场景图像相关足艮踪算法研究.激光与红外,2002,32(6):428-430.
[36]姚剑,刘其真,张斌等.模糊技术与神经网络的混合算法在运动目标识别与跟踪中的应用.计算机工程与应用,2000,1:62-64.
[37]董学志,宋建中韩广良.一种利用Gabor小波特征的目标跟踪方法.光学技术,2003,29(4):484-486.
[38]Shalom Y B,Sherlukde H M.Use of Measurernents from an image sensor for precision target tracking.IEEE Trans.AES,1989,25(6):863-871.
[39]Blackman S,Dempster R,Broida T.Multiple Hypothesis track confirmation for infrared surveillance systems.IEEE Trans.AES,1993,29(3):810-823.
[40]Deborah W.Burgess.Automatic ship detection in satellite multispectral imagery.Photogrammetric Engineering and Remote Sensing,1993,59(2):229-237.
[41]Hsu S,SAR and HIS data fusion for counter CC&D.IEEE Radar Conference,1999:218-220.
[42]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.遥感学报,2007,11(4):552-562.
[43]Heynen M.Coastal mapping and ship detection from VHR satellite imagery.The 5th Meeting of the DECLMS Project,Famborough,UK,2005.
[44]Lemoine G G,Greidanus H,Shepherd I M,et al.Developments in satellite fisheries monitoring and control.The 8th International Conference on Remote Sensing for Marine and Coastal Environments,Halifax,Canada,2005.
[45]Olivier Pronier.Optical data for ship detection.The 6th Meeting of the DECLMS Proiect,Ispra,Italy,2006.
[46]胡圣武,张卷美,王新洲等.空间数据融合的基本框架.测绘科学,2007,32(3):175-177.
[47]ttenkei R E.Tests of significance.Sage Beverly Hills,1976.
[48]Berger J.Statistical decision theory:foundations,concepts and methods.Springer-Verlag,1990
[49]Harris C J.Application of artificial intelligence to command&control systems.1988.
[50]王壮.C~4ISR系统目标综合识别理论与技术研究.博士学位论文.国防科学技术大学,2001.
[51]Hong L.Recursive temporal-spatial information fusion with application to target identification.IEEE Transactions on Aerospace and Electronic Systems,1993,29(2):435-445.
[52]Zadeh L A.Syllogistic reasoning in fuzzy logic and its application to usuality and reasoning with dispositions.IEEE Transactions on System,Man and Cybernetics,1985,15:754-763.
[53]Zadeh L A.The role of fuzzy logic in the management of uncertainty in expert systems.Fuzzy Sets and Systems,1983,11:199-211.
[54]Hu M K.Pattern recognition by moment invariant.Proc.IRE,1961,49:1428-1436.
[55]Flusser J,Suk Y.Pattern Recognition by Affine Moment Invariants.Pattern Recognition,1993,26(1):167-174.
[56]A Khotanzad.Zernike Moment Based Rotation Invariant Features for Pattern Recognition.SPIE,Vol.1002,1988:212-219.
[57]Suk T,Flusser J.Graph method for generating affine moment invariants.In Proc International Conference on Pattern Recognition,Cambridge,England,volume 2,2004:192-195.
[58]Esa Rahtu,Mikko Salo,Janne Heikkil(a|¨),et al.Generalized affine moment invariants for object recognition.The 18th International Conference on Pattern Recognition(ICPR'06),2006.
[59]杨冠羽,舒华忠,周卫平等.一种新的灰度图像Legendre矩的快速算法.计算机学报,2004,27(12):11642-1647.
[60]Debasish Bhattacharya,Satyabroto Sinta.Invariant of stero images via the theory of complex moments.Pattern Recognition,1997,30(9):1373-1386.
[61]Zhengwei Yang,Fernand S.Cohen.Cross-Weighted Moments and Affine Invariants for Image Registration and Matching.IEEE Transactions on Pattern Analysis and Machine Intelligence,1999,21(8):804-814.
[62]Xiong H,Zhang T,Moon Y S.A translation anti scale-invarlant adaptive wavelet transform.IEEE Transactions on Image Processing,2000,9(12):2100-2108.
[63]Maria Petrou,Alexander Kadyrov.Affine invariant features from the trace transform.IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(1):30-44.
[64]Alexander Kadyrov,Maria Petrou.The trace transform and its application.IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,23(8):811-828.
[65]Jezekiel Ben-Arie,Zhiqian Wang.Pictorial recognition of objects employing affine invariance in the frequency domain.IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(6):604-618.
[66]N.Gotze,S.Drue,G.Hartmann.Invariant Object Recognition with Discriminant Features Based on Local Fast-Fourier Mellin Transform.In Proceeding of International Conference on Pattern Recognition,2000:950-951.
[67]Ville Kryki,Joni-Kristian Kamarainen,Heikki Kalviainen.Simple Gabor feature space for invariant object recognition.Elsevier:Pattern Recognition Letters,2004,25:311-318.
[68]陈涛.图像仿射不变特征提取方法研究.博士学位论文.国防科学技术大学,2006.
[69]Zhengwei Yang,Fernand S Cohen.Image registration and object recognition using affine invariants and convex hulls.IEEE Transactions on Image Processing,1999,8(7):934-946.
[70]刘方.目标3D不变特征的提取与应用.博士学位论文.国防科学技术大学,2001.
[71]Jianguo Zhang,Tieniu Tan.Affine Invariant Texture Analysis Based on Structural Properties[C].Proceedings of the Fifth Asian Conference on Computer Vision(ACCV 2002),2002:216-221.
[72]安玮,李宏,徐晖等.模式识别中的透射变换与仿射变换.系统工程与电子技术,1999,21(1):55-60.
[73]赵军,曲仕茹.基于同底三角形面积比的飞机外形识别方法研究.兰州交通大学学报(自然科学版),2005,24(6):94-97.
[74]邓志鹏.野外景象匹配稳健性的研究.博士学位论文.上海交通大学,2004.
[75]Esa Rahtu.A multiscale framework for affine invariant pattern recognition and registration.Finland:Oulu University Press,2007.
[76]Esa Rahtu.Object recognition using multi-scale autoconvolution:M.Sc.thesis.Oulu,Finland:University of Oulu,2004.
[77]J Heikkila.Multi-scale autoconvolution for affine invariant Pattern Recognition,In Proceedings ofICPR02,Oubec,Canada,2002,1:119-122.
[78]尤承业.解析几何.北京:北京大学出版社,2004.
[79]余翔宇,孙洪,余志雄.改进的二维点集凸包快速求取方法.武汉理工大学学报,2005,27(10):81-83.
[80]Moravec H P.Robot rover visual navigation.UMI Research Press,1981.
[81]Harris C,Stephens M.A combined corner and edge detector.In:Proceedings,4th Alvey Vision Conference,Manchester,1988:147-151.
[82]Beaudet P R.Rotationally invariant image operators.International Joint Conference on Artificial Intelligence,1987:579-583.
[83] Kitchen L, Rosenfeld A. Grey-level corner detection. Pattern Recognition Letters, 1982: 95-102.
[84] Smith S M, Brady J M. SUSAN-a new approach to low level image processing.International Journal of Computer Vision, 23,1997: 45-78.
[85] Lowe D G. Object recognition from local scale-invariant features. IEEE International Conference on Computer Vision, 1999: 1150-1157.
[86] Lowe D G, Brown M. Recognising Panoramas. Proceedings of the Ninth IEEE International Conference on Computer Vision (ICCV'03), 2003.
[87] Lowe D G. Distinctive Image Features from Scale-invariant Keypoints.International Journal of Computer Vision, 2004, 60(2): 91-110.
[88] Mikolajczyk K, Schmid C. Indexing based on scale invariant interest points.IEEE International Conference on Computer Vision, 2001: 525-531.
[89] Fauqueur J, Kingsbury N, Anderson R. Multiscale keypoint detection using the Dual-tree complex wavelet. IEEE International Conference on Image Processing,2006: 1625-1628.
[90] 贾静平.图像序列中多目标跟踪技术研究.硕士学位论文.西北工业大学:2004.
[91] Zuniga O A, Haralick R M. Corner detection using the facet model. Proc. IEEE Conference on Computer Vision and Pattern Recognition, 1983: 30-37.
[92] Mehrotra R, Nichani S, Ranganathan N. Corner detection. Pattern Recognition,1990,23:1223-1233.
[93] Schmid C, Mohr R, Bauckhage C. Evaluation of interest point detectors.International Journal of Computer Vision, 2000, 37(2): 151-172.
[94] Rosenthaler L, Heitger F, Kubler O, et al. Detection of general edges and keypoints. ECCV92, Italy, 1992, 522 :78-86.
[95] Robbins B, Owens R. 2D feature detection via local energy. Image and Vision Computing, 1997(15):353-368.
[96] Witkin A P. Scale-space filtering. Proceedings of the 8th International Joint Conference on Artificial Intelligence, Karlsruhe, Germany, 1983: 1019-1023.
[97] Witkin A P. Scale-space filtering: A new approach to multiscale description.Image Understanding, 1984.
[98] Lindeberg T. Scale-Space for discrete Signals. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990,12(3):187-217.
[99] Lindeberg T. Scale space: A frame work for handling image structures at multiple scales. Proc. CERN school of Computering, Netherlands, 1996:695-702.
[100] Lindeberg T. Feature detection with automatic scale selection. International Journal of Computer Vision, 1998, 30(2): 79-116.
[101] Koenderink J J. The structure of images. Biological Cybernetics, 1984, 50:363-370.
[102] Florack L M J, B M ter Haar Romeny, Koenderink J J. Scale and the differential structure of images. Image and Vision Computing, 1992, 10(6): 376-388.
[103] Babaud J, Witkin A P, Metal B. Uniqueness of the gaussian kernel for scale-space filtering. IEEE Transactions on PAMI, 1996, 8(1): 26-33.
[104] Kadir T, Brady M. Scale, saliency and image description. International Journal of Computer Vision, 2001,45(2): 83-105.
[105] Mikolajczyk K, Schmid C. Scale & Affine invariant interest point detectors.International Journal of Computer Vision, 2004, 60(1): 63-86.
[106]Daugman J.Uncertainty relation for resolution in space,spatial frequency and orientation optimized by two-dimensional visual cortical filters.Journal of the Optical Society of America A.1985,2:1160-1169.
[107]山世光.人脸识别中若干关键问题的研究.博士学位论文.中国科学院:2004.
[108]Joni Kamarainen.Local Object Description Using Gabor Features.http://www.lut.fi/～jkamarai.
[109]Manjunath B S.Textures features for browsing and retrieval of image data.IEEE Trans on Pattern Analysis and Machine Intelligence,1996,18(8):837-842.
[110]马文坡.低轨对地观测卫星凝视成像仪探讨.航天返回与遥感,2006,27(4):17-21.
[111]余建慧,苏增立,谭谦.空间目标天基光学观测模式分析.量子电子学报,2006,23(6):772-776.
[112]刘兆军,陈伟.面阵凝视型成像空间应用技术.红外与激光工程,2006,35(5):541-545.
[113]Wu Qing X.A correlation-relaxation-labeling framework for computing optical flow-template matching from a new perspective.IEEE Transactions on Pattern Analysis and Machine Intelligence,1995,17(8):843-851.
[114]Wakahara Toru,Kimura Yoshimasa,Tomono Akira.Affine-invariant recognition of gray-scale characters using global affine transformation correlation.IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,23(4):384-395.
[115]Ranade S,Rosenfeld A.Point pattern matching by relaxation.Pattern Recognition,1980,12:269-275.
[116]Stockman G,Kopstein S,Benett S.Matching images to models for registration and object detection via clustering.IEEE Transactions on Pattern Analysis and Machine Intelligence,1982,4:229-241.
[117]Daniel P Huttenlocher,Klanderman G A,William J Rucklidge.Comparing Images Using the Hausdorff Distance.IEEE Transactions on Pattern Analysis and Machine Intelligence,1993,15(9):850-863.
[118]Fishler M A,Bolles R C.Random sample concensus:A paradigm for model fitting with applications to image analysis and automated cartography.Communications of ACM,March 1981,24(6):381-395.
[119]陈付幸,王润生.基于预检验的快速随机抽样一致性算法.软件学报,2005,16(8):1434-1473.
[120]赵向阳,杜利民.一种全自动稳健的图像拼接融合算法.中国图象图形学报,2004,9(4):417-422.
[121]Bar-Shalom Y,Kirubarajan T,Gokberk Cenk.Tracking with classification aided multiframe data association.IEEE Transactions on Aerospace and Electronic Systems,2005,41(3):868-878.
[122]Mandal Achintya K,Pal Srimanta,De Arun K,et al.Novel approach to identify good tracer clouds from a sequence of satellite images.IEEE Transactions on Geoscience and Remote Sensing,2005,43(4):813-818.
[123]Krickpatric S,Gelett J C D,Vecchi M P.Optimization by simulated annealing.Science,1983,220(4598):671-680.
[124]Gerhard Winkler.Image analysis,random fields and dynamic monte carlo methods.Berlin:Springer-Verlag.1999.
[125]汤亚波,徐守时.基于D-S证据理论的多源遥感图像目标数据联合关联算法.中国科学技术大学学报,2006(5):466-471.
[126]Nguyen Duy H.,Kay John H.,Orchard Bradley J.,et al.Classification and tracking of moving ground vehicles.Lincoln Laboratory Journal,2002,13(2):275-308.
[127]周宏仁,敬忠良,王培德.机动目标跟踪.北京:国防工业出版社,1991.
[128]刘福声,罗鹏飞.统计信号处理.长沙:国防科技大学出版社,1999.
[129]匡纲要,高贵,蒋咏梅等.合成孔径雷达目标检测理论、算法及应用.长沙:国防科技大学出版社,2007.
[130]尤晓建,徐守时,侯蕾.基于特征融合的可见光图像舰船检测新方泫.计算机工程与应用,2005,19:199-202.
[131]章毓晋.图像分割.北京:科学出版社,2001.
[132]Greenberg Shlomo,Rotman Stanley R,Guterman Hugo,et al.Region-of-interest-based algorithm for automatic target detection in infrared images.Optical Engineering,2005,44(7):1-10.
[133]阮秋琦.数字图像处理学.北京:电子工业出版社,2001.
[134]郦苏丹,李广侠,张翠等.一种SAR图像中目标姿态估计的综合方法.信号处理,2003,19(5):473-477.
[135]贾永红.多源遥感影像数据融合方法及其应用的研究.博士学位论文:武汉大学,2001.
[136]袁学华,罗景青.基于修正M距离辐射源识别方法研究.电子对抗技术,2003,18(4):9-11.
[137]Dempster A P.Upper and lower probabilities induced by multivalued mapping.Ann.Math.Stat.,1967,38:325-339.
[138]段新生.证据理论与决策人工智能.北京:中国人民大学出版社,1993.
[139]Shafer G,Logan R.Implementing Dempster's rule for hierarchical evidence.Artificial Intelligence,1987,33(3):271-298.
[2]Joint Vision 2010.http://www.dtic.mil/jv2010/jvpub.htm,1996.
[3]康耀红.数据融合理论与应用.西安:西安电子科技大学出版社.1997.
[4]David L Hall,James Llinas.Handbook of multisensor data fusion.New York,USA:CRC Press,2001.
[5]Edward L Waltz,James Llinas.Mulitsensor Data Fusion.Norwood,MA:Artech House,1990.
[6]何友,王国宏等.多传感器信息融合及应用.北京:电子工业出版社,2000.
[7]王润生.信息融合.北京:科学出版社,2007.
[8]Fabrice Marre.Automatic vessel detection system on SPOT-5 optical imagery:A neuron-genetic approach.The 4th Meeting of the DECLMS Project,Toulouse,France,2004.
[9]Kreithen D E,Halversen S D,Oqirka G J.Discriminating targets from clutter.The Lincoln Laboratory Journal,1993,6(1):25-51.
[10]Leslie M Novak,Shawn D Halversen.Effects of polarization and resolution on the Performance of a SAR Automatic Target Recognition System.The Lincoln Laboratory Journal,1995,8(1):49-68.
[11]王晓红.矩技术及其在图像处理与识别中的应用研究.博士学位论文.西北工业大学,2001.4.
[12]Heynen M.Coastal mapping and ship detection from VHR satellite imagery.The 5th Meeting of the DECLMS Project,Famborough,UK,2005.
[13]李禹.SAR图像机动目标检测与鉴别技术研究.博士学位论文.国防科学技术大学,2007.
[14]周烽,封举富,石青云.一种新的基于局部傅立叶级数的纹理描述子.中国图象图形学报,2001,6(10):993-997.
[15]Skoelv A.Simulation of SAR imaging of ship wakes.Proceedings of IGARSS'88 Symposium,Edinburgh,Scotland,1988:13-16.
[16]Zhijian Wu.On the estimation of a moving ship's velocity and hull geometry information from its wave spectra.Ph.D.Dissertation.The University of Michigan,1991.
[17]Whal T.SAR detection of ship and ship wakes.SAR Applications Workshop,Frascati,Italy,1986.
[18]孙即祥等.模式识别中的特征提取与计算机视觉不变量.长沙:国防工业出版社,2001.
[19]Esa Rahtu,Mikko Salo,Janne Heikkil(a|¨).Affine Invariant Pattern Recognition Using Multiscale Autoconvolution.IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(6):908-918.
[20]Singer R A,Sea R G.New results in optimizing surveillance system tracking and data correlation performance in dense multitarget environments.IEEE Transactions on automatic control,1973,18(6):571-582.
[21]Singer R A,Sea R G,Housewright K B.Derivation and evaluation of improved tracking filters for use in dense multitarget environments.IEEE Transactions on information theory,1974,20(7):423-432.
[22]Bar Shalom Y,Tse E.Tracking in a cluttered environment with probabilistic data association.Automatica,1975,11(5):451-460.
[23]Bar Shalom Y.Extension of the probabilistic data association filter in multi-target tracking.Proceedings of the 5th symposium on nonlinear estimation,1974:16-21.
[24]Reid D B.An algorithm for tracking multiple targets.IEEE Transactions on Automatic Control,1978,24(6):843-854.
[25]Brown L G.A survey of image registration techniques.ACM Computing Surveys,1992,24(4):325-376.
[26]Hanaizumi H,Fujimura S.An automated method for registration of satellite remote sensing images.Proceedings of the International Geoscience and Remote Sensing Symposium IGARSS'93,Tokyo,Japan,1993:1348-1350.
[27]Wolberg G,Zokai S.Robust image registration using log-polar transform.Proceedings of the IEEE International Conference on Image Processing,Canada,September 2000.
[28]Reddy B S,Chatterji B N.An FFT-based technique for translation,rotation and scale-invariant image registration.IEEE Transactions on Image Processing,1996,5:1266-1271.
[29]Thevenaz P,Unser M.An efficient mutual information optimizer for multiresolution image registration.Proceedings of the IEEE International Conference on Image Processing ICIP'98,Chicago,IL,1998:833-837.
[30]Goshtasby A,Stockman G C.Point pattern matching using convex hull edges.IEEE Transactions on Systems,Man and Cybernetics,1985,15:631-637.
[31]Borgefors G.Hierarchical chamfer matching:a parametric edge matching algorithm.IEEE Transactions on Pattern Analysis and Machine Intelligence,1988,10:849-865.
[32]Sester M,Hild H,Fritsch D.Definition of ground control features for image registration using GIS data.Proceedings of the Symposium on Object Recognition and Scene Classification from Multispectral and Multisensor Pixels,CD-ROM,Columbus,Ohio,1998:7.
[33]Shekhar C,Govindu V,Chellapa R.Multisensor image registration by feature consensus.Pattern Recognition,1999,32:39-52.
[34]Barbara Zitova,Jan Flusser.Image registration methods:a survey.Image and Vision Computing,2003,21:977-1000.
[35]吉书鹏,张桂林,丁晓青.地面复杂场景图像相关足艮踪算法研究.激光与红外,2002,32(6):428-430.
[36]姚剑,刘其真,张斌等.模糊技术与神经网络的混合算法在运动目标识别与跟踪中的应用.计算机工程与应用,2000,1:62-64.
[37]董学志,宋建中韩广良.一种利用Gabor小波特征的目标跟踪方法.光学技术,2003,29(4):484-486.
[38]Shalom Y B,Sherlukde H M.Use of Measurernents from an image sensor for precision target tracking.IEEE Trans.AES,1989,25(6):863-871.
[39]Blackman S,Dempster R,Broida T.Multiple Hypothesis track confirmation for infrared surveillance systems.IEEE Trans.AES,1993,29(3):810-823.
[40]Deborah W.Burgess.Automatic ship detection in satellite multispectral imagery.Photogrammetric Engineering and Remote Sensing,1993,59(2):229-237.
[41]Hsu S,SAR and HIS data fusion for counter CC&D.IEEE Radar Conference,1999:218-220.
[42]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.遥感学报,2007,11(4):552-562.
[43]Heynen M.Coastal mapping and ship detection from VHR satellite imagery.The 5th Meeting of the DECLMS Project,Famborough,UK,2005.
[44]Lemoine G G,Greidanus H,Shepherd I M,et al.Developments in satellite fisheries monitoring and control.The 8th International Conference on Remote Sensing for Marine and Coastal Environments,Halifax,Canada,2005.
[45]Olivier Pronier.Optical data for ship detection.The 6th Meeting of the DECLMS Proiect,Ispra,Italy,2006.
[46]胡圣武,张卷美,王新洲等.空间数据融合的基本框架.测绘科学,2007,32(3):175-177.
[47]ttenkei R E.Tests of significance.Sage Beverly Hills,1976.
[48]Berger J.Statistical decision theory:foundations,concepts and methods.Springer-Verlag,1990
[49]Harris C J.Application of artificial intelligence to command&control systems.1988.
[50]王壮.C~4ISR系统目标综合识别理论与技术研究.博士学位论文.国防科学技术大学,2001.
[51]Hong L.Recursive temporal-spatial information fusion with application to target identification.IEEE Transactions on Aerospace and Electronic Systems,1993,29(2):435-445.
[52]Zadeh L A.Syllogistic reasoning in fuzzy logic and its application to usuality and reasoning with dispositions.IEEE Transactions on System,Man and Cybernetics,1985,15:754-763.
[53]Zadeh L A.The role of fuzzy logic in the management of uncertainty in expert systems.Fuzzy Sets and Systems,1983,11:199-211.
[54]Hu M K.Pattern recognition by moment invariant.Proc.IRE,1961,49:1428-1436.
[55]Flusser J,Suk Y.Pattern Recognition by Affine Moment Invariants.Pattern Recognition,1993,26(1):167-174.
[56]A Khotanzad.Zernike Moment Based Rotation Invariant Features for Pattern Recognition.SPIE,Vol.1002,1988:212-219.
[57]Suk T,Flusser J.Graph method for generating affine moment invariants.In Proc International Conference on Pattern Recognition,Cambridge,England,volume 2,2004:192-195.
[58]Esa Rahtu,Mikko Salo,Janne Heikkil(a|¨),et al.Generalized affine moment invariants for object recognition.The 18th International Conference on Pattern Recognition(ICPR'06),2006.
[59]杨冠羽,舒华忠,周卫平等.一种新的灰度图像Legendre矩的快速算法.计算机学报,2004,27(12):11642-1647.
[60]Debasish Bhattacharya,Satyabroto Sinta.Invariant of stero images via the theory of complex moments.Pattern Recognition,1997,30(9):1373-1386.
[61]Zhengwei Yang,Fernand S.Cohen.Cross-Weighted Moments and Affine Invariants for Image Registration and Matching.IEEE Transactions on Pattern Analysis and Machine Intelligence,1999,21(8):804-814.
[62]Xiong H,Zhang T,Moon Y S.A translation anti scale-invarlant adaptive wavelet transform.IEEE Transactions on Image Processing,2000,9(12):2100-2108.
[63]Maria Petrou,Alexander Kadyrov.Affine invariant features from the trace transform.IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(1):30-44.
[64]Alexander Kadyrov,Maria Petrou.The trace transform and its application.IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,23(8):811-828.
[65]Jezekiel Ben-Arie,Zhiqian Wang.Pictorial recognition of objects employing affine invariance in the frequency domain.IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(6):604-618.
[66]N.Gotze,S.Drue,G.Hartmann.Invariant Object Recognition with Discriminant Features Based on Local Fast-Fourier Mellin Transform.In Proceeding of International Conference on Pattern Recognition,2000:950-951.
[67]Ville Kryki,Joni-Kristian Kamarainen,Heikki Kalviainen.Simple Gabor feature space for invariant object recognition.Elsevier:Pattern Recognition Letters,2004,25:311-318.
[68]陈涛.图像仿射不变特征提取方法研究.博士学位论文.国防科学技术大学,2006.
[69]Zhengwei Yang,Fernand S Cohen.Image registration and object recognition using affine invariants and convex hulls.IEEE Transactions on Image Processing,1999,8(7):934-946.
[70]刘方.目标3D不变特征的提取与应用.博士学位论文.国防科学技术大学,2001.
[71]Jianguo Zhang,Tieniu Tan.Affine Invariant Texture Analysis Based on Structural Properties[C].Proceedings of the Fifth Asian Conference on Computer Vision(ACCV 2002),2002:216-221.
[72]安玮,李宏,徐晖等.模式识别中的透射变换与仿射变换.系统工程与电子技术,1999,21(1):55-60.
[73]赵军,曲仕茹.基于同底三角形面积比的飞机外形识别方法研究.兰州交通大学学报(自然科学版),2005,24(6):94-97.
[74]邓志鹏.野外景象匹配稳健性的研究.博士学位论文.上海交通大学,2004.
[75]Esa Rahtu.A multiscale framework for affine invariant pattern recognition and registration.Finland:Oulu University Press,2007.
[76]Esa Rahtu.Object recognition using multi-scale autoconvolution:M.Sc.thesis.Oulu,Finland:University of Oulu,2004.
[77]J Heikkila.Multi-scale autoconvolution for affine invariant Pattern Recognition,In Proceedings ofICPR02,Oubec,Canada,2002,1:119-122.
[78]尤承业.解析几何.北京:北京大学出版社,2004.
[79]余翔宇,孙洪,余志雄.改进的二维点集凸包快速求取方法.武汉理工大学学报,2005,27(10):81-83.
[80]Moravec H P.Robot rover visual navigation.UMI Research Press,1981.
[81]Harris C,Stephens M.A combined corner and edge detector.In:Proceedings,4th Alvey Vision Conference,Manchester,1988:147-151.
[82]Beaudet P R.Rotationally invariant image operators.International Joint Conference on Artificial Intelligence,1987:579-583.
[83] Kitchen L, Rosenfeld A. Grey-level corner detection. Pattern Recognition Letters, 1982: 95-102.
[84] Smith S M, Brady J M. SUSAN-a new approach to low level image processing.International Journal of Computer Vision, 23,1997: 45-78.
[85] Lowe D G. Object recognition from local scale-invariant features. IEEE International Conference on Computer Vision, 1999: 1150-1157.
[86] Lowe D G, Brown M. Recognising Panoramas. Proceedings of the Ninth IEEE International Conference on Computer Vision (ICCV'03), 2003.
[87] Lowe D G. Distinctive Image Features from Scale-invariant Keypoints.International Journal of Computer Vision, 2004, 60(2): 91-110.
[88] Mikolajczyk K, Schmid C. Indexing based on scale invariant interest points.IEEE International Conference on Computer Vision, 2001: 525-531.
[89] Fauqueur J, Kingsbury N, Anderson R. Multiscale keypoint detection using the Dual-tree complex wavelet. IEEE International Conference on Image Processing,2006: 1625-1628.
[90] 贾静平.图像序列中多目标跟踪技术研究.硕士学位论文.西北工业大学:2004.
[91] Zuniga O A, Haralick R M. Corner detection using the facet model. Proc. IEEE Conference on Computer Vision and Pattern Recognition, 1983: 30-37.
[92] Mehrotra R, Nichani S, Ranganathan N. Corner detection. Pattern Recognition,1990,23:1223-1233.
[93] Schmid C, Mohr R, Bauckhage C. Evaluation of interest point detectors.International Journal of Computer Vision, 2000, 37(2): 151-172.
[94] Rosenthaler L, Heitger F, Kubler O, et al. Detection of general edges and keypoints. ECCV92, Italy, 1992, 522 :78-86.
[95] Robbins B, Owens R. 2D feature detection via local energy. Image and Vision Computing, 1997(15):353-368.
[96] Witkin A P. Scale-space filtering. Proceedings of the 8th International Joint Conference on Artificial Intelligence, Karlsruhe, Germany, 1983: 1019-1023.
[97] Witkin A P. Scale-space filtering: A new approach to multiscale description.Image Understanding, 1984.
[98] Lindeberg T. Scale-Space for discrete Signals. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990,12(3):187-217.
[99] Lindeberg T. Scale space: A frame work for handling image structures at multiple scales. Proc. CERN school of Computering, Netherlands, 1996:695-702.
[100] Lindeberg T. Feature detection with automatic scale selection. International Journal of Computer Vision, 1998, 30(2): 79-116.
[101] Koenderink J J. The structure of images. Biological Cybernetics, 1984, 50:363-370.
[102] Florack L M J, B M ter Haar Romeny, Koenderink J J. Scale and the differential structure of images. Image and Vision Computing, 1992, 10(6): 376-388.
[103] Babaud J, Witkin A P, Metal B. Uniqueness of the gaussian kernel for scale-space filtering. IEEE Transactions on PAMI, 1996, 8(1): 26-33.
[104] Kadir T, Brady M. Scale, saliency and image description. International Journal of Computer Vision, 2001,45(2): 83-105.
[105] Mikolajczyk K, Schmid C. Scale & Affine invariant interest point detectors.International Journal of Computer Vision, 2004, 60(1): 63-86.
[106]Daugman J.Uncertainty relation for resolution in space,spatial frequency and orientation optimized by two-dimensional visual cortical filters.Journal of the Optical Society of America A.1985,2:1160-1169.
[107]山世光.人脸识别中若干关键问题的研究.博士学位论文.中国科学院:2004.
[108]Joni Kamarainen.Local Object Description Using Gabor Features.http://www.lut.fi/～jkamarai.
[109]Manjunath B S.Textures features for browsing and retrieval of image data.IEEE Trans on Pattern Analysis and Machine Intelligence,1996,18(8):837-842.
[110]马文坡.低轨对地观测卫星凝视成像仪探讨.航天返回与遥感,2006,27(4):17-21.
[111]余建慧,苏增立,谭谦.空间目标天基光学观测模式分析.量子电子学报,2006,23(6):772-776.
[112]刘兆军,陈伟.面阵凝视型成像空间应用技术.红外与激光工程,2006,35(5):541-545.
[113]Wu Qing X.A correlation-relaxation-labeling framework for computing optical flow-template matching from a new perspective.IEEE Transactions on Pattern Analysis and Machine Intelligence,1995,17(8):843-851.
[114]Wakahara Toru,Kimura Yoshimasa,Tomono Akira.Affine-invariant recognition of gray-scale characters using global affine transformation correlation.IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,23(4):384-395.
[115]Ranade S,Rosenfeld A.Point pattern matching by relaxation.Pattern Recognition,1980,12:269-275.
[116]Stockman G,Kopstein S,Benett S.Matching images to models for registration and object detection via clustering.IEEE Transactions on Pattern Analysis and Machine Intelligence,1982,4:229-241.
[117]Daniel P Huttenlocher,Klanderman G A,William J Rucklidge.Comparing Images Using the Hausdorff Distance.IEEE Transactions on Pattern Analysis and Machine Intelligence,1993,15(9):850-863.
[118]Fishler M A,Bolles R C.Random sample concensus:A paradigm for model fitting with applications to image analysis and automated cartography.Communications of ACM,March 1981,24(6):381-395.
[119]陈付幸,王润生.基于预检验的快速随机抽样一致性算法.软件学报,2005,16(8):1434-1473.
[120]赵向阳,杜利民.一种全自动稳健的图像拼接融合算法.中国图象图形学报,2004,9(4):417-422.
[121]Bar-Shalom Y,Kirubarajan T,Gokberk Cenk.Tracking with classification aided multiframe data association.IEEE Transactions on Aerospace and Electronic Systems,2005,41(3):868-878.
[122]Mandal Achintya K,Pal Srimanta,De Arun K,et al.Novel approach to identify good tracer clouds from a sequence of satellite images.IEEE Transactions on Geoscience and Remote Sensing,2005,43(4):813-818.
[123]Krickpatric S,Gelett J C D,Vecchi M P.Optimization by simulated annealing.Science,1983,220(4598):671-680.
[124]Gerhard Winkler.Image analysis,random fields and dynamic monte carlo methods.Berlin:Springer-Verlag.1999.
[125]汤亚波,徐守时.基于D-S证据理论的多源遥感图像目标数据联合关联算法.中国科学技术大学学报,2006(5):466-471.
[126]Nguyen Duy H.,Kay John H.,Orchard Bradley J.,et al.Classification and tracking of moving ground vehicles.Lincoln Laboratory Journal,2002,13(2):275-308.
[127]周宏仁,敬忠良,王培德.机动目标跟踪.北京:国防工业出版社,1991.
[128]刘福声,罗鹏飞.统计信号处理.长沙:国防科技大学出版社,1999.
[129]匡纲要,高贵,蒋咏梅等.合成孔径雷达目标检测理论、算法及应用.长沙:国防科技大学出版社,2007.
[130]尤晓建,徐守时,侯蕾.基于特征融合的可见光图像舰船检测新方泫.计算机工程与应用,2005,19:199-202.
[131]章毓晋.图像分割.北京:科学出版社,2001.
[132]Greenberg Shlomo,Rotman Stanley R,Guterman Hugo,et al.Region-of-interest-based algorithm for automatic target detection in infrared images.Optical Engineering,2005,44(7):1-10.
[133]阮秋琦.数字图像处理学.北京:电子工业出版社,2001.
[134]郦苏丹,李广侠,张翠等.一种SAR图像中目标姿态估计的综合方法.信号处理,2003,19(5):473-477.
[135]贾永红.多源遥感影像数据融合方法及其应用的研究.博士学位论文:武汉大学,2001.
[136]袁学华,罗景青.基于修正M距离辐射源识别方法研究.电子对抗技术,2003,18(4):9-11.
[137]Dempster A P.Upper and lower probabilities induced by multivalued mapping.Ann.Math.Stat.,1967,38:325-339.
[138]段新生.证据理论与决策人工智能.北京:中国人民大学出版社,1993.
[139]Shafer G,Logan R.Implementing Dempster's rule for hierarchical evidence.Artificial Intelligence,1987,33(3):271-298.
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