超光谱图像系统几何校正与图像配准方法研究
|
摘要
超光谱成像仪遥感能同时获得目标的空间和光谱信息。本文研究的超光谱成像仪是航天对地成像系统,由于各种因素的影响,会造成超光谱图像存在着一定的几何畸变,超光谱图像不能真实反映地物的光谱信息;本文研究的超光谱成像仪采用两个光谱仪来拓宽对地成像的光谱范围,这种方式会造成光谱仪波段图像之间的失配变形,影响地物目标的光谱纯度,降低超光谱图像的质量;另外,高光谱分辨率的超光谱图像需与高空间分辨率的全色波段图像融合,以增强图像的空间分辨率,而高精度的图像配准是融合的基本前提。本论文主要针对超光谱图像的系统几何校正、光谱仪图像配准、超光谱图像与全色波段图像的配准展开研究,完成了以下工作:
1.建立了超光谱遥感图像系统几何校正模型,利用仿真实验验证了该模型的可行性,并在此基础上构建了光谱仪图像失配模型,分析了垂直对地成像和沿轨摆扫运动补偿对地成像时光谱仪图像的失配变形,采用蒙特卡洛法分析了光谱仪图像在不同狭缝间距,不同星下点纬度时姿态角误差、指向镜角度误差对失配变形的影响,分析结果可以为超光谱成像仪狭缝设计以及光谱仪图像配准提供参考。
2.根据超光谱图像数据的特点,提出一种结合波段选择和主成分变换的方法,该方法能将三维超光谱图像转换为灰度图像以便图像的配准。波段选择方法能保证从光谱仪图像中选择相似性高的波段组合,主成分变换方法能提高生成图像的信息量和信噪比。
3.提出一种像素到像素的光谱仪图像配准方法,该方法首先通过局部图像窗口平移估计来计算图像的平移矢量场,然后利用估计的平移参数通过灰度插值来实现光谱仪图像的配准。其中局部图像窗口平移估计是该方法的关键,其估计精度决定了图像配准的质量,在相位相关方法的基础上提出了两种局部图像窗口平移估计方法:
(1)在相位相关空域估计方法中,提出一种改进的sinc函数拟合相位相关面估计平移参数的方法,实验结果显示该方法在光谱图像的配准精度能达到0.03个像素,比sinc拟合函数方法提高2～3倍,说明了提出的拟合函数能更准确地描述相位相关面的实际分布。将该方法应用到真实光谱仪图像配准实验中,结果表明该方法有效可行。
(2)在相位相关频域估计方法的基础上,提出一种基于核密度估计的平移参数估计方法,该方法将奇异值分解和核密度估计有机结合,奇异值分解能提高相位相关矩阵的质量,核密度估计能在受噪声干扰的相位分量中正确估计到平移参数。从对比实验来看,该方法比最小二乘法对噪声有更强的抗干扰能力,能有效提高平移参数估计精度和稳健性,在光谱图像的配准实验中精度能达到0.04个像素,比最小二乘拟合方法提高1倍。该方法可以做为上述空域估计方法的补充。
4.提出了一种基于特征点的全色波段图像和超光谱图像配准方法。该方法分为两步,首先利用传统的基于特征点的方法对待配准图像进行粗配准,然后利用提出的特征点位置优化方法对特征点位置进行优化处理,利用优化后的特征点对应关系对图像进行二次配准,实验结果表明该方法能显著提高图像配准的精度,满足全色波段图像和超光谱图像0.3个像素配准精度的需求。
Hyperspectral imagery can provide image data containing both spatial and spectral information. In this thesis, the Hyperspectral imagery was an aerospace earth observing system, because of the influences of various factors, the hyperspectral images would have geometric distortion that can’t real reflect the spectral characteristics of the objects on the Earth. Besides, the hyperspectral imagery collected data using two spectrometers to span a broad spectral range, this configuration caused an inherent misregistration between the two sets of spectral bands, the misregistration between spectral bands had detrimental effects on the spectral purity of each pixel which seriously degrade the quality of the hyperspectral images. Furthermore, to enhance the spatial resolution, the hyperspectral images were usually fusion with the high spatial resolution panchromatic image, the essential prerequisite for this was the precise image registration. Therefore, this thesis was focus on the method of the systematic geometric correction, spectral bands misregistration correction and the registration between the hyperspectral images and the panchromatic image. The accomplished research works as follows:
1. A hyperspectral imaging systematic geometric correction model was established, the feasibility of the model was verified by the simulation experiment. Based on this, a misregisation model was established and the misregistration deformation of the spectrometer images was analyzed when imaging vertically and imaging with motion compensation. With the Monte Carlo method, the influences on the misregistration deformation under different slit interval and different latitude of nadir point were analyzed. These results can be a reference for slit design and image registration.
2. Due to the characteristic of hyperspectral images, a method that combining the band selection and principal component transformation was proposed, which can convert 3-D images to grayscale image for image registration. The band selection method can guarantee the corresponding generated images to have the largest spatial similarity, and the principal component transformation can improve the information and SNR of the generated images.
3. A pixel-to-pixel image registration method for spectrometer images was proposed. Firstly, the motion vector was calculated by local image motion estimation method. Then, the motion vector was used to register the spectrometer images by intensity interpolation. The local image motion estimation method was the key technique which determines the accuracy of the image registration. Two methods for local image motion estimation were proposed based on the phase correlation method:
(1) A modified sinc fitting function to obtain a subpixel estimate of motion was proposed. The experiment results show that this new method can enable the estimation motion with 0.03 pixel accuracy , about 2-3 times higher than the sinc function fitting method. In the real spectrometer image registration experiments, the real results show that the method is feasible and efficient.
(2) A subpixel motion estimation method combining singular value decomposition and kernel density estimation in frequency domain was proposed. Singular value decomposition can improve the quality of the phase correlation matrix, and the kernel density estimation can estimate the motion correctly from the phase correlation corrupted by the noise. Comparative results show that this new method is more accurate and robust than the least square fitting method in the presence of noise. The registration accuracy can reach 0.04 pixel in spectral images registration, about 1 times higher than the least square fitting method. This method can be used as supplementary motion estimation method of the above method.
4. An image registration method Based on feature points between the panchromatic image and the hyperspectral images was proposed. The method consists of two steps: firstly, the images were roughly registered based on traditional method using feature points; secondly, a location optimization algorithm was used to improve the location of the Corresponding feature points, and then the image transformation was re-estimated. Experimental results show that this method can significantly improve the registration accuracy to meet the accuracy needs of 0.3 pixel between the panchromatic image and hyperspectral images registration.
1.建立了超光谱遥感图像系统几何校正模型,利用仿真实验验证了该模型的可行性,并在此基础上构建了光谱仪图像失配模型,分析了垂直对地成像和沿轨摆扫运动补偿对地成像时光谱仪图像的失配变形,采用蒙特卡洛法分析了光谱仪图像在不同狭缝间距,不同星下点纬度时姿态角误差、指向镜角度误差对失配变形的影响,分析结果可以为超光谱成像仪狭缝设计以及光谱仪图像配准提供参考。
2.根据超光谱图像数据的特点,提出一种结合波段选择和主成分变换的方法,该方法能将三维超光谱图像转换为灰度图像以便图像的配准。波段选择方法能保证从光谱仪图像中选择相似性高的波段组合,主成分变换方法能提高生成图像的信息量和信噪比。
3.提出一种像素到像素的光谱仪图像配准方法,该方法首先通过局部图像窗口平移估计来计算图像的平移矢量场,然后利用估计的平移参数通过灰度插值来实现光谱仪图像的配准。其中局部图像窗口平移估计是该方法的关键,其估计精度决定了图像配准的质量,在相位相关方法的基础上提出了两种局部图像窗口平移估计方法:
(1)在相位相关空域估计方法中,提出一种改进的sinc函数拟合相位相关面估计平移参数的方法,实验结果显示该方法在光谱图像的配准精度能达到0.03个像素,比sinc拟合函数方法提高2～3倍,说明了提出的拟合函数能更准确地描述相位相关面的实际分布。将该方法应用到真实光谱仪图像配准实验中,结果表明该方法有效可行。
(2)在相位相关频域估计方法的基础上,提出一种基于核密度估计的平移参数估计方法,该方法将奇异值分解和核密度估计有机结合,奇异值分解能提高相位相关矩阵的质量,核密度估计能在受噪声干扰的相位分量中正确估计到平移参数。从对比实验来看,该方法比最小二乘法对噪声有更强的抗干扰能力,能有效提高平移参数估计精度和稳健性,在光谱图像的配准实验中精度能达到0.04个像素,比最小二乘拟合方法提高1倍。该方法可以做为上述空域估计方法的补充。
4.提出了一种基于特征点的全色波段图像和超光谱图像配准方法。该方法分为两步,首先利用传统的基于特征点的方法对待配准图像进行粗配准,然后利用提出的特征点位置优化方法对特征点位置进行优化处理,利用优化后的特征点对应关系对图像进行二次配准,实验结果表明该方法能显著提高图像配准的精度,满足全色波段图像和超光谱图像0.3个像素配准精度的需求。
Hyperspectral imagery can provide image data containing both spatial and spectral information. In this thesis, the Hyperspectral imagery was an aerospace earth observing system, because of the influences of various factors, the hyperspectral images would have geometric distortion that can’t real reflect the spectral characteristics of the objects on the Earth. Besides, the hyperspectral imagery collected data using two spectrometers to span a broad spectral range, this configuration caused an inherent misregistration between the two sets of spectral bands, the misregistration between spectral bands had detrimental effects on the spectral purity of each pixel which seriously degrade the quality of the hyperspectral images. Furthermore, to enhance the spatial resolution, the hyperspectral images were usually fusion with the high spatial resolution panchromatic image, the essential prerequisite for this was the precise image registration. Therefore, this thesis was focus on the method of the systematic geometric correction, spectral bands misregistration correction and the registration between the hyperspectral images and the panchromatic image. The accomplished research works as follows:
1. A hyperspectral imaging systematic geometric correction model was established, the feasibility of the model was verified by the simulation experiment. Based on this, a misregisation model was established and the misregistration deformation of the spectrometer images was analyzed when imaging vertically and imaging with motion compensation. With the Monte Carlo method, the influences on the misregistration deformation under different slit interval and different latitude of nadir point were analyzed. These results can be a reference for slit design and image registration.
2. Due to the characteristic of hyperspectral images, a method that combining the band selection and principal component transformation was proposed, which can convert 3-D images to grayscale image for image registration. The band selection method can guarantee the corresponding generated images to have the largest spatial similarity, and the principal component transformation can improve the information and SNR of the generated images.
3. A pixel-to-pixel image registration method for spectrometer images was proposed. Firstly, the motion vector was calculated by local image motion estimation method. Then, the motion vector was used to register the spectrometer images by intensity interpolation. The local image motion estimation method was the key technique which determines the accuracy of the image registration. Two methods for local image motion estimation were proposed based on the phase correlation method:
(1) A modified sinc fitting function to obtain a subpixel estimate of motion was proposed. The experiment results show that this new method can enable the estimation motion with 0.03 pixel accuracy , about 2-3 times higher than the sinc function fitting method. In the real spectrometer image registration experiments, the real results show that the method is feasible and efficient.
(2) A subpixel motion estimation method combining singular value decomposition and kernel density estimation in frequency domain was proposed. Singular value decomposition can improve the quality of the phase correlation matrix, and the kernel density estimation can estimate the motion correctly from the phase correlation corrupted by the noise. Comparative results show that this new method is more accurate and robust than the least square fitting method in the presence of noise. The registration accuracy can reach 0.04 pixel in spectral images registration, about 1 times higher than the least square fitting method. This method can be used as supplementary motion estimation method of the above method.
4. An image registration method Based on feature points between the panchromatic image and the hyperspectral images was proposed. The method consists of two steps: firstly, the images were roughly registered based on traditional method using feature points; secondly, a location optimization algorithm was used to improve the location of the Corresponding feature points, and then the image transformation was re-estimated. Experimental results show that this method can significantly improve the registration accuracy to meet the accuracy needs of 0.3 pixel between the panchromatic image and hyperspectral images registration.
引文
[1]沈中,葛之江,张连台.航天超光谱成像仪技术原理及其发展现状[J].航天器工程,2001,10(4):45-52.
[2]张兵.时空信息辅助下的高光谱数据挖掘[D]:[博士学位论文].北京:中国科学院遥感应用研究所博士学位论文,2002.
[3]谭炳像,李增元,陈尔学,等. EO-1 Hyperion高光谱数据的预处理[J].应用技术,2005(6):36-41.
[4]王恒立,许强,陈永生,等.超光谱图像预处理技术[J].光电技术应用,2005,20(1):43-47.
[5]梁继,王建. Hyperion高光谱影响的分析预处理[J].冰川冻土,2009,31(2):247-253.
[6] Bisum Datt, Tim McVicar, Tom G et al,. Preprocessing EO-1 Hyperion Hyperspectral Data to Support the Application of Agricultural Indexes[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2003,41(6):1246-1259.
[7] Thomas Wilson, Curtiss Davis. Hyperspectral Remote Sensing Technology (HRST) program and the Naval EarthMap Observer (NEMO) satellite[C]. Part of the SPIE Conference on Infrared Spaceborne Remote Sensing VI,1998, 2-10.
[8] Davis C, Carder K, Bo Gao et al,. The Development of Imaging Spectrometry of the Coastal Ocean[C].Geoscience and Remote Sensing Symposium, IEEE International Conference on 2006.
[9] Leonard John, Andrew D, R Sellar. Spaceborne Fourier Transform Hyperspectral Imager[C]. Engineering, Construction, and Operations in Space V Proceedings of the Fifth International Conference on Space, 1996, 224-230.
[10] Jay Pearlman, Pamela Barry, Carol Segal et al,. Hyperion, a Space-Based Imaging Spectrometer [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2003, 41(6):1160-1173.
[11] D Beiso. Overview of Hyperion On-Orbit Instrument Performance, Stability, and Artifacts[C]. Proceedings of the 31st Applied Imagery Pattern Recognition Workshop, 2002.
[12] Cooley Thomas, Seigel Gary, Thorsos Ivan. Hyperspectral imaging from space: Warfighter-1 [C].Space technology and applications international forum, AIP Conference Proceedings, 1999(458): 747-752.
[13] Mike Cutter, Dan Lobb, Robert Cockshott. Compact High Resolution Imaging Spectrometer (CHRIS) [J]. Acta Astronautica, 2000, 46(2):263-268.
[14] M Corson, J Bowles, C Davis et al,. The HICO Program-Hyperspectral Imaging of the Coastal Ocean from the International Space Station [C]. IEEE International Geoscience and Remote Sensing Symposium, 2004, 4183-4185.
[15] Umberto B, Peter M, Roland Meynart et al,. PRISM: a hyperspectral imager for a future ESA land observation mission [C]. Remote Sensing for Agriculture, Forestry, and Natural Resources,1995.
[16] K Staenz, A Hollinger. A Canadian Hyperspectral Spaceborne Mission– Applications and User Requirements [C]. 3rd EARSeL Workshop on Imaging Spectroscopy, 2003, 31-35.
[17] Demetrio Labate, Massimo Ceccherini, Andrea Cisbani et al,. The PRISMApayload optomechanical design, a high performance instrument for a newhyperspectralmission [J]. Acta Astronautica, 2009,65:1429-1436.
[18]张雷.基于超光谱遥感图像三维特征的压缩方法研究[D]:[博士学位论文].长春:中国科学院长春光学精密机械与物理研究所,2005.
[19]倪国强,刘琼.多源图像配准技术分析与展望[J].光电工程,2004,31(9):1-6.
[20] B Zitova, J Flusser. Image registration methods: a survey [J]. Image and Vision Computing, 2003, 21:977-1000.
[21]陈正超,罗文斐,张浩,等.北京一号小卫星多光谱图像波段配准和图像变形评价[J].遥感学报,20066,10(5)690-696.
[22] Zvi Figov, Karni Wolowelsky, Nitzan Goldberg. Co-registration of hyperspectral bands [C]. Proc of SPIE Image and Signal Processing for Remote Sensing XIII, 2007, 6478.
[23]陈明生,谢耀华,杨强,等.遥感图像配准算法评述[J].测绘科学与工程,2007,27(1):55-58.
[24]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,2006,(7):690-694.
[25]马广彬,章文毅,刘定生.一种新的卫星多光谱图像自动配准算法[J].遥感技术与应用,2007,22(1):95:100.
[26] J Kern, M Pattichis. Robust Multispectral Image Registration Using Mutual-Information Models [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SNESING,2007,45(5):1494-1515.
[27] J Kern, M Pattichis, S Stearns. Registration of image cubes using multivariate mutual information [J]. in Proc. 37th Asilomar Conf.Signals, Syst., Comput., Nov. 2003, (2): 1645-1649.
[28]罗欣,郭雷,刘震.多元互信息在超光谱图像自动配准中的应用[J].计算机工程与应用,2006,(3):5-9.
[29]罗欣,郭雷,杨诸胜.基于互信息和随机优化的超光谱遥感图像配准[J].遥感技术与应用,2006,21(1):61-66.
[30] K Johnson, A Rhodes, I Zavorin et al,. Mutual Information as a Similarity Measure for Remote Sensing Image Registration [C].Proceeding of SPIE,Geo-Spatial Image and DataExploitation II,2001,4383:51-61.
[31] A Rhodes, L Johnson, J LeMoigne et al,. Multiresolution Registration of Remote Sensing Imagery by Optimization of Mutual Information Using a Stochastic Gradient [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2003,12(12):1495-1516.
[32] C. Kuglin, D. Hines. The phase correlation image alignment method [C] Proc. IEEE Inter National Conference Cybernetics and Society, September 1975, pp: 163-165.
[33] David J. Fleet. Disparity from Local Weighted Phase-Correlation [C]. IEEE International Conference on Systems, Man and Cybernetics, San Antonio, October 1994, pp: 48-56.
[34]邓红梅,吴四夫.基于相位相关算法的研究与实现[J].信息技术,2005(4): 19-20.
[35]邱庆军,徐可欣,蒋景英,虞启琏.结合小波变换与相位相关的图像匹配方法[J].信号处理,2003,19(5):469-472.
[36] Isamu Aoki, Toshinori Takahashi. Material Flow Analysis on Shearing Process by Applying Fourier Phase Correlation Method-Analysis of Piercing and Fine-blanking [J]. Journal of material processing technology,2003(124): 45-52.
[37] C Mandujano, S Mitra. Cross-Power spectrum phase for automated registration of Multi/Hyperspectral data-cubes for efficient information retrieval [J]. Fifth IEEE Southwest Symposium on Image Analysis and Interpretation,2002.
[38] Qin-sheng Chen, Michel Defrise, F. Deconinck. Symmetric Phase-Only Matched Filtering of Fourier-Mellin Transforms for Image Registration and Recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994,16(12)p: 1156-1168.
[39]强赞霞,彭嘉雄,王洪群.基于傅里叶变换的遥感图像配准[J].红外与激光工程,2004,,3(4):386-389.
[40] Hector Erives, Glenn Fitzgerald. Automated registration of hyperspectral images for precision agriculture [J]. Computers and Electronics in Agriculture, 2005:103-119.
[41] Hector Erives, Glenn Fitzgerald. Automated registration of hyperspectral images [J]. Remote Sensing and Modeling of Ecosystems for Sustainability. Proceedings of SPIE, vol.5544, Bellingham,328-335.2007.
[42] Hector Erives, Glenn Fitzgerald. Automated subpixel registration for a tunable hyperspectral imaging system [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2006, 3(3):397-340.
[43]唐玎,涂丹,甘亚莉.基于相位相关和模板匹配的亚像素图像配准参数估计[J].微电子学与计算机,2007,24(12):128-130.
[44] M David, N Nathan, M Jacqueline. Efficient algorithm for robust feature matching [J]. Pattern Recognition,1999, 32:17-38.
[45]周峰飞,陈卫东,李良福.一种基于Canny边缘的红外与可见光图像配准算法[J].应用光学,2009,30(4):605-609.
[46] L Manjunath, A Mitra. A contour-based approach to multisensor image registration [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 4(3):320-324.
[47] A Goshtasby. A symbolically-assisted approach to digital image registration with application in computer vision [D]. Dept, of Computer Science,Michigan State University,1983.
[48]文贡坚,吕金建,王继阳.基于特征的高精度自动图像配准方法[J]. Journal of Software,2008,19(9):2293-2301.
[49] P Moravec. Towards automatic visual obstacle avoidance [C]. Proceedings of the 5th International Joint Conference on Artificial Intelligence, 1977.
[50] C Harris, M Stephens. Combined corner and edge detector [C]. Proceedings of the 4th Alvey Vision Conference,1988.
[51] Y Bentoutou, N Taleb, K Kpalma,et al,.An Automatic Image Registration for Applications in Remote Sensing [J].IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2005,43(9):2127-2137.
[52] D Lowe. Object recognition from local scale-invariant features [J]. In ICCV, 1999, 1150-1157.
[53]李吉军,周尚波,陈虹,等.基于SIFT特征的多源遥感影像自动配准[J].测绘科学与工程,2008,28(4):48-52.
[54]李芳芳,肖本林,贾永红,等. SIFT算法优化及其用于遥感影像自动配准[J].武汉大学学报(信息科学版),2009,34(10):1245-1249.
[55]雷小群,李芳芳,肖本林.一种基于改进SIFT算法的遥感影像配准方法[J].测绘科学,2010,35(3):143:145.
[56]简剑锋,林怡,周利华,等.一种优化的多源遥感影像高精度配准方法[J].同济大学学报(自然科学版),2008,36(10):1427-1433.
[57]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):799-803.
[58] Jason Caseya, John Kerekes. Misregistration impacts on hyperspectral target detection [J].Journal of Applied Remote Sensing, Vol.3 2009:1-11.
[59] Joseph Meola, Michael Eismann. Image misregistration effects on hyperspectral change detection [C]. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, Proc. of SPIE Vol. 6966, 69660Y, (2008): 1-10.
[60]于海洋,甘甫平,党福星.高分辨率遥感影像波段配准误差试验分析[J].国土资源遥感,2007,(3):39-43.
[61]张过.缺少控制点的高分辨率卫星遥感影像几何纠正[D]:[博士学位论文]武汉:武汉大学,2005.
[62]邓晓嘉,朱建军.Quick Bird遥感影像的几何校正[J].现代测绘,2005,28(6):40-42.
[63]蔡喜琴,曹建君,蔡迪花,等.中巴地球资源卫星CCD影像几何纠正方法比较[J].遥感技术与应用,2006,21(4):396-398.
[64]张过,李扬,祝小勇,等.有理函数模型在光学卫星影像几何纠正中的应用[J].航天返回与遥感,2010,(4):78-82.
[65]徐青,张艳,邢帅等.遥感影像融合与分辨率增强技术[M].北京:科学出版社,2007.
[66]赵书河,冯学智.中巴资源一号卫星南京幅数据质量与几何纠正评价[J].遥感技术与应用,2000,15(3):170-174.
[67]彭泽,刘定生.北京一号小卫星几何纠正方法与试验[J].遥感应用,2008(1)74:77.
[68]王新民,邵贝恩. ETM数据的几何校正[J].自动化学报,1994,20(4):452-458.
[69]唐海荣,向茂生,朱敏慧.Landsat7图象系统级几何校正算法研究[J].中国图象图形学报,2003,8(9):1008-1014.
[70]宁晓刚,张晓东,胡进刚.SPOT5影像严格几何纠正方法研究[J].测绘信息与工程,2005,30(1):43-45.
[71]何挺,王静,程烨.OMIS图象几何校正研究[J].地理与地理信息科学,2005,21(1):23-26.
[72]王伟,童小华,谢锋,等.基于POS数据的OMIS影像几何校正[J].遥感应用,2010(1):89-94.
[73]徐卫明,王建宇,舒嵘,等.线阵推扫成像光谱仪几何校正误差的理论分析[J].红外与毫米波学报,2006,25(2):109-112.
[74]张浩,张兵,卫征.基于POS的高光谱影像快速几何纠正[J].测绘通报,2009(1):14-17.
[75]周小虎,万余庆.IMU在航空高光谱遥感图像几何校正中的应用[J].海洋科学进展,2004,22(增刊):95-100.
[76]徐青,张艳,邢帅等.遥感影像融合与分辨率增强技术[M].科学出版社,北京,2007.
[77]童庆禧,张兵,郑兰芬.高光谱遥感-原理、技术与应用[M].北京:高等教育出版社,2006。
[78]王之卓.摄影测量原理[M].武汉:武汉大学出版社,2007.
[79]赵森,许振宇,张乘权.地理坐标与直角坐标相互转换的算法[J].火力与指挥控制,2001,26(4),64-66.
[80]刘健,刘高峰.高斯-克吕格投影下的坐标变换算法研究[J].计算机仿真,2005,22(10),119-124.
[81]郭兆曾,赵阳.一种星载红外多光谱扫描相机成象仿真分析[J].遥感学报,1997,1(3),204-207.
[82]江万寿,张剑清,张祖勋.三线阵CCD卫星影像的模拟研究[J].武汉大学学报。信息科学版,2002,27(4),414-419.
[83] J Richards,X Jia.遥感数字图像分析(第四版)[M].北京:电子工业出版社,2008.
[84]王家骐.光学仪器总体设计[M].长春:长春光机所研究生部内部教材,2003.
[85]柳健,蒋英,彭复员,等.遥感卫星图像几何校正模型法[J].华中理工大学学报,1992,20(5):135-140.
[86]黎俊,彭启明,范植华.亚像素级图像配准算法研究[J].中国图象图形学报,2008,13(11):2070-2075.
[87]张本军,陶华学.亚像元配准技术研究[J].计算机工程与设计,2009,30(24):5681-5685.
[88]雷鸣,张广军.基于互相关的图像匹配亚像素定位[J].光电工程,2008,35(5):108-112.
[89]郭荣鑫,杨邦成,蔡光程,等.亚像素位移插值计算方法的比较分析[J].昆明理工大学学报(理工版),2005,30(2):55-59.
[90] Tian Q, HuhnsM N. Algorithm for subpixel registration [J]. CUGIP: Graphical Model andImage Processing,1986,35(2):220-223.
[91]张加万,韩真毅,张怡,等.基于灰度特征的一致性弹性配准算法[J].计算机应用,2009,29(10):2684-2687.
[92]孙辉,李志强,孙丽娜,等.基于相位相关的亚像素配准技术及其在电子稳像中的应用[J].中国光学与应用光学,2010,3(5):480-485.
[93] G Thomas. Television motion measurement for DATV and other applicatios[J]. Comm.,1987,41(4):604-612.
[94] Kenji TAKITA, Takafumi AOKI, Yoshifumi SASAKI et al,. High-Accuracy Subpixel Image Registration Based on Phase-Only Correlation [J]. IEICE TRANS. FUNDAMENTALS, 2003, E86(8):1925-1934.
[95] E Abdou. Practical approach to the registration of multiple frames of video images [J].Conf. Vis. Comm. Image Proc,Proc of SPIE,1999,(3653):371-382.
[96]甘亚莉,涂丹,李国辉.基于梯度相位相关的自动图像配准方法[J].微电子学与计算机,2007,2(7):1-3.
[97]甘亚莉,涂丹,李国辉.频率域基于梯度预处理的互相关图像配准方法[J].计算机工程与应用,2007,43(6):24-26.
[98] V Argyriou, T Vlachos. Estimation of sub-pixel motion using gradient cross-correlation [J]. ELECTRONICS LETTERS,2003,39(13):980-982.
[99] V Argyriou, T Vlachos. Motion measurement using gradient based correlation [C]. Proc. Int. Conf. on Visual Information Engineering, Guildford, UK, 2003,81-84.
[100] V Argyriou, T Vlachos. Performance study of gradient correlation for sub-pixel motion estimation in the frequency domain [J]. IEEE Proc.-Vis.Image Signal Process,2005,152(1):107-114.
[101] H. Foroosh, J. Zerubia, M. Berthod. Extension of phase correlation to subpixel registration [J]. IEEE Trans. On Image Processing, 2002,11(3):188-200.
[102]刘卫光.图像信息融合与识别[M].北京:电子工业出版社,2008,43-45.
[103] P Thevenaz, U Ruttimann, M Unser. A pyramidal approach to subpixel registration based on intensity [J]. IEEE Trans. on Image Processing,1998,7(1):27-41.
[104] Vasileios Argyriou, Theodore Vlachos. On the estimation of subpixel motion using phase correlation [J]. Journal of Electronic Imaging, 2007,16(3):1-8.
[105] S Nagashima, Takafumi Aoki, Tatsuo, Higuchi et al., A subpixel image matching technique using phase-only correlation [C]. 2006 International Symposium on Intelligent Signal Processing and Communication Systems, Tottori, Japan, 2006. 701-704.
[106] K. Takita, T. Aoki, Y. Sasaki et al., High-accuracy subpixel image registration based on phase-only correlation [C]. IEICE Trans. Fund., vol. E86-A, no. 8,pp.1925-1934,2003.
[107] Naoto Yokoya, Norihide Miyamura, Akira Iwasaki. Detection and correction of spectral and spatial misregistration for hyperspectral data using phase correlation method [J]. APPLIED OPTICS, 2010,49(24):4568-4575.
[108] Naoto Yokoya, Norihide Miyamurab, Akira Iwasakib. Preprocessing of hyperspectral imagery with consideration of smile and keystone properties[C]. Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications III, Proc. of SPIE, 2010, 7857: 1-10.
[109] Esteban Vera, Sergio Torres. Subpixel Accuracy Analysis of Phase Correlation Registration Methods Applied to Aliased Imagery [J]. 16th European Signal Processing Conference, Switzerland, 2008.
[110] Harold Stone, Michael Orchard Ee-Chien Chang et al,. A fast direct fourier-based algorithm for subpixel registration of images [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2001,39(10):2235-2243.
[111] William Scott Hoge. A Subspace Identification Extension to the Phase Correlation Method [J]. IEEE Trans. Medical Imaging, 2003, 22(2): 277-280.
[112] Hongshi Yan,Jianguo Liu. Robust phase correlation based feature matching for image co-registration and DEM generation [C]. International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, Beijing, 2008, Vol.XXXVII:1751-1755.
[113] Hanzi Wang, D Sutter. MDPE: A very robust estimator for model fitting and range image segmentation [J]. IEEE Trans. PAMI, 2004,59(2):139-166.
[114]张旭光,赵恩良,王延杰.基于Mean-shift的灰度目标跟踪新算法[J].光学技术,2007,33(2):226-229.
[115]刘春红.超光谱遥感图像降维及分类方法研究[D]:[博士学位论文].哈尔滨:哈尔滨工程大学信息与通信学院,2005.
[116]张多坤,田兆申,龙辉,等.基于图像匹配的星载遥感影像自动几何精校正算法[J].遥感技术与应用,2008,23(3):545-550.
[117]张迁,刘政凯,庞彦伟,等.基于SUSAN算法的航空影像的自动配准[J].测绘学报,2003,,32(3):231-235.
[118] Hu M K. Visual pattern recognition by moment invariants [J].IEEE Transactions on Information Theory,1962,8(2):179-187.
[119]邵泽明,朱剑英. RSTC不变矩图像特征点匹配新方法[J].华南理工大学学报(自然科学版),2008,36(8):37-40.
[120] Youcef Bentoutou, Nasreddine Taleb, Kidiyo Kpalma et al., An automatic image registration for applications in remote sensing [J].IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2005,43(9):2127-2137.
[121] KE Y, SUKTHANKAR R. PCA-SIFT: a more distinctive representation for local image descriptors [C].Proceedings Conference Computer Vision and Pattern Recognition,2004:511-517.
[122]张锐娟,张建奇,杨翠.基于SURF的图像配准方法研究[J].红外与激光工程,2009,38(1):160-165.
[123]彭海龙,邹斌,刘士斌.基于薄板样条函数的HY-1B卫星L1B数据几何校正方法研究[J].海洋通报,2009,28(5):106-109.
[124]廖斌.基于特征点的图像配准技术研究[D].[博士学位论文].长沙:国防科学技术大学,2008.
[125]马广彬,章文毅,刘定生,等.一种新的卫星多光谱图像自动配准算法[J].遥感技术与应用,2007,22(1):95-100.
[126]张登荣,蔡志刚,俞乐.基于匹配的遥感影像自动纠正方法研究[J].浙江大学学报(工学版),2007,41(3):402-406.
[127]潘兵,续伯钦,陈丁等.数字图像相关中亚像素位移测量的曲面拟合法[J].计量学报,2005,26(2):128-134.
[128]刘贵喜,刘冬梅,刘凤鹏,周亚平.一种稳健的特征点配准算法[J],光学学报,2008,28(3):454-461.
[129]马德政,杜云飞,周海芳,等.遥感图像配准中相似性测度的比较和分析[J],计算机工程与科学,2008,30(2):45-48
[130]冯宇平.图像快速配准与自动拼接技术研究[D]:[博士学位论文].长春:中国科学院长春光学精密机械与物理研究所,2010.
[131]雷鸣,张广军.基于互相关的图像匹配亚像素定位[J].光电工程,2008,35(5):108-113.
[132] Shaum S.Gleason, Martin A.Hunt, W.Bruce Jatko. Subpixel measurement of image features based on paraboloid surface fitting [C]. SPIE Vol. 1386. Machine Vision Systems Intetration in Industry.
[133] Irene Karybali, Emmanouil Psarakis, Kostas Berberidis et al,.An efficient spatial domain technique for subpixel image registration [J]. Signal Processing: Image Communication 2008(23):711-724.
[134] Emmanouil Psarakis, Georgios D. Evangelidis.An enhanced Correlation-Based Method for Stereo Correspondence with Sub-pixel Accuracy [C]. Proceedings of the 10th IEEE International Conference on Computer Vision, October 2005,Beijing,China.
[2]张兵.时空信息辅助下的高光谱数据挖掘[D]:[博士学位论文].北京:中国科学院遥感应用研究所博士学位论文,2002.
[3]谭炳像,李增元,陈尔学,等. EO-1 Hyperion高光谱数据的预处理[J].应用技术,2005(6):36-41.
[4]王恒立,许强,陈永生,等.超光谱图像预处理技术[J].光电技术应用,2005,20(1):43-47.
[5]梁继,王建. Hyperion高光谱影响的分析预处理[J].冰川冻土,2009,31(2):247-253.
[6] Bisum Datt, Tim McVicar, Tom G et al,. Preprocessing EO-1 Hyperion Hyperspectral Data to Support the Application of Agricultural Indexes[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2003,41(6):1246-1259.
[7] Thomas Wilson, Curtiss Davis. Hyperspectral Remote Sensing Technology (HRST) program and the Naval EarthMap Observer (NEMO) satellite[C]. Part of the SPIE Conference on Infrared Spaceborne Remote Sensing VI,1998, 2-10.
[8] Davis C, Carder K, Bo Gao et al,. The Development of Imaging Spectrometry of the Coastal Ocean[C].Geoscience and Remote Sensing Symposium, IEEE International Conference on 2006.
[9] Leonard John, Andrew D, R Sellar. Spaceborne Fourier Transform Hyperspectral Imager[C]. Engineering, Construction, and Operations in Space V Proceedings of the Fifth International Conference on Space, 1996, 224-230.
[10] Jay Pearlman, Pamela Barry, Carol Segal et al,. Hyperion, a Space-Based Imaging Spectrometer [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2003, 41(6):1160-1173.
[11] D Beiso. Overview of Hyperion On-Orbit Instrument Performance, Stability, and Artifacts[C]. Proceedings of the 31st Applied Imagery Pattern Recognition Workshop, 2002.
[12] Cooley Thomas, Seigel Gary, Thorsos Ivan. Hyperspectral imaging from space: Warfighter-1 [C].Space technology and applications international forum, AIP Conference Proceedings, 1999(458): 747-752.
[13] Mike Cutter, Dan Lobb, Robert Cockshott. Compact High Resolution Imaging Spectrometer (CHRIS) [J]. Acta Astronautica, 2000, 46(2):263-268.
[14] M Corson, J Bowles, C Davis et al,. The HICO Program-Hyperspectral Imaging of the Coastal Ocean from the International Space Station [C]. IEEE International Geoscience and Remote Sensing Symposium, 2004, 4183-4185.
[15] Umberto B, Peter M, Roland Meynart et al,. PRISM: a hyperspectral imager for a future ESA land observation mission [C]. Remote Sensing for Agriculture, Forestry, and Natural Resources,1995.
[16] K Staenz, A Hollinger. A Canadian Hyperspectral Spaceborne Mission– Applications and User Requirements [C]. 3rd EARSeL Workshop on Imaging Spectroscopy, 2003, 31-35.
[17] Demetrio Labate, Massimo Ceccherini, Andrea Cisbani et al,. The PRISMApayload optomechanical design, a high performance instrument for a newhyperspectralmission [J]. Acta Astronautica, 2009,65:1429-1436.
[18]张雷.基于超光谱遥感图像三维特征的压缩方法研究[D]:[博士学位论文].长春:中国科学院长春光学精密机械与物理研究所,2005.
[19]倪国强,刘琼.多源图像配准技术分析与展望[J].光电工程,2004,31(9):1-6.
[20] B Zitova, J Flusser. Image registration methods: a survey [J]. Image and Vision Computing, 2003, 21:977-1000.
[21]陈正超,罗文斐,张浩,等.北京一号小卫星多光谱图像波段配准和图像变形评价[J].遥感学报,20066,10(5)690-696.
[22] Zvi Figov, Karni Wolowelsky, Nitzan Goldberg. Co-registration of hyperspectral bands [C]. Proc of SPIE Image and Signal Processing for Remote Sensing XIII, 2007, 6478.
[23]陈明生,谢耀华,杨强,等.遥感图像配准算法评述[J].测绘科学与工程,2007,27(1):55-58.
[24]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,2006,(7):690-694.
[25]马广彬,章文毅,刘定生.一种新的卫星多光谱图像自动配准算法[J].遥感技术与应用,2007,22(1):95:100.
[26] J Kern, M Pattichis. Robust Multispectral Image Registration Using Mutual-Information Models [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SNESING,2007,45(5):1494-1515.
[27] J Kern, M Pattichis, S Stearns. Registration of image cubes using multivariate mutual information [J]. in Proc. 37th Asilomar Conf.Signals, Syst., Comput., Nov. 2003, (2): 1645-1649.
[28]罗欣,郭雷,刘震.多元互信息在超光谱图像自动配准中的应用[J].计算机工程与应用,2006,(3):5-9.
[29]罗欣,郭雷,杨诸胜.基于互信息和随机优化的超光谱遥感图像配准[J].遥感技术与应用,2006,21(1):61-66.
[30] K Johnson, A Rhodes, I Zavorin et al,. Mutual Information as a Similarity Measure for Remote Sensing Image Registration [C].Proceeding of SPIE,Geo-Spatial Image and DataExploitation II,2001,4383:51-61.
[31] A Rhodes, L Johnson, J LeMoigne et al,. Multiresolution Registration of Remote Sensing Imagery by Optimization of Mutual Information Using a Stochastic Gradient [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2003,12(12):1495-1516.
[32] C. Kuglin, D. Hines. The phase correlation image alignment method [C] Proc. IEEE Inter National Conference Cybernetics and Society, September 1975, pp: 163-165.
[33] David J. Fleet. Disparity from Local Weighted Phase-Correlation [C]. IEEE International Conference on Systems, Man and Cybernetics, San Antonio, October 1994, pp: 48-56.
[34]邓红梅,吴四夫.基于相位相关算法的研究与实现[J].信息技术,2005(4): 19-20.
[35]邱庆军,徐可欣,蒋景英,虞启琏.结合小波变换与相位相关的图像匹配方法[J].信号处理,2003,19(5):469-472.
[36] Isamu Aoki, Toshinori Takahashi. Material Flow Analysis on Shearing Process by Applying Fourier Phase Correlation Method-Analysis of Piercing and Fine-blanking [J]. Journal of material processing technology,2003(124): 45-52.
[37] C Mandujano, S Mitra. Cross-Power spectrum phase for automated registration of Multi/Hyperspectral data-cubes for efficient information retrieval [J]. Fifth IEEE Southwest Symposium on Image Analysis and Interpretation,2002.
[38] Qin-sheng Chen, Michel Defrise, F. Deconinck. Symmetric Phase-Only Matched Filtering of Fourier-Mellin Transforms for Image Registration and Recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994,16(12)p: 1156-1168.
[39]强赞霞,彭嘉雄,王洪群.基于傅里叶变换的遥感图像配准[J].红外与激光工程,2004,,3(4):386-389.
[40] Hector Erives, Glenn Fitzgerald. Automated registration of hyperspectral images for precision agriculture [J]. Computers and Electronics in Agriculture, 2005:103-119.
[41] Hector Erives, Glenn Fitzgerald. Automated registration of hyperspectral images [J]. Remote Sensing and Modeling of Ecosystems for Sustainability. Proceedings of SPIE, vol.5544, Bellingham,328-335.2007.
[42] Hector Erives, Glenn Fitzgerald. Automated subpixel registration for a tunable hyperspectral imaging system [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2006, 3(3):397-340.
[43]唐玎,涂丹,甘亚莉.基于相位相关和模板匹配的亚像素图像配准参数估计[J].微电子学与计算机,2007,24(12):128-130.
[44] M David, N Nathan, M Jacqueline. Efficient algorithm for robust feature matching [J]. Pattern Recognition,1999, 32:17-38.
[45]周峰飞,陈卫东,李良福.一种基于Canny边缘的红外与可见光图像配准算法[J].应用光学,2009,30(4):605-609.
[46] L Manjunath, A Mitra. A contour-based approach to multisensor image registration [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 4(3):320-324.
[47] A Goshtasby. A symbolically-assisted approach to digital image registration with application in computer vision [D]. Dept, of Computer Science,Michigan State University,1983.
[48]文贡坚,吕金建,王继阳.基于特征的高精度自动图像配准方法[J]. Journal of Software,2008,19(9):2293-2301.
[49] P Moravec. Towards automatic visual obstacle avoidance [C]. Proceedings of the 5th International Joint Conference on Artificial Intelligence, 1977.
[50] C Harris, M Stephens. Combined corner and edge detector [C]. Proceedings of the 4th Alvey Vision Conference,1988.
[51] Y Bentoutou, N Taleb, K Kpalma,et al,.An Automatic Image Registration for Applications in Remote Sensing [J].IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2005,43(9):2127-2137.
[52] D Lowe. Object recognition from local scale-invariant features [J]. In ICCV, 1999, 1150-1157.
[53]李吉军,周尚波,陈虹,等.基于SIFT特征的多源遥感影像自动配准[J].测绘科学与工程,2008,28(4):48-52.
[54]李芳芳,肖本林,贾永红,等. SIFT算法优化及其用于遥感影像自动配准[J].武汉大学学报(信息科学版),2009,34(10):1245-1249.
[55]雷小群,李芳芳,肖本林.一种基于改进SIFT算法的遥感影像配准方法[J].测绘科学,2010,35(3):143:145.
[56]简剑锋,林怡,周利华,等.一种优化的多源遥感影像高精度配准方法[J].同济大学学报(自然科学版),2008,36(10):1427-1433.
[57]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):799-803.
[58] Jason Caseya, John Kerekes. Misregistration impacts on hyperspectral target detection [J].Journal of Applied Remote Sensing, Vol.3 2009:1-11.
[59] Joseph Meola, Michael Eismann. Image misregistration effects on hyperspectral change detection [C]. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, Proc. of SPIE Vol. 6966, 69660Y, (2008): 1-10.
[60]于海洋,甘甫平,党福星.高分辨率遥感影像波段配准误差试验分析[J].国土资源遥感,2007,(3):39-43.
[61]张过.缺少控制点的高分辨率卫星遥感影像几何纠正[D]:[博士学位论文]武汉:武汉大学,2005.
[62]邓晓嘉,朱建军.Quick Bird遥感影像的几何校正[J].现代测绘,2005,28(6):40-42.
[63]蔡喜琴,曹建君,蔡迪花,等.中巴地球资源卫星CCD影像几何纠正方法比较[J].遥感技术与应用,2006,21(4):396-398.
[64]张过,李扬,祝小勇,等.有理函数模型在光学卫星影像几何纠正中的应用[J].航天返回与遥感,2010,(4):78-82.
[65]徐青,张艳,邢帅等.遥感影像融合与分辨率增强技术[M].北京:科学出版社,2007.
[66]赵书河,冯学智.中巴资源一号卫星南京幅数据质量与几何纠正评价[J].遥感技术与应用,2000,15(3):170-174.
[67]彭泽,刘定生.北京一号小卫星几何纠正方法与试验[J].遥感应用,2008(1)74:77.
[68]王新民,邵贝恩. ETM数据的几何校正[J].自动化学报,1994,20(4):452-458.
[69]唐海荣,向茂生,朱敏慧.Landsat7图象系统级几何校正算法研究[J].中国图象图形学报,2003,8(9):1008-1014.
[70]宁晓刚,张晓东,胡进刚.SPOT5影像严格几何纠正方法研究[J].测绘信息与工程,2005,30(1):43-45.
[71]何挺,王静,程烨.OMIS图象几何校正研究[J].地理与地理信息科学,2005,21(1):23-26.
[72]王伟,童小华,谢锋,等.基于POS数据的OMIS影像几何校正[J].遥感应用,2010(1):89-94.
[73]徐卫明,王建宇,舒嵘,等.线阵推扫成像光谱仪几何校正误差的理论分析[J].红外与毫米波学报,2006,25(2):109-112.
[74]张浩,张兵,卫征.基于POS的高光谱影像快速几何纠正[J].测绘通报,2009(1):14-17.
[75]周小虎,万余庆.IMU在航空高光谱遥感图像几何校正中的应用[J].海洋科学进展,2004,22(增刊):95-100.
[76]徐青,张艳,邢帅等.遥感影像融合与分辨率增强技术[M].科学出版社,北京,2007.
[77]童庆禧,张兵,郑兰芬.高光谱遥感-原理、技术与应用[M].北京:高等教育出版社,2006。
[78]王之卓.摄影测量原理[M].武汉:武汉大学出版社,2007.
[79]赵森,许振宇,张乘权.地理坐标与直角坐标相互转换的算法[J].火力与指挥控制,2001,26(4),64-66.
[80]刘健,刘高峰.高斯-克吕格投影下的坐标变换算法研究[J].计算机仿真,2005,22(10),119-124.
[81]郭兆曾,赵阳.一种星载红外多光谱扫描相机成象仿真分析[J].遥感学报,1997,1(3),204-207.
[82]江万寿,张剑清,张祖勋.三线阵CCD卫星影像的模拟研究[J].武汉大学学报。信息科学版,2002,27(4),414-419.
[83] J Richards,X Jia.遥感数字图像分析(第四版)[M].北京:电子工业出版社,2008.
[84]王家骐.光学仪器总体设计[M].长春:长春光机所研究生部内部教材,2003.
[85]柳健,蒋英,彭复员,等.遥感卫星图像几何校正模型法[J].华中理工大学学报,1992,20(5):135-140.
[86]黎俊,彭启明,范植华.亚像素级图像配准算法研究[J].中国图象图形学报,2008,13(11):2070-2075.
[87]张本军,陶华学.亚像元配准技术研究[J].计算机工程与设计,2009,30(24):5681-5685.
[88]雷鸣,张广军.基于互相关的图像匹配亚像素定位[J].光电工程,2008,35(5):108-112.
[89]郭荣鑫,杨邦成,蔡光程,等.亚像素位移插值计算方法的比较分析[J].昆明理工大学学报(理工版),2005,30(2):55-59.
[90] Tian Q, HuhnsM N. Algorithm for subpixel registration [J]. CUGIP: Graphical Model andImage Processing,1986,35(2):220-223.
[91]张加万,韩真毅,张怡,等.基于灰度特征的一致性弹性配准算法[J].计算机应用,2009,29(10):2684-2687.
[92]孙辉,李志强,孙丽娜,等.基于相位相关的亚像素配准技术及其在电子稳像中的应用[J].中国光学与应用光学,2010,3(5):480-485.
[93] G Thomas. Television motion measurement for DATV and other applicatios[J]. Comm.,1987,41(4):604-612.
[94] Kenji TAKITA, Takafumi AOKI, Yoshifumi SASAKI et al,. High-Accuracy Subpixel Image Registration Based on Phase-Only Correlation [J]. IEICE TRANS. FUNDAMENTALS, 2003, E86(8):1925-1934.
[95] E Abdou. Practical approach to the registration of multiple frames of video images [J].Conf. Vis. Comm. Image Proc,Proc of SPIE,1999,(3653):371-382.
[96]甘亚莉,涂丹,李国辉.基于梯度相位相关的自动图像配准方法[J].微电子学与计算机,2007,2(7):1-3.
[97]甘亚莉,涂丹,李国辉.频率域基于梯度预处理的互相关图像配准方法[J].计算机工程与应用,2007,43(6):24-26.
[98] V Argyriou, T Vlachos. Estimation of sub-pixel motion using gradient cross-correlation [J]. ELECTRONICS LETTERS,2003,39(13):980-982.
[99] V Argyriou, T Vlachos. Motion measurement using gradient based correlation [C]. Proc. Int. Conf. on Visual Information Engineering, Guildford, UK, 2003,81-84.
[100] V Argyriou, T Vlachos. Performance study of gradient correlation for sub-pixel motion estimation in the frequency domain [J]. IEEE Proc.-Vis.Image Signal Process,2005,152(1):107-114.
[101] H. Foroosh, J. Zerubia, M. Berthod. Extension of phase correlation to subpixel registration [J]. IEEE Trans. On Image Processing, 2002,11(3):188-200.
[102]刘卫光.图像信息融合与识别[M].北京:电子工业出版社,2008,43-45.
[103] P Thevenaz, U Ruttimann, M Unser. A pyramidal approach to subpixel registration based on intensity [J]. IEEE Trans. on Image Processing,1998,7(1):27-41.
[104] Vasileios Argyriou, Theodore Vlachos. On the estimation of subpixel motion using phase correlation [J]. Journal of Electronic Imaging, 2007,16(3):1-8.
[105] S Nagashima, Takafumi Aoki, Tatsuo, Higuchi et al., A subpixel image matching technique using phase-only correlation [C]. 2006 International Symposium on Intelligent Signal Processing and Communication Systems, Tottori, Japan, 2006. 701-704.
[106] K. Takita, T. Aoki, Y. Sasaki et al., High-accuracy subpixel image registration based on phase-only correlation [C]. IEICE Trans. Fund., vol. E86-A, no. 8,pp.1925-1934,2003.
[107] Naoto Yokoya, Norihide Miyamura, Akira Iwasaki. Detection and correction of spectral and spatial misregistration for hyperspectral data using phase correlation method [J]. APPLIED OPTICS, 2010,49(24):4568-4575.
[108] Naoto Yokoya, Norihide Miyamurab, Akira Iwasakib. Preprocessing of hyperspectral imagery with consideration of smile and keystone properties[C]. Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications III, Proc. of SPIE, 2010, 7857: 1-10.
[109] Esteban Vera, Sergio Torres. Subpixel Accuracy Analysis of Phase Correlation Registration Methods Applied to Aliased Imagery [J]. 16th European Signal Processing Conference, Switzerland, 2008.
[110] Harold Stone, Michael Orchard Ee-Chien Chang et al,. A fast direct fourier-based algorithm for subpixel registration of images [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2001,39(10):2235-2243.
[111] William Scott Hoge. A Subspace Identification Extension to the Phase Correlation Method [J]. IEEE Trans. Medical Imaging, 2003, 22(2): 277-280.
[112] Hongshi Yan,Jianguo Liu. Robust phase correlation based feature matching for image co-registration and DEM generation [C]. International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, Beijing, 2008, Vol.XXXVII:1751-1755.
[113] Hanzi Wang, D Sutter. MDPE: A very robust estimator for model fitting and range image segmentation [J]. IEEE Trans. PAMI, 2004,59(2):139-166.
[114]张旭光,赵恩良,王延杰.基于Mean-shift的灰度目标跟踪新算法[J].光学技术,2007,33(2):226-229.
[115]刘春红.超光谱遥感图像降维及分类方法研究[D]:[博士学位论文].哈尔滨:哈尔滨工程大学信息与通信学院,2005.
[116]张多坤,田兆申,龙辉,等.基于图像匹配的星载遥感影像自动几何精校正算法[J].遥感技术与应用,2008,23(3):545-550.
[117]张迁,刘政凯,庞彦伟,等.基于SUSAN算法的航空影像的自动配准[J].测绘学报,2003,,32(3):231-235.
[118] Hu M K. Visual pattern recognition by moment invariants [J].IEEE Transactions on Information Theory,1962,8(2):179-187.
[119]邵泽明,朱剑英. RSTC不变矩图像特征点匹配新方法[J].华南理工大学学报(自然科学版),2008,36(8):37-40.
[120] Youcef Bentoutou, Nasreddine Taleb, Kidiyo Kpalma et al., An automatic image registration for applications in remote sensing [J].IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2005,43(9):2127-2137.
[121] KE Y, SUKTHANKAR R. PCA-SIFT: a more distinctive representation for local image descriptors [C].Proceedings Conference Computer Vision and Pattern Recognition,2004:511-517.
[122]张锐娟,张建奇,杨翠.基于SURF的图像配准方法研究[J].红外与激光工程,2009,38(1):160-165.
[123]彭海龙,邹斌,刘士斌.基于薄板样条函数的HY-1B卫星L1B数据几何校正方法研究[J].海洋通报,2009,28(5):106-109.
[124]廖斌.基于特征点的图像配准技术研究[D].[博士学位论文].长沙:国防科学技术大学,2008.
[125]马广彬,章文毅,刘定生,等.一种新的卫星多光谱图像自动配准算法[J].遥感技术与应用,2007,22(1):95-100.
[126]张登荣,蔡志刚,俞乐.基于匹配的遥感影像自动纠正方法研究[J].浙江大学学报(工学版),2007,41(3):402-406.
[127]潘兵,续伯钦,陈丁等.数字图像相关中亚像素位移测量的曲面拟合法[J].计量学报,2005,26(2):128-134.
[128]刘贵喜,刘冬梅,刘凤鹏,周亚平.一种稳健的特征点配准算法[J],光学学报,2008,28(3):454-461.
[129]马德政,杜云飞,周海芳,等.遥感图像配准中相似性测度的比较和分析[J],计算机工程与科学,2008,30(2):45-48
[130]冯宇平.图像快速配准与自动拼接技术研究[D]:[博士学位论文].长春:中国科学院长春光学精密机械与物理研究所,2010.
[131]雷鸣,张广军.基于互相关的图像匹配亚像素定位[J].光电工程,2008,35(5):108-113.
[132] Shaum S.Gleason, Martin A.Hunt, W.Bruce Jatko. Subpixel measurement of image features based on paraboloid surface fitting [C]. SPIE Vol. 1386. Machine Vision Systems Intetration in Industry.
[133] Irene Karybali, Emmanouil Psarakis, Kostas Berberidis et al,.An efficient spatial domain technique for subpixel image registration [J]. Signal Processing: Image Communication 2008(23):711-724.
[134] Emmanouil Psarakis, Georgios D. Evangelidis.An enhanced Correlation-Based Method for Stereo Correspondence with Sub-pixel Accuracy [C]. Proceedings of the 10th IEEE International Conference on Computer Vision, October 2005,Beijing,China.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |