多源遥感图像的匹配与融合算法研究
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摘要
在遥感领域,卫星遥感系统提供着越来越多覆盖全球和重复测度的数据,为了充分、有效、综合地利用这些信息,就需要将同一目标或同一区域的多源图像数据进行匹配与融合。本文以多源遥感图像作为对象,研究并实现了多源遥感图像的匹配与融合算法。
首先,讨论了一种基于Contourlet域、Krawtchouk矩和改进粒子群的图像匹配算法,主要针对单一传感器所获取的遥感图像进行快速匹配。与目前常用的匹配算法相比,该算法可在加快匹配速度的同时,大大提高匹配的精度。
其次,针对多传感器获取的遥感图像,研究了两种多源遥感图像匹配算法,分别引入改进型Hausdorff距离和基于Tsallis熵的互信息量作为相似性度量准则。结果表明,这两种算法都具有较高的匹配效率和运算速度。
然后,实现了一种基于对数极坐标变换和对齐度的多源遥感图像配准算法,对以Harris角点为中心的圆进行对数极坐标变换,采用对齐度度量准则得到匹配点。实验结果显示,该算法能有效地对多源遥感图像进行配准,对旋转、尺度变换具有一定的鲁棒性。
接着,采用了无下采样Contourlet变换对多源遥感图像进行融合处理。该方法的性能较传统的基于小波变换或基于Contourlet变换的融合方法有了一定的提高,融合效果更好。
最后,介绍了一种基于无下采样Contourlet变换和模糊推理的图像融合算法,利用模糊推理计算各源图像对融合图像的贡献程度。结果表明,该算法简单、可行,并能更加有效地融合源图像中的信息。
In the field of remote sensing, satellite remote sensing systems are providing a growing number of global coverage and repeated measure data. To make full use of this information, multi-source images of the same target or the same region need to be matched and fused. In this paper, multi-source remote sensing images are taken as the research objects, the matching and fusion methods for multi-source remote sensing images are studied and implemented.
Firstly, a fast image matching algorithm based on contourlet-domain, Krawtchouk moments and improved particle swarm optimization is discussed, which is mainly used to match the remote sensing images taken from a single sensor. Compared with those of other existing image matching methods, the algorithm is less time-consuming, while greatly improves the precision.
Secondly, aiming at the remote sensing images taken from multi-sensors, two image matching algorithms are studied. Improved hausdorff distance and Tsallis entropy based mutual information are used as matching measure function, respectively. The results show that the algorithms both have the higher accuracy and require fewer operations.
Then, an image registration method based on log-polar transformation and alignment metric is implemented. Log-polar transform is applied to the region whose centre is Harris corner, and the matching points are obtained by using alignment metric. The results show that the method can effectively match the multi-source remote sensing images, and has robustness with rotation and scale transform.
Next, the nonsubsampled contourlet transform is used in image fusion for multi-source remote sensing images. The approach can achieve better results than the methods based on wavelet transform or contourlet transform.
Finally, an image fusion method based on nonsubsampled contourlet transform and fuzzy reasoning is introduced, estimating the importance of the source images with fuzzy reasoning. The results show that the method is simple, feasible and can be more effective for image fusion.
首先,讨论了一种基于Contourlet域、Krawtchouk矩和改进粒子群的图像匹配算法,主要针对单一传感器所获取的遥感图像进行快速匹配。与目前常用的匹配算法相比,该算法可在加快匹配速度的同时,大大提高匹配的精度。
其次,针对多传感器获取的遥感图像,研究了两种多源遥感图像匹配算法,分别引入改进型Hausdorff距离和基于Tsallis熵的互信息量作为相似性度量准则。结果表明,这两种算法都具有较高的匹配效率和运算速度。
然后,实现了一种基于对数极坐标变换和对齐度的多源遥感图像配准算法,对以Harris角点为中心的圆进行对数极坐标变换,采用对齐度度量准则得到匹配点。实验结果显示,该算法能有效地对多源遥感图像进行配准,对旋转、尺度变换具有一定的鲁棒性。
接着,采用了无下采样Contourlet变换对多源遥感图像进行融合处理。该方法的性能较传统的基于小波变换或基于Contourlet变换的融合方法有了一定的提高,融合效果更好。
最后,介绍了一种基于无下采样Contourlet变换和模糊推理的图像融合算法,利用模糊推理计算各源图像对融合图像的贡献程度。结果表明,该算法简单、可行,并能更加有效地融合源图像中的信息。
In the field of remote sensing, satellite remote sensing systems are providing a growing number of global coverage and repeated measure data. To make full use of this information, multi-source images of the same target or the same region need to be matched and fused. In this paper, multi-source remote sensing images are taken as the research objects, the matching and fusion methods for multi-source remote sensing images are studied and implemented.
Firstly, a fast image matching algorithm based on contourlet-domain, Krawtchouk moments and improved particle swarm optimization is discussed, which is mainly used to match the remote sensing images taken from a single sensor. Compared with those of other existing image matching methods, the algorithm is less time-consuming, while greatly improves the precision.
Secondly, aiming at the remote sensing images taken from multi-sensors, two image matching algorithms are studied. Improved hausdorff distance and Tsallis entropy based mutual information are used as matching measure function, respectively. The results show that the algorithms both have the higher accuracy and require fewer operations.
Then, an image registration method based on log-polar transformation and alignment metric is implemented. Log-polar transform is applied to the region whose centre is Harris corner, and the matching points are obtained by using alignment metric. The results show that the method can effectively match the multi-source remote sensing images, and has robustness with rotation and scale transform.
Next, the nonsubsampled contourlet transform is used in image fusion for multi-source remote sensing images. The approach can achieve better results than the methods based on wavelet transform or contourlet transform.
Finally, an image fusion method based on nonsubsampled contourlet transform and fuzzy reasoning is introduced, estimating the importance of the source images with fuzzy reasoning. The results show that the method is simple, feasible and can be more effective for image fusion.
引文
[1]童强.基于两视图的图像匹配算法研究[D].合肥:安徽大学, 2006.
[2]刘松涛,周晓东.图像融合技术研究的最新进展[J].激光与红外, 2006, 8(36): 627~632.
[3]马俊.基于边缘信息的红外与可见光图像匹配技术[D].武汉:华中科技大学, 2006.
[4] Kumar M, Dass S. A total variation-based algorithm for pixel-level image fusion[J]. IEEE Transactions on Image Processing, 2009,18(9): 2137~2143.
[5]洪日昌.多源图像融合算法及应用研究[D].合肥:中国科学技术大学, 2005.
[6] Wang C, Wan T R, Palmer I J. A real-time dynamic simulation scheme for large-scale flood hazard using 3D real world data[C]. 11th International Conference on Information Visualization, Maryland, USA, 2007.
[7] Tomazevic D, Likar B, Pernus F. 3-D/2-D registration by integrating 2-D information in 3-D[J]. IEEE Transactions on Medical Imaging, 2006, 25(1): 17~27.
[8]李德广,李科杰,高丽丽.基于多尺度多方向相位匹配的立体视觉方法[J].仪器仪表学报(增刊), 2004, 25(4): 600~602.
[9]刘芳,潘晓英.基于免疫克隆选择的块匹配运动估计[J].软件学报, 2007, 18(4): 850~860.
[10] Rohde G K, Dawant B M, Shien-Fong L. Correction of motion artifact in cardiac optical mapping using image registration[J]. IEEE Transactions on Biomedical Engineering, 2005, 58(2): 338~341.
[11] Li T. A robust image-based method for 3D registration[C]. 5th International Conference on 3-D Digital Imaging and Modeling, Ottawa, Canada, 2005, 6: 270~276.
[12]火元莲,齐永锋,宋海声.基于轮廓特征点最大互信息的多模态医学图像配准[J].激光与红外, 2008, 1(38): 96~98.
[13] Bentoutou Y, Taleb N. Automatic extraction of control points for digital subtraction angiography image enhancement[J]. IEEE Transactions on Nuclear Science, 2005, 52(1): 238~246.
[14] Prat W K. Digital image processing[M]. New York: Wiley Press, 1991.
[15] Barnea D I, Silverman H F. A class of algorithms for fast digital image registration[J]. IEEE Transactions on Computing, 1972, 21(2): 179~186.
[16] Viola P A, Wells W M. Alignment by maximization of mutual information[J]. InternationalJournal of Computer Vision, 1997, 24(2): 137~154.
[17] Zitova B, Flusser J. Image registration methods: a survey[J]. Image and Vision Computing. 2003, 24: 977~1000.
[18]钟家强,王润生.基于互信息相似性度量的多时相遥感图像配准[J].宇航学报, 2007, 27(4): 690~694.
[19]施颖琦,顾力栩.基于互信息多步骤优化的医学图像配准[J].计算机工程, 2006, 32(22): 187~188.
[20] Dufournaud Y, Schmid C, Horaud R. Image matching with scale adjust[J]. Computer Vision and Image Understanding, 2004, 93(2): 175~194.
[21]件建宁,郭宝龙,冯宗哲.一种基于兴趣点特征匹配的图像镶嵌技术[J].光电子.激光, 2006, 17(6): 733~737.
[22]薛模根,韩裕生,李从利,等.基于区域同名度矩阵的连续帧图像配[J].中国图象图形学报, 2006, 11(1): 53~59.
[23]刘贵喜,王蕾.基于区域选择和特征点匹配的图像配准算法[J].光电子.激光, 2007, 18(8): 999~1002.
[24] Liu Y R, Hou M L, Rao X J, et al. A steady corner detection of gray level images based on improved harris algorithm[C]. 5th International Conference on Networking, Sensing and Control, Sanya, China, 2008, 4: 708~713.
[25] Bouchafa S, Zavidovique B. Efficient cumulative matching for image registration[J]. Image and Computing, 2006, 24: 70~79.
[26]张鹏强,余旭初,韩丽,等.基于直线特征匹配的序列图像自动配准[J].武汉大学学报(信息科学版), 2007, 32(8): 676~679.
[27]堪安军,陈炜,毛士艺.一种基于边缘的图像配准方法[J].电子与信息学报, 2004, 26(5): 679~684.
[28]李树涛,王耀南,张昌凡.多传感器图像融合的客观评价与分析[J].仪器仪表学报, 2002, 23 (6): 651~654.
[29]周礼,王章野,金剑秋,等.基于HIS的小波图像融合新算法.中国图象图形学报, 2004, 9(9): 1088~1095.
[30]杨进,刘建波.一种改进的HIS图像融合新算法[J].华中科技大学学报, 2007, 35(8): 21~23.
[31]何贵青,郝重阳.特征量积和HIS变换的多源遥感图像融合方法[J].火力与指挥控制, 2006, 31 (11): 88~90.
[32] Gonzalez-Audicana M, Saleta J L, Catalan R G, et al. Fusion of multispectral and panchromatic images using improved HIS and PCA mergers based on wavelet decomposition[J]. Geoscience and Remote Sensing, 2004, 42(6): 1291~1230.
[33] Chen H X. A multiresolution image fusion based on principle component analysis[C]. 4th International Conference on Image and Graphics, Chengdu, Sichuan, 2007, 8: 737~741.
[34] Yue J, Yang R L, Huan R H. Pixel level fusion for multiple SAR images using PCA and wavelet transform[C]. 2006 CIE International Conference on Radar, Shanghai, China, 2006: 1~4.
[35]成云霞,田裕鹏.一种基于小波变换的图像融合方法[J].计算机与数字工程, 2006, 34(5): 75~77.
[36]燕文浩,马彩文,张鸣,等.基于小波变换的图像融合新算法[J].光子学报, 2006, 35(4): 638~640.
[37] Do M N, Vetterli M. The contourlet transform: an efficient directional multiresolution image representation[J]. IEEE Transactions On Image Processing, 2005, 14(12): 2091~2106.
[38]梁栋,李瑶,沈敏,等.一种基于小波-Contourlet变换的多聚集图像融合算法[J].电子学报, 2007, 2(35): 320~322.
[39]陈蜜,李德仁,秦前清,等.基于Contourlet变换的遥感影像融合算法[J].小型微型计算机系统, 2006, 27(11): 2052~2056.
[40]张继贤,李国胜,曾钰.多源遥感影像高精度自动配准的方法研究[J].遥感学报, 2005, 9(1): 73~77.
[41]于秋则,程辉,柳健,等.基于改进Hausdorff测度和遗传算法的SAR图像与光学图像匹配[J].宇航学报, 2006, 27(1): 130~134.
[42] Zhang Y, Chen Z G, Xu Y. Multi-spectral remote image registration based on SIFT[J]. Electronics Letters, 2008, 44(2): 107~108.
[43]薛弘晔,李言俊,张科.加权Hausdorff距离蚁群算法寻优的红外图像匹配[J].红外技术. 2007, 29(12): 708~711.
[44]李晖晖,郭雷,刘航.基于不同类型小波变换的SAR与可见光图像融合研究[J].光子学报, 2006, 35(8): 1263~1267.
[45]李晖晖,郭雷,李国新.基于脊波变换的SAR与可见光图像融合研究[J].西北工业大学学报, 2006, 24(4): 418~422.
[46] Zheng S, Shi W Z, Liu J, et al. Remote sensing image fusion using multiscale mapped LS-SVM[J]. Geoscience and Remote Sensing, 2008, 46(5): 1313~1322.
[47]刘盛鹏,方勇.基于Contourlet变换和IPCNN的融合算法及其在可见光与红外线图像融合中的应用[J].红外与毫米波学报, 2007, 26(3): 217~221.
[48]李卫华,周军.基于估计理论的红外与可见光图像融合算法[J].空军工程大学学报(自然科学版), 2006, 7(3): 44~47.
[49] Rebollo-Neira L and Lowe D. Optimized orthogonal matching pursuit approach[J]. IEEE Signal Processing Letters, 2002, 9: 137~140.
[50] Brown L G. A survey of image registration techniques[J]. ACM Computing Surveys, 1992, 24 (4): 325~376.
[51] Olson C F, Huttenlocher D P. Automatic target recognition by matching oriented edge pixels[J]. IEEE Transactions on Image Processing, 1997, 6(1): 103~112.
[52] You J, Bhattacharya P A. Wavelet-based coarse-to-fine image matching scheme in a parallel virtual machine environment[J]. IEEE Transactions on Image Processing, 2000, 9(9): 1547~1559.
[53] Yutaro Y, Hyoungseop K, Joo k T, et al. A method for reducing of computation time on image registration employing wavelet transformation[C]. International Conference on Control, Automation and Systems, Seoul, Korea, 2007: 1286~1291.
[54]张登荣,俞乐,蔡志刚.点特征和小波金字塔技术的遥感图像快速匹配技术[J].浙江大学学报(理学版), 2007, 34(4): 465~468.
[55]朱江,赵亦工.基于遗传算法的快速图像相关匹配[J].红外与毫米波学报, 1999, 18(2): 145~150.
[56]熊兴华,钱曾波,王任享.遗传算法与最小二乘法相结合的遥感图像子像素匹配[J].测绘学报, 2001, 30(1): 54~59.
[57] Chalermwat P, El-Ghazawi T, Le Moigne J. Two-phase genetic algorithm-based image registration on parallel clusters[J]. Journal of Future Generation Computing Systems, 2001, 17: 467~476.
[58]郑军,诸静.基于自适应遗传算法的图像匹配[J].浙江大学学报(工学版), 2003, 37(6): 689~692.
[59]徐建斌,洪文,吴一戎.基于遗传算法的遥感影像匹配定位的研究[J].测试技术学报, 2004, 18(4): 351~354.
[60]杨延西,刘丁,辛菁.模糊遗传图像相关匹配算法[J].仪器仪表学报, 2005, 26(11): 1166~1169.
[61]徐建斌,洪文,吴一戎.一种基于距离变换和遗传算法的遥感图像匹配算法[J].电子与信息学报, 2005, 27(7): 1009~1012.
[62]徐建斌,洪文,吴一戎.基于小波变换和遗传算法的遥感影像匹配方法的研究[J].电子与信息学报, 2005, 27(2): 283~285.
[63] Kennedy J,Eberhart R. Particle swarm optimization[C]. International Conference on Neural Networks, Piscataway, NJ, USA, 1995: 1942~1948.
[64] Pew-Thian Yap, Raceendran Paramesran. Senior Member, et al. Image analysis by Krawtchouk moments[J]. IEEE Transactions on Image Processing, 2003, 12(11): 1367~1377.
[65]易文娟,郁梅,蒋刚毅. Contourlet:一种有效的方向多尺度变换分析方法[J].计算机应用研究, 2006, 9(11): 18~22.
[66]胡望,李志蜀.一种更简化而高效的粒子群优化算法[J].软件学报, 2007, 18(4): 861~868.
[67]陈贵敏,贾建援,韩琪.粒子群优化算法的惯性权值递减策略研究[J].西安交通大学学报, 2006, 40(1): 53~56.
[68]孙瑾,顾宏斌,秦小麟,等.一种鲁棒型Hausdorff距离图像匹配方法[J].中国图象图形学报, 2008, 13(4): 761~767.
[69]周志强,汪渤.一种基于鲁棒Hausdorff距离的目标匹配算法[J].计算机应用, 2009, 29(1): 86~89.
[70] Sim D G, Kwon O K, Park R H. Object matching algorithms using robust hausdorff distance measures[J]. IEEE Transactions on Image Processing, 1999, 8(3): 425~429.
[71] Cole-Rhodes A A, Johnson K L, Le-Moigne J, et al. Multiresolution registration of remote sensing imagery by optimization of mutual information using a stochastic gradient[J]. Image Processing, 2003, 12(12): 1495~1511.
[72]田伟刚,郭雷,李晖晖,等.基于区域互信息的特征级多光谱图像配准[J].光电子.激光, 2008, 19(6): 799~783.
[73] Furuichi S. Information theoretical properties of Tsallis entropies[J]. Journal of Mathematical Physics, 2006, 47(2): 23302-1~18.
[74] Martin S, Morison G H, Nailon W H, et al. Fast and accurate image registration using Tsallis entropy and simultaneous perturbation stochastic approximation[J]. IET Electronics Letters, 2001, 40(10): 595~597.
[75] Waleed M and Ben Hamza A. Nonextensive entropic image registration[J]. Image Analysis and Recognition, 2009, 5627: 116~125.
[76] Skouson M B, Quji G, et al. A bound on mutual Iinformation for image registration[J]. IEEETransactions on Medical Imaging, 2001, 20(8): 843~846.
[77] Hua-Mei C, Varshney, P K., Arora, et al. Performance of mutual information similarity measure for registration of multi-temporal remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(11): 2445~2454.
[78]曹晓光,徐琳,郁文霞.基于角点检测的高精度点匹配算法[J].仪器仪表学报[J], 2006, 27(6): 1269~1271.
[79]王靖,朱梦宇,赵保军,等.基于小波和改进型Hausdorff距离的遥感图像配准方法[J].电子学报, 2006, 34(12): 2167~2169.
[80]苏娟,林行刚,刘代志.一种基于结构特征边缘的多传感器图像配准方法[J].自动化学报, 2009, 35(3): 251~257.
[81]田越,张永梅,李波.遥感图像的快速配准方法[J].北京航空航天大学学报, 2008, 34(11): 1356~1359.
[82] Qiguang M, Baoshu W. A novel image fusion method using contourlet transform[J]. IEEE Transactions On Image Processing, 2006, 6: 548~552.
[83]吴一全,李晓燕,陈飒.基于Contourlet域ICA和SVM的图像融合[J].光电子.激光, 2009, 20(6): 32~35.
[84]李光鑫,王珂.基于Contourlet变换的彩色图像融合算法[J].电子学报, 2007, 1(35):112~118.
[85] Arthur L C, Zhou J P, Do M N. The nonsubsampled contourlet transform theory, design and application[J]. IEEE Transaction on Image Processing, 2006, 15(10): 3089~3101.
[86]吴一全,陈飒,罗子娟.基于无下采样Contourlet变换的图像融合[J].中国体视学与图像分析, 2008, 13(2): 111~115.
[87] Zhu M Y, Yang Y L. A new image fusion algorithm based on fuzzy Logic[C]. International Conference on Intelligent Computation Technology and Automation, Changsha, Hunan, China, 2008, 2: 83~86.
[88] Chen S, Wu Y Q. Image fusion based on contourlet transform and fuzzy logic[C]. 2nd International Conference on Image and Signal Processing, Tianjing, China, 2009, 4: 2143~2147.
[2]刘松涛,周晓东.图像融合技术研究的最新进展[J].激光与红外, 2006, 8(36): 627~632.
[3]马俊.基于边缘信息的红外与可见光图像匹配技术[D].武汉:华中科技大学, 2006.
[4] Kumar M, Dass S. A total variation-based algorithm for pixel-level image fusion[J]. IEEE Transactions on Image Processing, 2009,18(9): 2137~2143.
[5]洪日昌.多源图像融合算法及应用研究[D].合肥:中国科学技术大学, 2005.
[6] Wang C, Wan T R, Palmer I J. A real-time dynamic simulation scheme for large-scale flood hazard using 3D real world data[C]. 11th International Conference on Information Visualization, Maryland, USA, 2007.
[7] Tomazevic D, Likar B, Pernus F. 3-D/2-D registration by integrating 2-D information in 3-D[J]. IEEE Transactions on Medical Imaging, 2006, 25(1): 17~27.
[8]李德广,李科杰,高丽丽.基于多尺度多方向相位匹配的立体视觉方法[J].仪器仪表学报(增刊), 2004, 25(4): 600~602.
[9]刘芳,潘晓英.基于免疫克隆选择的块匹配运动估计[J].软件学报, 2007, 18(4): 850~860.
[10] Rohde G K, Dawant B M, Shien-Fong L. Correction of motion artifact in cardiac optical mapping using image registration[J]. IEEE Transactions on Biomedical Engineering, 2005, 58(2): 338~341.
[11] Li T. A robust image-based method for 3D registration[C]. 5th International Conference on 3-D Digital Imaging and Modeling, Ottawa, Canada, 2005, 6: 270~276.
[12]火元莲,齐永锋,宋海声.基于轮廓特征点最大互信息的多模态医学图像配准[J].激光与红外, 2008, 1(38): 96~98.
[13] Bentoutou Y, Taleb N. Automatic extraction of control points for digital subtraction angiography image enhancement[J]. IEEE Transactions on Nuclear Science, 2005, 52(1): 238~246.
[14] Prat W K. Digital image processing[M]. New York: Wiley Press, 1991.
[15] Barnea D I, Silverman H F. A class of algorithms for fast digital image registration[J]. IEEE Transactions on Computing, 1972, 21(2): 179~186.
[16] Viola P A, Wells W M. Alignment by maximization of mutual information[J]. InternationalJournal of Computer Vision, 1997, 24(2): 137~154.
[17] Zitova B, Flusser J. Image registration methods: a survey[J]. Image and Vision Computing. 2003, 24: 977~1000.
[18]钟家强,王润生.基于互信息相似性度量的多时相遥感图像配准[J].宇航学报, 2007, 27(4): 690~694.
[19]施颖琦,顾力栩.基于互信息多步骤优化的医学图像配准[J].计算机工程, 2006, 32(22): 187~188.
[20] Dufournaud Y, Schmid C, Horaud R. Image matching with scale adjust[J]. Computer Vision and Image Understanding, 2004, 93(2): 175~194.
[21]件建宁,郭宝龙,冯宗哲.一种基于兴趣点特征匹配的图像镶嵌技术[J].光电子.激光, 2006, 17(6): 733~737.
[22]薛模根,韩裕生,李从利,等.基于区域同名度矩阵的连续帧图像配[J].中国图象图形学报, 2006, 11(1): 53~59.
[23]刘贵喜,王蕾.基于区域选择和特征点匹配的图像配准算法[J].光电子.激光, 2007, 18(8): 999~1002.
[24] Liu Y R, Hou M L, Rao X J, et al. A steady corner detection of gray level images based on improved harris algorithm[C]. 5th International Conference on Networking, Sensing and Control, Sanya, China, 2008, 4: 708~713.
[25] Bouchafa S, Zavidovique B. Efficient cumulative matching for image registration[J]. Image and Computing, 2006, 24: 70~79.
[26]张鹏强,余旭初,韩丽,等.基于直线特征匹配的序列图像自动配准[J].武汉大学学报(信息科学版), 2007, 32(8): 676~679.
[27]堪安军,陈炜,毛士艺.一种基于边缘的图像配准方法[J].电子与信息学报, 2004, 26(5): 679~684.
[28]李树涛,王耀南,张昌凡.多传感器图像融合的客观评价与分析[J].仪器仪表学报, 2002, 23 (6): 651~654.
[29]周礼,王章野,金剑秋,等.基于HIS的小波图像融合新算法.中国图象图形学报, 2004, 9(9): 1088~1095.
[30]杨进,刘建波.一种改进的HIS图像融合新算法[J].华中科技大学学报, 2007, 35(8): 21~23.
[31]何贵青,郝重阳.特征量积和HIS变换的多源遥感图像融合方法[J].火力与指挥控制, 2006, 31 (11): 88~90.
[32] Gonzalez-Audicana M, Saleta J L, Catalan R G, et al. Fusion of multispectral and panchromatic images using improved HIS and PCA mergers based on wavelet decomposition[J]. Geoscience and Remote Sensing, 2004, 42(6): 1291~1230.
[33] Chen H X. A multiresolution image fusion based on principle component analysis[C]. 4th International Conference on Image and Graphics, Chengdu, Sichuan, 2007, 8: 737~741.
[34] Yue J, Yang R L, Huan R H. Pixel level fusion for multiple SAR images using PCA and wavelet transform[C]. 2006 CIE International Conference on Radar, Shanghai, China, 2006: 1~4.
[35]成云霞,田裕鹏.一种基于小波变换的图像融合方法[J].计算机与数字工程, 2006, 34(5): 75~77.
[36]燕文浩,马彩文,张鸣,等.基于小波变换的图像融合新算法[J].光子学报, 2006, 35(4): 638~640.
[37] Do M N, Vetterli M. The contourlet transform: an efficient directional multiresolution image representation[J]. IEEE Transactions On Image Processing, 2005, 14(12): 2091~2106.
[38]梁栋,李瑶,沈敏,等.一种基于小波-Contourlet变换的多聚集图像融合算法[J].电子学报, 2007, 2(35): 320~322.
[39]陈蜜,李德仁,秦前清,等.基于Contourlet变换的遥感影像融合算法[J].小型微型计算机系统, 2006, 27(11): 2052~2056.
[40]张继贤,李国胜,曾钰.多源遥感影像高精度自动配准的方法研究[J].遥感学报, 2005, 9(1): 73~77.
[41]于秋则,程辉,柳健,等.基于改进Hausdorff测度和遗传算法的SAR图像与光学图像匹配[J].宇航学报, 2006, 27(1): 130~134.
[42] Zhang Y, Chen Z G, Xu Y. Multi-spectral remote image registration based on SIFT[J]. Electronics Letters, 2008, 44(2): 107~108.
[43]薛弘晔,李言俊,张科.加权Hausdorff距离蚁群算法寻优的红外图像匹配[J].红外技术. 2007, 29(12): 708~711.
[44]李晖晖,郭雷,刘航.基于不同类型小波变换的SAR与可见光图像融合研究[J].光子学报, 2006, 35(8): 1263~1267.
[45]李晖晖,郭雷,李国新.基于脊波变换的SAR与可见光图像融合研究[J].西北工业大学学报, 2006, 24(4): 418~422.
[46] Zheng S, Shi W Z, Liu J, et al. Remote sensing image fusion using multiscale mapped LS-SVM[J]. Geoscience and Remote Sensing, 2008, 46(5): 1313~1322.
[47]刘盛鹏,方勇.基于Contourlet变换和IPCNN的融合算法及其在可见光与红外线图像融合中的应用[J].红外与毫米波学报, 2007, 26(3): 217~221.
[48]李卫华,周军.基于估计理论的红外与可见光图像融合算法[J].空军工程大学学报(自然科学版), 2006, 7(3): 44~47.
[49] Rebollo-Neira L and Lowe D. Optimized orthogonal matching pursuit approach[J]. IEEE Signal Processing Letters, 2002, 9: 137~140.
[50] Brown L G. A survey of image registration techniques[J]. ACM Computing Surveys, 1992, 24 (4): 325~376.
[51] Olson C F, Huttenlocher D P. Automatic target recognition by matching oriented edge pixels[J]. IEEE Transactions on Image Processing, 1997, 6(1): 103~112.
[52] You J, Bhattacharya P A. Wavelet-based coarse-to-fine image matching scheme in a parallel virtual machine environment[J]. IEEE Transactions on Image Processing, 2000, 9(9): 1547~1559.
[53] Yutaro Y, Hyoungseop K, Joo k T, et al. A method for reducing of computation time on image registration employing wavelet transformation[C]. International Conference on Control, Automation and Systems, Seoul, Korea, 2007: 1286~1291.
[54]张登荣,俞乐,蔡志刚.点特征和小波金字塔技术的遥感图像快速匹配技术[J].浙江大学学报(理学版), 2007, 34(4): 465~468.
[55]朱江,赵亦工.基于遗传算法的快速图像相关匹配[J].红外与毫米波学报, 1999, 18(2): 145~150.
[56]熊兴华,钱曾波,王任享.遗传算法与最小二乘法相结合的遥感图像子像素匹配[J].测绘学报, 2001, 30(1): 54~59.
[57] Chalermwat P, El-Ghazawi T, Le Moigne J. Two-phase genetic algorithm-based image registration on parallel clusters[J]. Journal of Future Generation Computing Systems, 2001, 17: 467~476.
[58]郑军,诸静.基于自适应遗传算法的图像匹配[J].浙江大学学报(工学版), 2003, 37(6): 689~692.
[59]徐建斌,洪文,吴一戎.基于遗传算法的遥感影像匹配定位的研究[J].测试技术学报, 2004, 18(4): 351~354.
[60]杨延西,刘丁,辛菁.模糊遗传图像相关匹配算法[J].仪器仪表学报, 2005, 26(11): 1166~1169.
[61]徐建斌,洪文,吴一戎.一种基于距离变换和遗传算法的遥感图像匹配算法[J].电子与信息学报, 2005, 27(7): 1009~1012.
[62]徐建斌,洪文,吴一戎.基于小波变换和遗传算法的遥感影像匹配方法的研究[J].电子与信息学报, 2005, 27(2): 283~285.
[63] Kennedy J,Eberhart R. Particle swarm optimization[C]. International Conference on Neural Networks, Piscataway, NJ, USA, 1995: 1942~1948.
[64] Pew-Thian Yap, Raceendran Paramesran. Senior Member, et al. Image analysis by Krawtchouk moments[J]. IEEE Transactions on Image Processing, 2003, 12(11): 1367~1377.
[65]易文娟,郁梅,蒋刚毅. Contourlet:一种有效的方向多尺度变换分析方法[J].计算机应用研究, 2006, 9(11): 18~22.
[66]胡望,李志蜀.一种更简化而高效的粒子群优化算法[J].软件学报, 2007, 18(4): 861~868.
[67]陈贵敏,贾建援,韩琪.粒子群优化算法的惯性权值递减策略研究[J].西安交通大学学报, 2006, 40(1): 53~56.
[68]孙瑾,顾宏斌,秦小麟,等.一种鲁棒型Hausdorff距离图像匹配方法[J].中国图象图形学报, 2008, 13(4): 761~767.
[69]周志强,汪渤.一种基于鲁棒Hausdorff距离的目标匹配算法[J].计算机应用, 2009, 29(1): 86~89.
[70] Sim D G, Kwon O K, Park R H. Object matching algorithms using robust hausdorff distance measures[J]. IEEE Transactions on Image Processing, 1999, 8(3): 425~429.
[71] Cole-Rhodes A A, Johnson K L, Le-Moigne J, et al. Multiresolution registration of remote sensing imagery by optimization of mutual information using a stochastic gradient[J]. Image Processing, 2003, 12(12): 1495~1511.
[72]田伟刚,郭雷,李晖晖,等.基于区域互信息的特征级多光谱图像配准[J].光电子.激光, 2008, 19(6): 799~783.
[73] Furuichi S. Information theoretical properties of Tsallis entropies[J]. Journal of Mathematical Physics, 2006, 47(2): 23302-1~18.
[74] Martin S, Morison G H, Nailon W H, et al. Fast and accurate image registration using Tsallis entropy and simultaneous perturbation stochastic approximation[J]. IET Electronics Letters, 2001, 40(10): 595~597.
[75] Waleed M and Ben Hamza A. Nonextensive entropic image registration[J]. Image Analysis and Recognition, 2009, 5627: 116~125.
[76] Skouson M B, Quji G, et al. A bound on mutual Iinformation for image registration[J]. IEEETransactions on Medical Imaging, 2001, 20(8): 843~846.
[77] Hua-Mei C, Varshney, P K., Arora, et al. Performance of mutual information similarity measure for registration of multi-temporal remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(11): 2445~2454.
[78]曹晓光,徐琳,郁文霞.基于角点检测的高精度点匹配算法[J].仪器仪表学报[J], 2006, 27(6): 1269~1271.
[79]王靖,朱梦宇,赵保军,等.基于小波和改进型Hausdorff距离的遥感图像配准方法[J].电子学报, 2006, 34(12): 2167~2169.
[80]苏娟,林行刚,刘代志.一种基于结构特征边缘的多传感器图像配准方法[J].自动化学报, 2009, 35(3): 251~257.
[81]田越,张永梅,李波.遥感图像的快速配准方法[J].北京航空航天大学学报, 2008, 34(11): 1356~1359.
[82] Qiguang M, Baoshu W. A novel image fusion method using contourlet transform[J]. IEEE Transactions On Image Processing, 2006, 6: 548~552.
[83]吴一全,李晓燕,陈飒.基于Contourlet域ICA和SVM的图像融合[J].光电子.激光, 2009, 20(6): 32~35.
[84]李光鑫,王珂.基于Contourlet变换的彩色图像融合算法[J].电子学报, 2007, 1(35):112~118.
[85] Arthur L C, Zhou J P, Do M N. The nonsubsampled contourlet transform theory, design and application[J]. IEEE Transaction on Image Processing, 2006, 15(10): 3089~3101.
[86]吴一全,陈飒,罗子娟.基于无下采样Contourlet变换的图像融合[J].中国体视学与图像分析, 2008, 13(2): 111~115.
[87] Zhu M Y, Yang Y L. A new image fusion algorithm based on fuzzy Logic[C]. International Conference on Intelligent Computation Technology and Automation, Changsha, Hunan, China, 2008, 2: 83~86.
[88] Chen S, Wu Y Q. Image fusion based on contourlet transform and fuzzy logic[C]. 2nd International Conference on Image and Signal Processing, Tianjing, China, 2009, 4: 2143~2147.