多源图像融合算法及应用研究
|
摘要
随着成像传感器技术的发展,越来越多的传感器被应用于各个领域中。传感器数量的增多导致获取的数据量急剧增加并呈现多样性,传统的信息处理方法已经不能满足新的需求。因此多源信息融合技术在这样的背景下应运而生。多源信息融合是指对来自多个传感器获得的多源信息进行多级别,多方面,多层次的处理和综合,从而获得更为丰富,精确和可靠的有用信息。而多源图像融合作为多源信息融合中以图像为对象的研究领域,综合了传感器、图像处理、计算机和人工智能等多个学科,其主要思想是通过采用一定的算法,将两个或多个具有互补或冗余特性的源图像融合成为新图像,使得融合后的图像能最大限度地利用互补信息,减少冗余,从而获得更高的清晰度和可理解性。
本论文集中探讨了多源图像融合这一课题产生的背景,研究的意义,融合的基本原理和体系结构,以及国内外已有的研究成果,并指出已有的研究工作中存在的不足之处。论文的研究内容即是围绕多源图像融合研究中一些存在的问题和不足之处展开的,具体内容包括基于多分辨率分解融合方法的工作总结和新思路,利用变分偏微分方程实现融合的探索,基于区域和多分辨率分解的特征级融合方向上的研究,如何实现图像融合性能的客观评价,以及多源图像特征级融合在实现道路目标识别上的具体应用。主要工作和创新成果如下:
1.总结已有的基于多分辨率分解的多源图像融合方法,提出利用双正交多小波变换的多光谱与全色图像融合。其中多小波变换作为单小波的扩展,增加了小波基的个数,从而能很好地解决对称性与正交性,支集长度与消失矩之间的矛盾。在融合策略上,为保留源图像多小波分解后尺度系数的显著性信息,提出利用平均与选择相结合实现融合。
2.提出基于特征保持的一阶对比度多聚焦图像融合方法。融合过程中根据源图像反映的不同特征的重要性,由特征图强度设计不同的权重,然后根据源图像的对比度信息的相应加权求取其主分量作为融合的目标梯度场,从而实现特征保持的图像融合。最后把融合算法拓展到彩色域,利用特征强度得到的权重实现彩色域中的高保真度融合。
3.由于在图像融合中,人们往往关注于图像中的实际目标或区域,而不是单个像素。因此我们提出结合双正交多小波和图分割的特征级融合方法,通过匹配度测试确定分割得到区域中的小波系数的融合规则。并且总结了基于多分辨率和区域的特征级融合的统一框架,分析单源分割和联合分割对于融合性能的影响,以及具体的融合策略的选取方法。
4.基于已有的客观评价方法指出具体的融合中评价指标的选取,提出基于图像结构相似性的客观评价方法。评价中主要考察源图像和融合图像之间的均值、方差相似性和相关系数,并且从灰度和梯度两个方向分别来做评价。
5.提出一种在低分辨率遥感图像中道路目标的全自动识别算法。算法利用感知编组确定由边缘检测和无效线段去除后得到的道路边缘线段组,然后确定道路种子点,基于动态规划的道路跟踪算法跟踪道路种子点生成整个道路网,最后利用知识推理去除道路虚警。
6.由于高分辨率遥感图像中的道路目标不仅包括低分辨率遥感图像中道路的拓扑特性,还包括高分辨率图像中的目标特性、光谱特性等。因此我们提出结合线性检测算子和分类结果的特征层融合道路识别算法。其中利用道路在高分辨率图象中可以被建模为具有某些统计行为的图像块,而这些图像块可以通过分类获得的特性,然后基于道路的拓扑关系辅助目标检测。
Various image sensors appear and their acquired data become explosive and multifarious with the fast development of micro-electronics. In this situation, traditional information processing cannot meet requirements and information fusion rises. The concept of information fusion indicates the information processing and synthesization of multilevel, multiaspect and multilayer. Multi-source image fusion is derived from information fusion and it includes digital image processing, computer science and artificial intelligence and so on. Image fusion is implemented by integrating multiple source images with redundant and complementary information into one result with better intelligibility and definition. In this way, redundancy can be reduced, while complementary information can be utilized more effectively.
This thesis studies the research backgrounds of multi-source image fusion and its significances, basic theories and systematic constructure of image fusion. Then we review the achievements of image fusion research at home and abroad. Following that, we set to our works on the deficiencies of existed works. Detailed contents consist of the reviewal and novel idea about multiresolution based image fusion, explorations in image fusion using PDEs, investigations into combination of multiresolution analysis and region segmentation, objective evaluations of various fusion methods and the applications of feature level fusion, such as road object recognition in high resolution multispectral images. The main work and innovations are listed as follows:
1. Based on a reviewal and summarization of existed image fusion methods which utilize multiresolution analysis, we propose a novel biorthogonal multiwavelet based multispectral and PAN image fusion algorithm. Increased number of basis in multiwavelet can handle the tradeoff between symmetry and orthogonality, filter length and vanishing moment. Meanwhile, combination of average and selection is used in the fusion scheme for preserving the salient information of scale coefficients from sources.
2. We propose a salience preserving based first-order contrast multi-focus image fusion method. According to the different importance of each source, we set different weights to each pixel in different source. Then weighted contrasts of sources are integrated into the target gradient of fused result. Moreover, we extend it into color domain by performing importance weight based color channel combination.
3. In image fusion, people pay more attentions to real object or region within sources, but not single pixels. Here we fuse optical and IR images by combing multiresolution analysis and region segmentation. Hereinto, match measure is utilized as the decision rules of wavelet coefficients. Meanwhile, we give a detailed analysis of the framework of multiresolution and region based feature level fusion, impacts of uni-modal and joint segmentation for fusion, selection of fusion rules.
4. We set forth most existed objective evaluation methods and how to adopt the objective evaluation method. An objective evaluation method based on structure similarity between sources and fused result is presented. The evaluation method takes the similarity of mean, variance and correlation into accounts and evaluates the fusion method from the viewpoint of gray values and gradients.
5. An automatic road recognition method in low resolution remote sensing image is proposed here. Hereinto, perceptual grouping is introduced to decide the road edge line group after edge detection and redundant line segments exclusion. Road seeds are generated accorading to candidate road edge line groups. Then road network is delineated by dynamic programming based road tracking. Finally false road segments are eliminated by knowledge ruling.
6. Road object has more traits in high resolution remote sensing images. It includes not only the topology, but also the spectral and shape traits and so on. Thus we proposed a road network recognition method, which combines both edge detection and spectral classification. Bacause roads in high resolution images can be modeled as image blocks with same statistic characteristics and these blocks can be acquired by classification.
本论文集中探讨了多源图像融合这一课题产生的背景,研究的意义,融合的基本原理和体系结构,以及国内外已有的研究成果,并指出已有的研究工作中存在的不足之处。论文的研究内容即是围绕多源图像融合研究中一些存在的问题和不足之处展开的,具体内容包括基于多分辨率分解融合方法的工作总结和新思路,利用变分偏微分方程实现融合的探索,基于区域和多分辨率分解的特征级融合方向上的研究,如何实现图像融合性能的客观评价,以及多源图像特征级融合在实现道路目标识别上的具体应用。主要工作和创新成果如下:
1.总结已有的基于多分辨率分解的多源图像融合方法,提出利用双正交多小波变换的多光谱与全色图像融合。其中多小波变换作为单小波的扩展,增加了小波基的个数,从而能很好地解决对称性与正交性,支集长度与消失矩之间的矛盾。在融合策略上,为保留源图像多小波分解后尺度系数的显著性信息,提出利用平均与选择相结合实现融合。
2.提出基于特征保持的一阶对比度多聚焦图像融合方法。融合过程中根据源图像反映的不同特征的重要性,由特征图强度设计不同的权重,然后根据源图像的对比度信息的相应加权求取其主分量作为融合的目标梯度场,从而实现特征保持的图像融合。最后把融合算法拓展到彩色域,利用特征强度得到的权重实现彩色域中的高保真度融合。
3.由于在图像融合中,人们往往关注于图像中的实际目标或区域,而不是单个像素。因此我们提出结合双正交多小波和图分割的特征级融合方法,通过匹配度测试确定分割得到区域中的小波系数的融合规则。并且总结了基于多分辨率和区域的特征级融合的统一框架,分析单源分割和联合分割对于融合性能的影响,以及具体的融合策略的选取方法。
4.基于已有的客观评价方法指出具体的融合中评价指标的选取,提出基于图像结构相似性的客观评价方法。评价中主要考察源图像和融合图像之间的均值、方差相似性和相关系数,并且从灰度和梯度两个方向分别来做评价。
5.提出一种在低分辨率遥感图像中道路目标的全自动识别算法。算法利用感知编组确定由边缘检测和无效线段去除后得到的道路边缘线段组,然后确定道路种子点,基于动态规划的道路跟踪算法跟踪道路种子点生成整个道路网,最后利用知识推理去除道路虚警。
6.由于高分辨率遥感图像中的道路目标不仅包括低分辨率遥感图像中道路的拓扑特性,还包括高分辨率图像中的目标特性、光谱特性等。因此我们提出结合线性检测算子和分类结果的特征层融合道路识别算法。其中利用道路在高分辨率图象中可以被建模为具有某些统计行为的图像块,而这些图像块可以通过分类获得的特性,然后基于道路的拓扑关系辅助目标检测。
Various image sensors appear and their acquired data become explosive and multifarious with the fast development of micro-electronics. In this situation, traditional information processing cannot meet requirements and information fusion rises. The concept of information fusion indicates the information processing and synthesization of multilevel, multiaspect and multilayer. Multi-source image fusion is derived from information fusion and it includes digital image processing, computer science and artificial intelligence and so on. Image fusion is implemented by integrating multiple source images with redundant and complementary information into one result with better intelligibility and definition. In this way, redundancy can be reduced, while complementary information can be utilized more effectively.
This thesis studies the research backgrounds of multi-source image fusion and its significances, basic theories and systematic constructure of image fusion. Then we review the achievements of image fusion research at home and abroad. Following that, we set to our works on the deficiencies of existed works. Detailed contents consist of the reviewal and novel idea about multiresolution based image fusion, explorations in image fusion using PDEs, investigations into combination of multiresolution analysis and region segmentation, objective evaluations of various fusion methods and the applications of feature level fusion, such as road object recognition in high resolution multispectral images. The main work and innovations are listed as follows:
1. Based on a reviewal and summarization of existed image fusion methods which utilize multiresolution analysis, we propose a novel biorthogonal multiwavelet based multispectral and PAN image fusion algorithm. Increased number of basis in multiwavelet can handle the tradeoff between symmetry and orthogonality, filter length and vanishing moment. Meanwhile, combination of average and selection is used in the fusion scheme for preserving the salient information of scale coefficients from sources.
2. We propose a salience preserving based first-order contrast multi-focus image fusion method. According to the different importance of each source, we set different weights to each pixel in different source. Then weighted contrasts of sources are integrated into the target gradient of fused result. Moreover, we extend it into color domain by performing importance weight based color channel combination.
3. In image fusion, people pay more attentions to real object or region within sources, but not single pixels. Here we fuse optical and IR images by combing multiresolution analysis and region segmentation. Hereinto, match measure is utilized as the decision rules of wavelet coefficients. Meanwhile, we give a detailed analysis of the framework of multiresolution and region based feature level fusion, impacts of uni-modal and joint segmentation for fusion, selection of fusion rules.
4. We set forth most existed objective evaluation methods and how to adopt the objective evaluation method. An objective evaluation method based on structure similarity between sources and fused result is presented. The evaluation method takes the similarity of mean, variance and correlation into accounts and evaluates the fusion method from the viewpoint of gray values and gradients.
5. An automatic road recognition method in low resolution remote sensing image is proposed here. Hereinto, perceptual grouping is introduced to decide the road edge line group after edge detection and redundant line segments exclusion. Road seeds are generated accorading to candidate road edge line groups. Then road network is delineated by dynamic programming based road tracking. Finally false road segments are eliminated by knowledge ruling.
6. Road object has more traits in high resolution remote sensing images. It includes not only the topology, but also the spectral and shape traits and so on. Thus we proposed a road network recognition method, which combines both edge detection and spectral classification. Bacause roads in high resolution images can be modeled as image blocks with same statistic characteristics and these blocks can be acquired by classification.
引文
[1]Kokar M.,Kim K.,Review of Multisensor Data Fusion Architecture and Techniques.Proceedings of the International Symposium on Intelligent Control.Chicago.USA.1993.261-266.
[2]Varshn P.K.,Multisensor Data Fusion.Electronics and Communication Engineering Journal.1997.541-552.
[3]Llinas J.,Edward W.,Multisensor data fusion,Boston,MA:Artech House,1990.
[4]Goshtasby A.A.,Image Fusion:Advances in the state of the art.Information Fusion.2007.8:114-118.
[5]Simone G.,Farina A,,Morabito F.C.,etc.Image Fusion Techniques for Remote Sensing Applications.Information Fusion.2002.3:3-15.
[6]Valet L.,Mauris G.,Bolon Ph.,A statistical overview of recent literature in information fusion.IEEE AESS Systems Magazine,2001,16(3):7-14.
[7]Piella G.,A General Framework for Multiresolution Image Fusion:from Pixels to Regions.Information Fusion.2003.4:259-280.
[8]刘刚,基于多分辨率的多传感器图像融合研究,上海交通大学博士学位论文,2005.
[9]Petrovic V.S.,Xydeas C.S.,Gradient-based Multiresolution Image Fusion.IEEE Trans.on Image Processing.2004.13(2):228-237.
[10]刘严岩,多传感器数据融合中几个关键技术的研究,中国科学技术大学博士学位论文,2006.
[11]赵英时 等蓍,遥感应用分析原理与方法.科学出版社,2003.
[12]Burt P.J.,Kolcznski R.J.,Enhanced Image Capture Through Fusion.IEEE 4th International Conf.on Computer Vision,1993.4:173-182.
[13]Scheunders P.,Orthogonal Wavelet Representation of Multivalue Images.IEEE Trans.on Image Processing.2003.12(6):718-725.
[14]Jia Y.H.,Fusion of Landsat TM and SAR Images based on Principal Component Analysis.Remote Sensing Technology and Application.1998.13(1):46-49.
[15]Melgani F.S.,Sebastiano B.,Vernazza G.,Fusion of Multitemporal Contextual Information by Neural Networks for Multisensor Remote Sensing Image Classification.International Geoscience and Remote Sensing Symposium(IGARSS).2001.7:2952-2954.
[16]Marengoni B.C.,Mauricio K.G.,Bayes Nets for Selective Perception and Data Fusion.Proceedings of SPIE.1995.2368:117-127.
[17]Solberg A.S.,Tact T.,Jain A.K.,A Markov Random Field Model for Classification of Multisource Satellite Imagery.IEEE Trans.on Geoscience and Remote Sensing.1996.34(1):100-113.
[18]Xia Y.,Leung H.,Bosse E.,Neural Data Fusion Algorithm Based on A Linearly Constrained Least Square Method.IEEE Trans.on Neural Networks.2002.13(2):320-329.
[19]倪国强,多波段图像融合算法研究及新发展(Ⅰ),光电子技术与信息,2001,14(5):11-17.
[20]Petrovic V.S.,Multisensor Pixel-level Image Fusion.Manchester School of Engineering.2001.
[21]Tseng D.C.,Chen Y.L.,Liu M.S.,Wavelet-based Multispectral Image Fusion.IEEE Int'l.Conf.on IGARSS.2001.4:1956-1958.
[22]Tu T.M.,Su S.C.,Shyu H.C.,Huang P.S.,A New Look at HIS-Like Image Fusion Methods.Information Fusion.2001.2:177-186.
[23]Yamamoto K.,Yamada K.,Image Processing and Fusion to Detect Navigation Obstacles.Proc SPIE.1998.3364:337-346.
[24]Shetigara V.,A Generalised Component Substitution Technique for Spatial Enhancement of Multispectral Image Using a Higher Resolution Data Set.Photogrammetric Engineering and Remote Sensing.1992.58(5):561-567.
[25]Pohl C.,Genderen J.V.,Multisensor Image Fusion in Remote Sensing:Concepts,Methods and Applications.Int.Journal Remote Sensing.1998.19(5):823-854.
[26]Toet A.,Walraven J.,New False Color Mapping for Image Fusion.Optical Engineering.1998.35(3):650-658.
[27]Geng B.Y.,Xu J.Z.,Yang J.Y.,An Approach Based on The Features of Space-Frequency Domain for Fusion of Edge Maps Obtained Through Multisensors.Systems Engineering and Electronics.2002.22(4):18-22.
[28]Smith S.,Scarff L.A.,Combing Visual and IR Images for Fusion-Two Approaches.Visual Data Interpretation.1992.1668:102-112.
[29]Wright W.A.,Fast Image Fusion with A Markov Random Field.Proc.Int.Conf.on Image Processing and Its Applications.Stevenage,UK:IEE.1999.557-561.
[30]刘刚,敬忠良,孙韶媛.基于期望值最大算法的图像融合.激光与红外,2005,35(2):130-133.
[31]Blum R.S.Robust image fusion using a statistical signal processing approach.Information Fusion,2005,(6):119-128.
[32]Melgani F.S.,Sebestiano B.,Vernazza G.,Fusion of Multitemporal Contextual Information by Neural Networks for Multisensor Remote Sensing Image Classification.Integrated Computer-Aided Engineering.2003.10(1):81-90.
[33]Mallat S.,A Compact Multiresolution Representation:the Wavelet Model.Proc.IEEE Computer Society Workshop on Computer Vision.Washington.D.C.2-7.
[34]Burt P.,The Pyramid as A Structure for Efficient Computation.Multiresolution Image Processing and Analysis.London:Springer-Veralg.1984.
[35]Zhang Z.,Blum R.S.,A Categorization of Multiscale-Decomposition-Based Image Fusion Schemes with A performance Study for A Digital Camera Application.Proceedings of the IEEE,1999.87(8):1315-132.
[36]Piella G.,A General Framework for Multiresolution Image Fusion:From Pixels to Regions,Information Fusion.2003.4(4):259-180.
[37]Burt P.J.,Adelson E.H.,The Laplacian Pyramid As A Compact Image Code.IEEE Trans on Communications.1983.31(4):532-540.
[38]Toet A.,Image Fusion by A Ratio of Low-Pass Pyramid.Pattern Recognition Letters.1989.9(4): 245-253.
[39]Zhou Y.T.,Multi-Sensor Image Fusion.Proc.of Int.Conf.on Image Processing.Los Alamitos:IEEE Computer Society.1994:193-197.
[40]Burt P.J.,A Gradient Pyramid Basis for Pattern Selective Image Fusion.Proc.of the Society for Information Display Conference.San Jose:SID Press.1992:467-470.
[41]Barron D.R.,Thomas O.D.L,Image Fusion Though Consideration of Texture Components.IEEE Trans on Electronics Letters.2001.37(12):746-748.
[42]Chipman L.J.,Orr T.M.,Graham L.N.,Wavelets and Image Fusion.Proc.of Int.Conf.on Image Processing.Los Alamitos:IEEE Computer Society.1995:248-251.
[43]Li H.,Manjunath B.S.,Mitra S.,Multisensor Image Fusion Using The Wavelet Transform.Graphical Models and Image Process.1995.57(3):235-245.
[44]Koren I.,Laine A.,Taylor F.,Image Fusion Using Steerable Dyadic Wavelet Transform.Proc.of Int.Conf.on Image Processing.Los Alamitos:IEEE Computer Society.1995:232-235.
[45]Liu Z.,Tsukada K.,Hanasaki K.,Image Fusion by Using Steerable Pyramid.Pattern Recognition Letters.2001.22(9):929-939.
[46]Chibani Y.,Houacine A.,Multiscale versus Multiresolution Analysis for Multisensor Image Fusion.Proc.Eusipco98,Rhodes,1998.
[47]Rochinger O.,Fechner T.,Pixel-Level Image Fusion:The Case of Image Sequences.Proc.SPIE 1998.3374:378-388.
[48]Oliver R.,Image Sequence Fusion Using A Shift Invariant Wavelet Transform.Proc.of Int.Conf.on Image Processing.Los Alamitos:IEEE Computer Society.1997.288-291.
[49]Nunez J.,Otazu X.,Fors O.,Multiresolution-based Image Fusion with Additive Wavelet Decomposition.IEEE Trans on Geoscience and Remote Sensing.1999.37(3):1204-1211.
[50]王海晖,彭嘉雄.基于多小波变换的图像融合研究.中国图象图形学报.2004.9(8):1002-1007.
[51]Mallat S.G.A.,Theory for Multiresolution Signal Decomposition:the Wavelet Representation.IEEE Trans.Pattern Anal.Machine Intell.1989.11(7):674-693.
[52]Kovacevic J.,Veterli M.Perfect Reconstruction Filter Banks with Rational Sampling Factors.IEEE Trans.Signal Processing.1993.41(6):2047-2066.
[53]Wickerhauser M.I.,Lectures on Wavelet Packet Algorithms.Lecture Notes.Department of Mathematics.Yale University.New Haven.1991.
[54]Gonzalez R.C.,Woods R.E.,Digital Image Processing(Second Edition).北京:电子工业出版社.2002.
[55]秦前清,杨宗凯.实用小波分析.西安:西安电子科技大学出版社,1998.
[56]Daubechies I.Ten lectures on wavelets.Philadelphia:Society for Industrial and Applied Mathematics.1992.
[57]Mallat S.Multiresolution Approximation and Wavelet Orthonormal Bases of L~2(R).IEEE Trans.mathematics Society.1989.6-7.
[58]Mallat S.A Theory for Multiresolution Signal Decomposition:The Wavelet Representation.IEEE Trans.Pattern Analysis and Machine Intelligence.1989.11(7):674-693.
[59]佘二永,多源图像融合方法研究,国防科技大学博士论文.2005.
[60]Vrabel J.,Multi-Spectral Imagery Advanced Band Sharping Study.Photogammetric Engineering and Remote Sensing.2000.66(1):73-79.
[61]Sunar F.,Musaoglu N.,Merging Multiresolution SPOT and Landsat TM Data:The Elects and Advantages.International Journal of Remote Sensing.1998.19(2):219-224.
[62]Zhang Y.,A New Merging Method and Its Spectral and Spatial Effects.International Journal of Remote Sensing.1999.20(10):2003-2014.
[63]Chavez P.S.,Stuart J.,Sides C.,et al.Comparison of three different methods to merge multiresolution and multispectral data:Landsat TM and SPOT panchromatic.Photogramm.Eng.Remote Sens,1991,57(3):259-303.
[64]Harris J.R.,IHS Transform for The Integration of Radar Imagery with Other Remotely Sensed Data.Photogammetric Engineering and Remote Sensing.1990.12;1631-1641.
[65]Maria G.A.,Jose L.S.,Raquel G.C.,Rafael G.,Fusion of Multispectral and Panchromatic Images Using Improved IHS and PCA Mergers Based on Wavelet Decomposition.IEEE trans.Geoscience and Remote Sensing.2004.42:1291-1299.
[66]Chavez P.S.,Digital Merging of Landsat TM and Digitized NHAP Data for 1:24.000 Scale Image mapping.Photogramm.Eng.Remote Sens.1986.52(10):1637-1646.
[67]Nunez J.,Multiresolution-Based Image Fusion with Additive Wavelet Decomposition.IEEE Trans.Geosci.Remote Sensing,1999.5:1204-1211.
[68]Scheunders P.,Backer S.D.,Fusion and Merging of Multispectral Images Using Multiscale Fundamental Forms.Jounal of the Optical Society of Amercia.2001.18(10):2468-2477.
[69]Strela V.Multiwavelets:Theory and Applications.Department of Mathematics,Massachusetts Institute of Technology,Massachusetts,USA,1996.
[70]Geronimo J.S.,Hardin D.P.,Massopust P.R.,Fractal Functions and Wavelet Expansions Based on Several Scaling Functions.Journal of Approximation Theory.1994.78(3):373-401.
[71]王海晖,彭嘉雄.基于多小波变换的图像融合研究.中国图象图形学报.2004.9(8):1002-1007.
[72]Strela V.,A Note on Construction of Biorthogonal Multi-Scaling Functions.Contemporary Mathematics,Wavelets Multiwavelets and Their Applications.AMS Philadelphia,Pennsylvania.USA.1998:149-157.
[73]Ranchin T.,Wald L.,Fusion of High Spatial and Spectral Resolution Images:The ARSIS Concept and Its Implementation.Photogramm.Eng.Remote Sens,2000,66(1):49-61.
[74]洪日昌,吴秀清,袁勋.基于双正交多小波的多光谱图像与全色图像融合研究.中国图像图形学报.2007.12(4):684-690.
[75]Ryuei N.,A Markov Random Field-Based Approach to Decision-Level Fusion for Remote Sensing Image Classification.IEEE Trans.Geoscience and Remote Sensing.2003,41(10):2316-2319.
[76]Randy P.B.,Steven K.R.,Mark E.O.,and Gregory L.T.,Physiologically Motivated Image Fusion for Object Detection using a Pulse Coupled Neural Network.IEEE Trans.Neural Networks,1999,10(3):554-563.
[77]洪日昌,吴秀清,杨浩.基于双正交多小波的SAR与可见光图像融合.测绘信息工程.2006.13(1):1-5.
[78]Glinton R.,Giampapa J.,Sycara K.,A Markov Random Field Model of Context for High-Level Information Fusion.International Conference on Information Fusion.2006.
[79]Nisbii R.,A Markov Random Field-Based Approach to Decision Level Fusion for Remote Sensing Image Classification,IEEE Trans.Geosci.Remote Sensing.2003.41:2316-2319.
[80]Socolinsky D.A.,Wolff L.B.,Multispectral Image Visualization Through First-order Fusion.IEEE Transactions on Image Processing.2002.11(8):923-931.
[81]Socolinsky D.A.,Wolff L.B.,A New Visualization Paradigm for Multispectral Imagery and Data Fusion.Proc.Computer Vision and Pattern Recognition.1999.(1):319-324.
[82]Socolinsky D.A,A Variational Approach to Image Fusion.Johns Hopkins University.2000.
[83]Socolinsky D.A.,Dynamic Range Constraint in Image Fusion and Visualization.Proc.Signal and Image Processing.IASTED2000.2000:349-354.
[84]Wang C.,Yang Q.,Tang X.,Ye Z.,Salience Preserving Image Fusion with Dynamic Range Compression,Int.Conf.on Image Processing.2006:989-992.
[85]Wang C.,Ye Z.,Perceptual Contrast-Based Image Fusion:A Variational Approach.Acta Automatica Sinica.2007.33(2):132-137.
[86]Wang C.,Ye Z.,First-Order Fusion of Volumetric Medical Imagery,IEE Proceedings- Vision,Image and Signal Processing.2006.153(2):191-198.
[87]王超,基于变分问题和偏微分方程的图像处理技术研究.中国科学技术大学博士论文.2007.
[88]Hong R.C.,Wang C.,Wang M.,Wu X.Q.,Zhang R.,Salience Preserving Multi-focus Image Fusion.IEEE International Conference on Multimedia & Expo.Beijing,China.July,2007.
[89]Hong R.C.,Wang C.,Wang M.,Mi J.,Wu X.Q.,Salience Preserving Multi-focus Image Fusion with Dynamic Range Compression.Submitted to Journal of Electronic Imaging.Submitted to Electronic Imaging.SPIE.
[90]Caselles V.,Morel J.M.,Sapiro G.,et al.Introduction to The Special Issue on Partial Differential Equations and Geometry-driven Diffusion in Image Processing and Analysis.IEEE Transactions on Image Processing.1998.7(3):269-273.
[91]Aubert G.,Kornprobst P.,Mathematical Problems in Image Processing:Partial Differential Equations and the Calculus of Variations.(147).AppliedMathematical Sciences:Springer-Verlag,New York,US,2001.
[92]Sapiro G.,Geometric Partial Differential Equations and Image Analysis.Cambridge University Press,Cambridge.UK.2001.
[93]Osher S.,Paragios N.,Geometric Level Set Methods in Imaging,Vision,and Graphics.Springer-Verlag.New York,US,2003.
[94]Kass M.,Witkin A.,Terzopoulos D.,Snakes:Active Contour Models.International Journal of Computer Vision.1987.1(4):321-331.
[95]Osher S.,Sethian J.,Fronts Propagating with Curvature Dependent Speed:Algorithms Based on Hamilton-Jacobi Formulation.Journal of Computational Physics.1988.79:12-49.
[96]Tikhonov A.N.,Arsenin V.Y.,Solutions of Ill-Posed Problems.Winston and Sons.Washington, D.C.1977.
[97]Rudin L.,Osher S.,Fatemi E.,Nonlinear Total Variation Based Noise Removal Algorithms.Physica D.1992.60:259-268.
[98]严镇军.数学物理方程(第二版).中国科学技术大学出版社.1996.
[99]苏家铎.泛函分析与变分法.中国科学技术大学出版社,合肥,1993.
[100]Aizawa K.,Kodama K.,Kubota A.,Producing Object-Based Special Effects by Fusing Multiple Differently Focused Images.IEEE Transactions on Circuits and Systems for Video Technology,2000.10(2):323-330.
[101]Li S.T.,Kwok J.T.,Tsang I.W.,Wang Y.N.,Fusing Images with Different Focuses Using Support Vector Machines.IEEE Transactions on Neural Networks.2004.15(6):1555-1561.
[102]Goshtasby A.Fusion of Multifocus Images to Maximize Image Information.Defense and Security symposium,Orlando,Florida,2006.17-21.
[103]Li S.T.,Kwok J.T.,Wang,Y.N.,Combination of images with diverse focuses using the spatial frequency.Inform.Fusion.2001.2:169-176.
[104]Bogoni L.,Hansen M.,Pattern-selective Color Image Fusion.Pattern Recognition,2001.34:1515-1526.
[105]De I.,Chanda B.,A Simple and Efficient Algorithm for Multifocus Image Fusion Using Morphological Wavelets.IEEE Transactions on Signal Processing.2006.86:924-936.
[106]V.Petrovic,C.Xydeas,On The Effects of Sensor Noise in Pixel-level Image Fusion Performance,Proc.of the Third International Conference on Image Fusion,2000.2:14-19.
[107]Ma Y.F.,Zhang H.J.,Contrast-based Image Attention Analysis by Using Fuzzy Growing.In Proceedings of ACM Multimedia.2003.374-381.
[108]Fattal R.,Lischinski D.,Werman D.,Gradient Domain High Dynamic Range Compression.Proceedings of ACM SIGGRAPH,also ACM Transactions on Graphics.2002.21(3):249-256.
[109]徐建平,桂子鹏.变分方法.同济大学出版社,上海,1999.
[110]Jeffereys S.H.,Swirles B.,Methods of Mathematical Physics.Cambridge University Press,Cambridge,UK,1972.
[111]Bhagavath S.,Wavelet-based Multi-sensor Image Fusion.http://vision.ece.ucsb.edul/～sitaram/ece594e.htm.
[112]Bogoni L.,Hansen M.,Pattern-selective Color Image Fusion.Pattern Recognition,2001,34:1515-1526.
[113]Petrovic V.,Xydeas C.,On the Effects of Sensor Noise in Pixel-Level Image Fusion Performance,in:Proceedings of the Third International Conference on Image Fusion.2000.2:14-19.
[114]Qu G.,Zhang D.,Yan P.,Information Measure for Performance of Image Fusion,Electronic Letters.2002.38(7):313-315.
[115]Wang Z.,Bovik A.,A universal Image Quality Index,Signal Processing Letters.2002.9(3):81-84.
[116]Li H.,Manjunath B.,Mitra S.,Multisensor Image Fusion Using the Wavelet Transform.Graphical Models and Image Processing,1995,57(3):235-245.
[117]Li S.,Kwok J.T.,Wang Y.,Combination of Images with Diverse Focuses Using the Spatial Frequency.Information Fusion.2001.2:169-176.
[118]Lewis J.J,O'callaghan R.J,Nikolov S.G,et al.Region-based Image Fusion Using Complex Wavelets.Proceeding of the 7th International Conference on Information Fusion.Jul.2004.Stockholm,Sweden.
[119]Hong R.C.,Ge Y.,Wu X.Q.,Multisensor Image Fusion Based on Biorthogonal Multiwavelet Transform and Region Competition.IEEE International Conference on Wavelet Analysis and Pattern Recognition.Beijing,China,Dec,2007.
[120]Yang J.,Blum R.S.,A Region-Based Image Fusion Method Using the Expectation-Maximization Algorithm.Proceedings of the Conference on Information Sciences and Systems,Princeton,NJ,March 2006.
[121]Felzenszwalb P.F.,Hutternlocher D.P.,Efficient graph-based image segmentation.Intl.Journal of Computer Vision.2004.59(2):167-181.
[122]Zhang Z.,Blum R.S.,A Region Based Image Fusion Scheme for Concealed Weapon Detection.Proc.Conference on Information Sciences and Systems.Baltimore,MD.1997.168-173.
[123]Sonka M.,Hlavae V.,Royle R.,Image Processing,Analysis and Machine Vision.Chapman &Hall Computing,1993.164-176.
[124]http://www.imagefusion.org
[125]洪日昌,吴秀清,袁勋.基于双正交多小波的多光谱图像与全色图像融合研究.中国图像图形学报.2007.12(4):684-690.[1-74]
[126]Piella G.,A General Framework for Multiresolution Image Fusion:From Pixels to Regions.Information fusion.2003,280:4-259.
[127]Matuszewski B.,Shark L.K.,Varley M.,Region-Based Wavelet Fusion of Ultrasonic Radiographic and Shearographyc Non-destructive Testing Images.Proceeding of the 15th world conference on non-destructive testing.Oct.2000.
[128]Pajares G.,Cruz J.M.,A Wavelet-Based Image Fusion Tutorial.Pattern Recognition.2004.37:1855-1872.
[129]Lewis J.J.,Nikolov S.G.,Canagarajah C.N.,Bull D.R.,Toet A.,Uni-modal Versus Joint Segmentation for Region-Based Image Fusion.Proceedings of the 9th International Conference on Information Fusion.Florence,Italy.July.2006.
[130]Zhang Y.J.,A Survey on Evaluation Methods for Image Segmentation.Pattern Recognition.29(8):1335-1346.1996.
[131]Deng Y.,Manjunath B.S.,Project:JSEG,Segmentation of Color-Texture Regions in Images and Video.http://vision.ece.ucsb.edu/segmentation/jseg/2004.
[132]王海晖,彭嘉雄,吴巍等.多源遥感图像融合效果评价方法研究.计算机工程与应用,2003.25:33-37.
[133]Pal N.R.,Pal S.K.,Entropy:A New Definition and Its Applications.IEEE Transcations on Systems.Man,and Cybernetics,1991.21(5):1260-1270.
[134]王文杰,唐娉,朱重光.一种基于小波变换的图像融合算法.中国图像图形学报,2001,6(11):1130-1136.
[135]Eskieioglu A.M.,Fisher P.S.,Image Quantity Measures and Their Performance.IEEE Transactions on Communications.1995.43(12):2959-2965.
[136]Xydeas C.S.,Petrovic V.S.,Objective Pixel-Level Image Fusion Performance Measure.Proceedings of SPIE.2000.4051:89-98.
[137]王昱.数字遥感影像构像质量评价方法初探.遥感信息,2000,15(4):32-33.
[138]Wald L,Ranchin T,Mangolini M.Fusion of Satellite Images of Different Spatial Resolutions:Assessing the Quality of Resulting Images.Photogrammetric Engineering and Remote Sensing,1997.63(3):691-699.
[139]贾永红,李德仁.选择最佳彩色变换用于遥感影像复合的定量评价方法.测绘通报,1997,12(1):2-4.
[140]Petrovic V.,Xydeas C.,On the Effects of Sensor Noise in Pixel-Level Image Fusion Performance,in:Proceedings of the Third International Conference on Image Fusion.2000.2:14-19.[2-37]
[141]Wang Z.,Bovik A.,A Universal Image Quality Index,Signal Processing Letters.2002.9(3):81-84.[2-39]
[142]Du Q.,Younan H.N.,King R.,Shah V.P.,On the Performance Evaluation of Pan-Sharpening Techniques.IEEE Geoscience and Remote Sensing Letters,2007.4(4):518-522.
[143]Qu G.,Zhang D.,Yan P.,Information Measure for Performance of Image Fusion.Electronics Letters,2002.38(7):313-315.
[144]Wang Z.,Bovik A.C.,Sheikh H.R.,Simoncelli E.P.,Image Quality Assessment:From Error Measurement to Structural Similarity.IEEE trans.Image Processing.13.Jan,2004.
[145]Yang C.,Zhang J.Q.,Wang X.R.,Liu X.,A Novel Similarity Based Quality Metric for Image Fusion.Information Fusion.2006.Sept.
[146]Alparone L.,Baronti S.,Garzelli A.,Nencini F.,A Global Quality Measurement of Pan-Sharpened Multispectral Imagery.IEEE Geoscience and Remote Sensing Letters,2004.1(4):313-317.
[147]王超,叶中付.基于相似性的图像融合质量的客观评估方法.软件学报.2006.17(7):1580-1587.
[148]李玲玲,丁明跃.像素级图像融合方法研究与应用.华中科技大学博士论文.2005.
[149]Gruen A.,Li H.,Road Extraction from Aerial and Satellite Images by Dynamic Programming.ISPRS Journal of Photogrammetry and Remote Sensing,1995,50(4):11-20.
[150]Donald G.,Jedynak B.,An Active Testing Model for Tracking Roads in Satellite Images.IEEE Transactions on Pattern Analysis and Machine Intelligence.1996.18(1):1-14.
[151]Gruen A.,Li H.,Semi-automatic Linear Feature Extraction by Dynamic Programming and LSB-Snakes.Photogrammetric Engineering and Remote Sensing,1997.63(8):985-995.
[152]文贡坚,王润生.从航空遥感影像中自动提取主要道路.软件学报,2000.11(7):957-964.
[153]Barzohar M.,Cooper D.B.,Automatic Finding of Main Roads Aerial Images by Using Geometric-Stochastic Models and Estimation.IEEE Transactions on Pattern Analysis and Machine Intelliganee.1996.18(7):707-721.
[154]Baumgartner A.,Extraction of Roads from Aerial Imagery Based on Grouping and Local Context.International Archives of Photogrammetry and Remote Sensing,Columbus,Ohio,1998.32:196--201.
[155]汪闽,骆剑承,周成虎等.结合高斯马尔可夫随机场纹理模型与支撑向量机在高分辨率遥感图像上提取道路网.遥感学报.2005.9(3):271-276.
[156]Fortier A.,Ziou D.,Armenalds C.,et al.Survey of Work on Road Extraction in Aerial and Satellite Images.Technical Report.URL:http://citeseer.nj.nec.com/fortier99survey.2004-03-04.
[157]Carsten S.,An Unbiased Detector of Curvilinear Structures.IEEE Transactions on Pattern Analysis and Machine Learning.1998.20(2):113-125.
[158]Miriam Amo,Fernando Martinez,and Margarita Torre.Road Extraction from Aerial Images Using a Region competition Algorithm.IEEE Transactions on Image Processing.2006,15(5):1192-1201
[159]贾承丽,匡纲要.SAR图像自动道路提取.中国图像图形学报,2005.10(10):1218-1223.
[160]Vosselman G,Knecht J.,Road Tracing by Profile Matching and Kalman Filtering.Automatic Extraction of Man-Made Objects from Aerial and Space Images.Birkhauser Verlag,Basel,Switzerland,1995.265-274.
[161]Offen R.J.,VLSI Image Processing.Beijing,Science Press,1989.
[162]任志清,尹东.一种中分辨率遥感影像道路的快速搜索算法.测绘信息工程,2006.31(1):15-16.
[163]Heipke C.,Mayer H.,Wiedemann C.,External Evaluation of Automatically Extracted Road Axes.Photogrammetric- Fernerkundung-Geoinformation,1998.(2):81-94.
[164]Mena J.B.,State of the Art on Automatic Road Extraction for GIS Update:A Novel Classification.Pattern Recognition Letters.2003.24:3037-3058.
[165]Lisini G.,Tison C.,Tupin F.,Gamba P.,Feature Fusion to Improve Road Network Extraction in High-Resolution SAR Images.IEEE Geoscience and Remote Sensing Letters.2006.3(2):217-221.
[166]Huber R.,Lang K,Road Extraction from High Resolution Airborne SAR Using Operator Fusion.Proc.IGARSS,Sidney,Australia.2001.2813-2815.
[167]Wessel B.,Context-Supported Road Extraction from SAP,Imagery:Transition from Rural to Built-up Areas.Proc.EUSAR 2004,Ulm Germany.2004.399-402.
[168]Acqua F.D.,Gamba P.,Detection of Urban Structures in SAR Image by Robust Fuzzy Clustering Algorithms:The Example of Street Tracking.IEEE Trans.Geosci.Remote Sens.2001.39(10):2287-2297.
[169]Acqua F.D.,Gamba P.,Lisini G.,Extraction and Fusion of Street Network from Fine Resolution SAR Data.Proc.IGARSS Toronto,ON,Canada.2002.1:89-91.
[170]Borghys D.,Perneel C.,Acheroy M.,A Multivariate Contour Detector for High-Resolution Polarimetric SAR Images.Proc.15th Int.Conf.Pattern Recognition.2000.3:646-651.
[171]洪日昌,吴秀清,刘媛,尹东.低分辨率遥感影像中道路的全自动提取研究.遥感学报.2008.
[172]Fischeler M.A.,Tenenbaum J.M.,Wolf H.C.,Detection of Roads and Linear Structures in Low Resolution Aerial Images Using Multi-source Knowledge Integration Techniques.Comput.Graph.Image Process.1981.15(3):201-223.
[173]McKeown D.W.,Denlinger J.L.,Cooperative Methods for Road Tracking in Aerial Imagery. Computer Vision and Pattern Recognition. CVPR'88, IEEE Press. 1988. 662-672.
[174]Tupin F., Maitre H., Detection of Linear Features in SAR Images: Application to Road Network Extraction. IEEE Trans. Geosci. Remote Sensing. 1998.36(2): 434-453.
[175]DelPAcqua F., Gamba P., Lisini G, Road Map Extraction by Multiple Detectors in Fine Spatial Resolution SAR Data. Can. J. Remote Sens. 2003. 29(4): 481-490.
[176]Doucette P., Agouris P., Musavi M., Stefanidis A., Automated Extraction of Linear Features from Aerial Imagery Using Kohonen Learning and GIS Data. Lecture Notes in Computer Science,Springer-Verlag: Berlin Heidelberg. 1999.1737: 20-33.
[177]Doucette P., Agouris P., Stefanidis A., Automated Road Extraction from High Resolution Multispectral Imagery. Photogrammetric Engineering & Remote Sensing, 2004. 70(12):1405-1416.
[178] Gao J., Wu L., Automatic Extraction of Road Networks in Urban Areas from Ikonos Imagery Based on Spatial Reasoning. Proceedings of ISPRS XXth Congress. 2004. July 12-23.
[179]Tarku K., Bretschneider T. R., Leedham C. G, Unsupervised Detection of Roads in High Resolution Panchromatic Satellite Images. Proceedings of the International Workshop Advanced Image Technology (IWAIT-2004). 15-19.
[180]Hu X., Tao C. V., Hu Y., Automatic Road Extraction from Dense Urban Area by Integrated Processing of High Resolution Imagery and Lidar Data. Proceedings of ISPRS XXth Congress.2004. 12-23.
[181] Zhang Y. Wang R., Multi-resolution and Multi-spectral Image Fusion for Urban Object Extraction.Proceedings of ISPRS XXth Congress. 2004.12-23.
[182] Song M., Civco D., Road Extraction Using SVM and Image Segmentation. Photogrammetric Engineering & Remote Sensing. 2004. 70(12):1365-1371.
[183] Zhang Q. P., Couloigner I., Automated Road Network Extraction from High Resolution Multi-spectral Imagery. ASPRS 2006 Annual Conference. 2006.
[184] Zhang Q., Couloigner I., Quality Assessment of Automated Road Extraction Based on Line Segment Matching. Proceedings of International Conference on Computational Science and its Applications (ICCSA2006). Glasgow, Scotland. 2006.
[2]Varshn P.K.,Multisensor Data Fusion.Electronics and Communication Engineering Journal.1997.541-552.
[3]Llinas J.,Edward W.,Multisensor data fusion,Boston,MA:Artech House,1990.
[4]Goshtasby A.A.,Image Fusion:Advances in the state of the art.Information Fusion.2007.8:114-118.
[5]Simone G.,Farina A,,Morabito F.C.,etc.Image Fusion Techniques for Remote Sensing Applications.Information Fusion.2002.3:3-15.
[6]Valet L.,Mauris G.,Bolon Ph.,A statistical overview of recent literature in information fusion.IEEE AESS Systems Magazine,2001,16(3):7-14.
[7]Piella G.,A General Framework for Multiresolution Image Fusion:from Pixels to Regions.Information Fusion.2003.4:259-280.
[8]刘刚,基于多分辨率的多传感器图像融合研究,上海交通大学博士学位论文,2005.
[9]Petrovic V.S.,Xydeas C.S.,Gradient-based Multiresolution Image Fusion.IEEE Trans.on Image Processing.2004.13(2):228-237.
[10]刘严岩,多传感器数据融合中几个关键技术的研究,中国科学技术大学博士学位论文,2006.
[11]赵英时 等蓍,遥感应用分析原理与方法.科学出版社,2003.
[12]Burt P.J.,Kolcznski R.J.,Enhanced Image Capture Through Fusion.IEEE 4th International Conf.on Computer Vision,1993.4:173-182.
[13]Scheunders P.,Orthogonal Wavelet Representation of Multivalue Images.IEEE Trans.on Image Processing.2003.12(6):718-725.
[14]Jia Y.H.,Fusion of Landsat TM and SAR Images based on Principal Component Analysis.Remote Sensing Technology and Application.1998.13(1):46-49.
[15]Melgani F.S.,Sebastiano B.,Vernazza G.,Fusion of Multitemporal Contextual Information by Neural Networks for Multisensor Remote Sensing Image Classification.International Geoscience and Remote Sensing Symposium(IGARSS).2001.7:2952-2954.
[16]Marengoni B.C.,Mauricio K.G.,Bayes Nets for Selective Perception and Data Fusion.Proceedings of SPIE.1995.2368:117-127.
[17]Solberg A.S.,Tact T.,Jain A.K.,A Markov Random Field Model for Classification of Multisource Satellite Imagery.IEEE Trans.on Geoscience and Remote Sensing.1996.34(1):100-113.
[18]Xia Y.,Leung H.,Bosse E.,Neural Data Fusion Algorithm Based on A Linearly Constrained Least Square Method.IEEE Trans.on Neural Networks.2002.13(2):320-329.
[19]倪国强,多波段图像融合算法研究及新发展(Ⅰ),光电子技术与信息,2001,14(5):11-17.
[20]Petrovic V.S.,Multisensor Pixel-level Image Fusion.Manchester School of Engineering.2001.
[21]Tseng D.C.,Chen Y.L.,Liu M.S.,Wavelet-based Multispectral Image Fusion.IEEE Int'l.Conf.on IGARSS.2001.4:1956-1958.
[22]Tu T.M.,Su S.C.,Shyu H.C.,Huang P.S.,A New Look at HIS-Like Image Fusion Methods.Information Fusion.2001.2:177-186.
[23]Yamamoto K.,Yamada K.,Image Processing and Fusion to Detect Navigation Obstacles.Proc SPIE.1998.3364:337-346.
[24]Shetigara V.,A Generalised Component Substitution Technique for Spatial Enhancement of Multispectral Image Using a Higher Resolution Data Set.Photogrammetric Engineering and Remote Sensing.1992.58(5):561-567.
[25]Pohl C.,Genderen J.V.,Multisensor Image Fusion in Remote Sensing:Concepts,Methods and Applications.Int.Journal Remote Sensing.1998.19(5):823-854.
[26]Toet A.,Walraven J.,New False Color Mapping for Image Fusion.Optical Engineering.1998.35(3):650-658.
[27]Geng B.Y.,Xu J.Z.,Yang J.Y.,An Approach Based on The Features of Space-Frequency Domain for Fusion of Edge Maps Obtained Through Multisensors.Systems Engineering and Electronics.2002.22(4):18-22.
[28]Smith S.,Scarff L.A.,Combing Visual and IR Images for Fusion-Two Approaches.Visual Data Interpretation.1992.1668:102-112.
[29]Wright W.A.,Fast Image Fusion with A Markov Random Field.Proc.Int.Conf.on Image Processing and Its Applications.Stevenage,UK:IEE.1999.557-561.
[30]刘刚,敬忠良,孙韶媛.基于期望值最大算法的图像融合.激光与红外,2005,35(2):130-133.
[31]Blum R.S.Robust image fusion using a statistical signal processing approach.Information Fusion,2005,(6):119-128.
[32]Melgani F.S.,Sebestiano B.,Vernazza G.,Fusion of Multitemporal Contextual Information by Neural Networks for Multisensor Remote Sensing Image Classification.Integrated Computer-Aided Engineering.2003.10(1):81-90.
[33]Mallat S.,A Compact Multiresolution Representation:the Wavelet Model.Proc.IEEE Computer Society Workshop on Computer Vision.Washington.D.C.2-7.
[34]Burt P.,The Pyramid as A Structure for Efficient Computation.Multiresolution Image Processing and Analysis.London:Springer-Veralg.1984.
[35]Zhang Z.,Blum R.S.,A Categorization of Multiscale-Decomposition-Based Image Fusion Schemes with A performance Study for A Digital Camera Application.Proceedings of the IEEE,1999.87(8):1315-132.
[36]Piella G.,A General Framework for Multiresolution Image Fusion:From Pixels to Regions,Information Fusion.2003.4(4):259-180.
[37]Burt P.J.,Adelson E.H.,The Laplacian Pyramid As A Compact Image Code.IEEE Trans on Communications.1983.31(4):532-540.
[38]Toet A.,Image Fusion by A Ratio of Low-Pass Pyramid.Pattern Recognition Letters.1989.9(4): 245-253.
[39]Zhou Y.T.,Multi-Sensor Image Fusion.Proc.of Int.Conf.on Image Processing.Los Alamitos:IEEE Computer Society.1994:193-197.
[40]Burt P.J.,A Gradient Pyramid Basis for Pattern Selective Image Fusion.Proc.of the Society for Information Display Conference.San Jose:SID Press.1992:467-470.
[41]Barron D.R.,Thomas O.D.L,Image Fusion Though Consideration of Texture Components.IEEE Trans on Electronics Letters.2001.37(12):746-748.
[42]Chipman L.J.,Orr T.M.,Graham L.N.,Wavelets and Image Fusion.Proc.of Int.Conf.on Image Processing.Los Alamitos:IEEE Computer Society.1995:248-251.
[43]Li H.,Manjunath B.S.,Mitra S.,Multisensor Image Fusion Using The Wavelet Transform.Graphical Models and Image Process.1995.57(3):235-245.
[44]Koren I.,Laine A.,Taylor F.,Image Fusion Using Steerable Dyadic Wavelet Transform.Proc.of Int.Conf.on Image Processing.Los Alamitos:IEEE Computer Society.1995:232-235.
[45]Liu Z.,Tsukada K.,Hanasaki K.,Image Fusion by Using Steerable Pyramid.Pattern Recognition Letters.2001.22(9):929-939.
[46]Chibani Y.,Houacine A.,Multiscale versus Multiresolution Analysis for Multisensor Image Fusion.Proc.Eusipco98,Rhodes,1998.
[47]Rochinger O.,Fechner T.,Pixel-Level Image Fusion:The Case of Image Sequences.Proc.SPIE 1998.3374:378-388.
[48]Oliver R.,Image Sequence Fusion Using A Shift Invariant Wavelet Transform.Proc.of Int.Conf.on Image Processing.Los Alamitos:IEEE Computer Society.1997.288-291.
[49]Nunez J.,Otazu X.,Fors O.,Multiresolution-based Image Fusion with Additive Wavelet Decomposition.IEEE Trans on Geoscience and Remote Sensing.1999.37(3):1204-1211.
[50]王海晖,彭嘉雄.基于多小波变换的图像融合研究.中国图象图形学报.2004.9(8):1002-1007.
[51]Mallat S.G.A.,Theory for Multiresolution Signal Decomposition:the Wavelet Representation.IEEE Trans.Pattern Anal.Machine Intell.1989.11(7):674-693.
[52]Kovacevic J.,Veterli M.Perfect Reconstruction Filter Banks with Rational Sampling Factors.IEEE Trans.Signal Processing.1993.41(6):2047-2066.
[53]Wickerhauser M.I.,Lectures on Wavelet Packet Algorithms.Lecture Notes.Department of Mathematics.Yale University.New Haven.1991.
[54]Gonzalez R.C.,Woods R.E.,Digital Image Processing(Second Edition).北京:电子工业出版社.2002.
[55]秦前清,杨宗凯.实用小波分析.西安:西安电子科技大学出版社,1998.
[56]Daubechies I.Ten lectures on wavelets.Philadelphia:Society for Industrial and Applied Mathematics.1992.
[57]Mallat S.Multiresolution Approximation and Wavelet Orthonormal Bases of L~2(R).IEEE Trans.mathematics Society.1989.6-7.
[58]Mallat S.A Theory for Multiresolution Signal Decomposition:The Wavelet Representation.IEEE Trans.Pattern Analysis and Machine Intelligence.1989.11(7):674-693.
[59]佘二永,多源图像融合方法研究,国防科技大学博士论文.2005.
[60]Vrabel J.,Multi-Spectral Imagery Advanced Band Sharping Study.Photogammetric Engineering and Remote Sensing.2000.66(1):73-79.
[61]Sunar F.,Musaoglu N.,Merging Multiresolution SPOT and Landsat TM Data:The Elects and Advantages.International Journal of Remote Sensing.1998.19(2):219-224.
[62]Zhang Y.,A New Merging Method and Its Spectral and Spatial Effects.International Journal of Remote Sensing.1999.20(10):2003-2014.
[63]Chavez P.S.,Stuart J.,Sides C.,et al.Comparison of three different methods to merge multiresolution and multispectral data:Landsat TM and SPOT panchromatic.Photogramm.Eng.Remote Sens,1991,57(3):259-303.
[64]Harris J.R.,IHS Transform for The Integration of Radar Imagery with Other Remotely Sensed Data.Photogammetric Engineering and Remote Sensing.1990.12;1631-1641.
[65]Maria G.A.,Jose L.S.,Raquel G.C.,Rafael G.,Fusion of Multispectral and Panchromatic Images Using Improved IHS and PCA Mergers Based on Wavelet Decomposition.IEEE trans.Geoscience and Remote Sensing.2004.42:1291-1299.
[66]Chavez P.S.,Digital Merging of Landsat TM and Digitized NHAP Data for 1:24.000 Scale Image mapping.Photogramm.Eng.Remote Sens.1986.52(10):1637-1646.
[67]Nunez J.,Multiresolution-Based Image Fusion with Additive Wavelet Decomposition.IEEE Trans.Geosci.Remote Sensing,1999.5:1204-1211.
[68]Scheunders P.,Backer S.D.,Fusion and Merging of Multispectral Images Using Multiscale Fundamental Forms.Jounal of the Optical Society of Amercia.2001.18(10):2468-2477.
[69]Strela V.Multiwavelets:Theory and Applications.Department of Mathematics,Massachusetts Institute of Technology,Massachusetts,USA,1996.
[70]Geronimo J.S.,Hardin D.P.,Massopust P.R.,Fractal Functions and Wavelet Expansions Based on Several Scaling Functions.Journal of Approximation Theory.1994.78(3):373-401.
[71]王海晖,彭嘉雄.基于多小波变换的图像融合研究.中国图象图形学报.2004.9(8):1002-1007.
[72]Strela V.,A Note on Construction of Biorthogonal Multi-Scaling Functions.Contemporary Mathematics,Wavelets Multiwavelets and Their Applications.AMS Philadelphia,Pennsylvania.USA.1998:149-157.
[73]Ranchin T.,Wald L.,Fusion of High Spatial and Spectral Resolution Images:The ARSIS Concept and Its Implementation.Photogramm.Eng.Remote Sens,2000,66(1):49-61.
[74]洪日昌,吴秀清,袁勋.基于双正交多小波的多光谱图像与全色图像融合研究.中国图像图形学报.2007.12(4):684-690.
[75]Ryuei N.,A Markov Random Field-Based Approach to Decision-Level Fusion for Remote Sensing Image Classification.IEEE Trans.Geoscience and Remote Sensing.2003,41(10):2316-2319.
[76]Randy P.B.,Steven K.R.,Mark E.O.,and Gregory L.T.,Physiologically Motivated Image Fusion for Object Detection using a Pulse Coupled Neural Network.IEEE Trans.Neural Networks,1999,10(3):554-563.
[77]洪日昌,吴秀清,杨浩.基于双正交多小波的SAR与可见光图像融合.测绘信息工程.2006.13(1):1-5.
[78]Glinton R.,Giampapa J.,Sycara K.,A Markov Random Field Model of Context for High-Level Information Fusion.International Conference on Information Fusion.2006.
[79]Nisbii R.,A Markov Random Field-Based Approach to Decision Level Fusion for Remote Sensing Image Classification,IEEE Trans.Geosci.Remote Sensing.2003.41:2316-2319.
[80]Socolinsky D.A.,Wolff L.B.,Multispectral Image Visualization Through First-order Fusion.IEEE Transactions on Image Processing.2002.11(8):923-931.
[81]Socolinsky D.A.,Wolff L.B.,A New Visualization Paradigm for Multispectral Imagery and Data Fusion.Proc.Computer Vision and Pattern Recognition.1999.(1):319-324.
[82]Socolinsky D.A,A Variational Approach to Image Fusion.Johns Hopkins University.2000.
[83]Socolinsky D.A.,Dynamic Range Constraint in Image Fusion and Visualization.Proc.Signal and Image Processing.IASTED2000.2000:349-354.
[84]Wang C.,Yang Q.,Tang X.,Ye Z.,Salience Preserving Image Fusion with Dynamic Range Compression,Int.Conf.on Image Processing.2006:989-992.
[85]Wang C.,Ye Z.,Perceptual Contrast-Based Image Fusion:A Variational Approach.Acta Automatica Sinica.2007.33(2):132-137.
[86]Wang C.,Ye Z.,First-Order Fusion of Volumetric Medical Imagery,IEE Proceedings- Vision,Image and Signal Processing.2006.153(2):191-198.
[87]王超,基于变分问题和偏微分方程的图像处理技术研究.中国科学技术大学博士论文.2007.
[88]Hong R.C.,Wang C.,Wang M.,Wu X.Q.,Zhang R.,Salience Preserving Multi-focus Image Fusion.IEEE International Conference on Multimedia & Expo.Beijing,China.July,2007.
[89]Hong R.C.,Wang C.,Wang M.,Mi J.,Wu X.Q.,Salience Preserving Multi-focus Image Fusion with Dynamic Range Compression.Submitted to Journal of Electronic Imaging.Submitted to Electronic Imaging.SPIE.
[90]Caselles V.,Morel J.M.,Sapiro G.,et al.Introduction to The Special Issue on Partial Differential Equations and Geometry-driven Diffusion in Image Processing and Analysis.IEEE Transactions on Image Processing.1998.7(3):269-273.
[91]Aubert G.,Kornprobst P.,Mathematical Problems in Image Processing:Partial Differential Equations and the Calculus of Variations.(147).AppliedMathematical Sciences:Springer-Verlag,New York,US,2001.
[92]Sapiro G.,Geometric Partial Differential Equations and Image Analysis.Cambridge University Press,Cambridge.UK.2001.
[93]Osher S.,Paragios N.,Geometric Level Set Methods in Imaging,Vision,and Graphics.Springer-Verlag.New York,US,2003.
[94]Kass M.,Witkin A.,Terzopoulos D.,Snakes:Active Contour Models.International Journal of Computer Vision.1987.1(4):321-331.
[95]Osher S.,Sethian J.,Fronts Propagating with Curvature Dependent Speed:Algorithms Based on Hamilton-Jacobi Formulation.Journal of Computational Physics.1988.79:12-49.
[96]Tikhonov A.N.,Arsenin V.Y.,Solutions of Ill-Posed Problems.Winston and Sons.Washington, D.C.1977.
[97]Rudin L.,Osher S.,Fatemi E.,Nonlinear Total Variation Based Noise Removal Algorithms.Physica D.1992.60:259-268.
[98]严镇军.数学物理方程(第二版).中国科学技术大学出版社.1996.
[99]苏家铎.泛函分析与变分法.中国科学技术大学出版社,合肥,1993.
[100]Aizawa K.,Kodama K.,Kubota A.,Producing Object-Based Special Effects by Fusing Multiple Differently Focused Images.IEEE Transactions on Circuits and Systems for Video Technology,2000.10(2):323-330.
[101]Li S.T.,Kwok J.T.,Tsang I.W.,Wang Y.N.,Fusing Images with Different Focuses Using Support Vector Machines.IEEE Transactions on Neural Networks.2004.15(6):1555-1561.
[102]Goshtasby A.Fusion of Multifocus Images to Maximize Image Information.Defense and Security symposium,Orlando,Florida,2006.17-21.
[103]Li S.T.,Kwok J.T.,Wang,Y.N.,Combination of images with diverse focuses using the spatial frequency.Inform.Fusion.2001.2:169-176.
[104]Bogoni L.,Hansen M.,Pattern-selective Color Image Fusion.Pattern Recognition,2001.34:1515-1526.
[105]De I.,Chanda B.,A Simple and Efficient Algorithm for Multifocus Image Fusion Using Morphological Wavelets.IEEE Transactions on Signal Processing.2006.86:924-936.
[106]V.Petrovic,C.Xydeas,On The Effects of Sensor Noise in Pixel-level Image Fusion Performance,Proc.of the Third International Conference on Image Fusion,2000.2:14-19.
[107]Ma Y.F.,Zhang H.J.,Contrast-based Image Attention Analysis by Using Fuzzy Growing.In Proceedings of ACM Multimedia.2003.374-381.
[108]Fattal R.,Lischinski D.,Werman D.,Gradient Domain High Dynamic Range Compression.Proceedings of ACM SIGGRAPH,also ACM Transactions on Graphics.2002.21(3):249-256.
[109]徐建平,桂子鹏.变分方法.同济大学出版社,上海,1999.
[110]Jeffereys S.H.,Swirles B.,Methods of Mathematical Physics.Cambridge University Press,Cambridge,UK,1972.
[111]Bhagavath S.,Wavelet-based Multi-sensor Image Fusion.http://vision.ece.ucsb.edul/～sitaram/ece594e.htm.
[112]Bogoni L.,Hansen M.,Pattern-selective Color Image Fusion.Pattern Recognition,2001,34:1515-1526.
[113]Petrovic V.,Xydeas C.,On the Effects of Sensor Noise in Pixel-Level Image Fusion Performance,in:Proceedings of the Third International Conference on Image Fusion.2000.2:14-19.
[114]Qu G.,Zhang D.,Yan P.,Information Measure for Performance of Image Fusion,Electronic Letters.2002.38(7):313-315.
[115]Wang Z.,Bovik A.,A universal Image Quality Index,Signal Processing Letters.2002.9(3):81-84.
[116]Li H.,Manjunath B.,Mitra S.,Multisensor Image Fusion Using the Wavelet Transform.Graphical Models and Image Processing,1995,57(3):235-245.
[117]Li S.,Kwok J.T.,Wang Y.,Combination of Images with Diverse Focuses Using the Spatial Frequency.Information Fusion.2001.2:169-176.
[118]Lewis J.J,O'callaghan R.J,Nikolov S.G,et al.Region-based Image Fusion Using Complex Wavelets.Proceeding of the 7th International Conference on Information Fusion.Jul.2004.Stockholm,Sweden.
[119]Hong R.C.,Ge Y.,Wu X.Q.,Multisensor Image Fusion Based on Biorthogonal Multiwavelet Transform and Region Competition.IEEE International Conference on Wavelet Analysis and Pattern Recognition.Beijing,China,Dec,2007.
[120]Yang J.,Blum R.S.,A Region-Based Image Fusion Method Using the Expectation-Maximization Algorithm.Proceedings of the Conference on Information Sciences and Systems,Princeton,NJ,March 2006.
[121]Felzenszwalb P.F.,Hutternlocher D.P.,Efficient graph-based image segmentation.Intl.Journal of Computer Vision.2004.59(2):167-181.
[122]Zhang Z.,Blum R.S.,A Region Based Image Fusion Scheme for Concealed Weapon Detection.Proc.Conference on Information Sciences and Systems.Baltimore,MD.1997.168-173.
[123]Sonka M.,Hlavae V.,Royle R.,Image Processing,Analysis and Machine Vision.Chapman &Hall Computing,1993.164-176.
[124]http://www.imagefusion.org
[125]洪日昌,吴秀清,袁勋.基于双正交多小波的多光谱图像与全色图像融合研究.中国图像图形学报.2007.12(4):684-690.[1-74]
[126]Piella G.,A General Framework for Multiresolution Image Fusion:From Pixels to Regions.Information fusion.2003,280:4-259.
[127]Matuszewski B.,Shark L.K.,Varley M.,Region-Based Wavelet Fusion of Ultrasonic Radiographic and Shearographyc Non-destructive Testing Images.Proceeding of the 15th world conference on non-destructive testing.Oct.2000.
[128]Pajares G.,Cruz J.M.,A Wavelet-Based Image Fusion Tutorial.Pattern Recognition.2004.37:1855-1872.
[129]Lewis J.J.,Nikolov S.G.,Canagarajah C.N.,Bull D.R.,Toet A.,Uni-modal Versus Joint Segmentation for Region-Based Image Fusion.Proceedings of the 9th International Conference on Information Fusion.Florence,Italy.July.2006.
[130]Zhang Y.J.,A Survey on Evaluation Methods for Image Segmentation.Pattern Recognition.29(8):1335-1346.1996.
[131]Deng Y.,Manjunath B.S.,Project:JSEG,Segmentation of Color-Texture Regions in Images and Video.http://vision.ece.ucsb.edu/segmentation/jseg/2004.
[132]王海晖,彭嘉雄,吴巍等.多源遥感图像融合效果评价方法研究.计算机工程与应用,2003.25:33-37.
[133]Pal N.R.,Pal S.K.,Entropy:A New Definition and Its Applications.IEEE Transcations on Systems.Man,and Cybernetics,1991.21(5):1260-1270.
[134]王文杰,唐娉,朱重光.一种基于小波变换的图像融合算法.中国图像图形学报,2001,6(11):1130-1136.
[135]Eskieioglu A.M.,Fisher P.S.,Image Quantity Measures and Their Performance.IEEE Transactions on Communications.1995.43(12):2959-2965.
[136]Xydeas C.S.,Petrovic V.S.,Objective Pixel-Level Image Fusion Performance Measure.Proceedings of SPIE.2000.4051:89-98.
[137]王昱.数字遥感影像构像质量评价方法初探.遥感信息,2000,15(4):32-33.
[138]Wald L,Ranchin T,Mangolini M.Fusion of Satellite Images of Different Spatial Resolutions:Assessing the Quality of Resulting Images.Photogrammetric Engineering and Remote Sensing,1997.63(3):691-699.
[139]贾永红,李德仁.选择最佳彩色变换用于遥感影像复合的定量评价方法.测绘通报,1997,12(1):2-4.
[140]Petrovic V.,Xydeas C.,On the Effects of Sensor Noise in Pixel-Level Image Fusion Performance,in:Proceedings of the Third International Conference on Image Fusion.2000.2:14-19.[2-37]
[141]Wang Z.,Bovik A.,A Universal Image Quality Index,Signal Processing Letters.2002.9(3):81-84.[2-39]
[142]Du Q.,Younan H.N.,King R.,Shah V.P.,On the Performance Evaluation of Pan-Sharpening Techniques.IEEE Geoscience and Remote Sensing Letters,2007.4(4):518-522.
[143]Qu G.,Zhang D.,Yan P.,Information Measure for Performance of Image Fusion.Electronics Letters,2002.38(7):313-315.
[144]Wang Z.,Bovik A.C.,Sheikh H.R.,Simoncelli E.P.,Image Quality Assessment:From Error Measurement to Structural Similarity.IEEE trans.Image Processing.13.Jan,2004.
[145]Yang C.,Zhang J.Q.,Wang X.R.,Liu X.,A Novel Similarity Based Quality Metric for Image Fusion.Information Fusion.2006.Sept.
[146]Alparone L.,Baronti S.,Garzelli A.,Nencini F.,A Global Quality Measurement of Pan-Sharpened Multispectral Imagery.IEEE Geoscience and Remote Sensing Letters,2004.1(4):313-317.
[147]王超,叶中付.基于相似性的图像融合质量的客观评估方法.软件学报.2006.17(7):1580-1587.
[148]李玲玲,丁明跃.像素级图像融合方法研究与应用.华中科技大学博士论文.2005.
[149]Gruen A.,Li H.,Road Extraction from Aerial and Satellite Images by Dynamic Programming.ISPRS Journal of Photogrammetry and Remote Sensing,1995,50(4):11-20.
[150]Donald G.,Jedynak B.,An Active Testing Model for Tracking Roads in Satellite Images.IEEE Transactions on Pattern Analysis and Machine Intelligence.1996.18(1):1-14.
[151]Gruen A.,Li H.,Semi-automatic Linear Feature Extraction by Dynamic Programming and LSB-Snakes.Photogrammetric Engineering and Remote Sensing,1997.63(8):985-995.
[152]文贡坚,王润生.从航空遥感影像中自动提取主要道路.软件学报,2000.11(7):957-964.
[153]Barzohar M.,Cooper D.B.,Automatic Finding of Main Roads Aerial Images by Using Geometric-Stochastic Models and Estimation.IEEE Transactions on Pattern Analysis and Machine Intelliganee.1996.18(7):707-721.
[154]Baumgartner A.,Extraction of Roads from Aerial Imagery Based on Grouping and Local Context.International Archives of Photogrammetry and Remote Sensing,Columbus,Ohio,1998.32:196--201.
[155]汪闽,骆剑承,周成虎等.结合高斯马尔可夫随机场纹理模型与支撑向量机在高分辨率遥感图像上提取道路网.遥感学报.2005.9(3):271-276.
[156]Fortier A.,Ziou D.,Armenalds C.,et al.Survey of Work on Road Extraction in Aerial and Satellite Images.Technical Report.URL:http://citeseer.nj.nec.com/fortier99survey.2004-03-04.
[157]Carsten S.,An Unbiased Detector of Curvilinear Structures.IEEE Transactions on Pattern Analysis and Machine Learning.1998.20(2):113-125.
[158]Miriam Amo,Fernando Martinez,and Margarita Torre.Road Extraction from Aerial Images Using a Region competition Algorithm.IEEE Transactions on Image Processing.2006,15(5):1192-1201
[159]贾承丽,匡纲要.SAR图像自动道路提取.中国图像图形学报,2005.10(10):1218-1223.
[160]Vosselman G,Knecht J.,Road Tracing by Profile Matching and Kalman Filtering.Automatic Extraction of Man-Made Objects from Aerial and Space Images.Birkhauser Verlag,Basel,Switzerland,1995.265-274.
[161]Offen R.J.,VLSI Image Processing.Beijing,Science Press,1989.
[162]任志清,尹东.一种中分辨率遥感影像道路的快速搜索算法.测绘信息工程,2006.31(1):15-16.
[163]Heipke C.,Mayer H.,Wiedemann C.,External Evaluation of Automatically Extracted Road Axes.Photogrammetric- Fernerkundung-Geoinformation,1998.(2):81-94.
[164]Mena J.B.,State of the Art on Automatic Road Extraction for GIS Update:A Novel Classification.Pattern Recognition Letters.2003.24:3037-3058.
[165]Lisini G.,Tison C.,Tupin F.,Gamba P.,Feature Fusion to Improve Road Network Extraction in High-Resolution SAR Images.IEEE Geoscience and Remote Sensing Letters.2006.3(2):217-221.
[166]Huber R.,Lang K,Road Extraction from High Resolution Airborne SAR Using Operator Fusion.Proc.IGARSS,Sidney,Australia.2001.2813-2815.
[167]Wessel B.,Context-Supported Road Extraction from SAP,Imagery:Transition from Rural to Built-up Areas.Proc.EUSAR 2004,Ulm Germany.2004.399-402.
[168]Acqua F.D.,Gamba P.,Detection of Urban Structures in SAR Image by Robust Fuzzy Clustering Algorithms:The Example of Street Tracking.IEEE Trans.Geosci.Remote Sens.2001.39(10):2287-2297.
[169]Acqua F.D.,Gamba P.,Lisini G.,Extraction and Fusion of Street Network from Fine Resolution SAR Data.Proc.IGARSS Toronto,ON,Canada.2002.1:89-91.
[170]Borghys D.,Perneel C.,Acheroy M.,A Multivariate Contour Detector for High-Resolution Polarimetric SAR Images.Proc.15th Int.Conf.Pattern Recognition.2000.3:646-651.
[171]洪日昌,吴秀清,刘媛,尹东.低分辨率遥感影像中道路的全自动提取研究.遥感学报.2008.
[172]Fischeler M.A.,Tenenbaum J.M.,Wolf H.C.,Detection of Roads and Linear Structures in Low Resolution Aerial Images Using Multi-source Knowledge Integration Techniques.Comput.Graph.Image Process.1981.15(3):201-223.
[173]McKeown D.W.,Denlinger J.L.,Cooperative Methods for Road Tracking in Aerial Imagery. Computer Vision and Pattern Recognition. CVPR'88, IEEE Press. 1988. 662-672.
[174]Tupin F., Maitre H., Detection of Linear Features in SAR Images: Application to Road Network Extraction. IEEE Trans. Geosci. Remote Sensing. 1998.36(2): 434-453.
[175]DelPAcqua F., Gamba P., Lisini G, Road Map Extraction by Multiple Detectors in Fine Spatial Resolution SAR Data. Can. J. Remote Sens. 2003. 29(4): 481-490.
[176]Doucette P., Agouris P., Musavi M., Stefanidis A., Automated Extraction of Linear Features from Aerial Imagery Using Kohonen Learning and GIS Data. Lecture Notes in Computer Science,Springer-Verlag: Berlin Heidelberg. 1999.1737: 20-33.
[177]Doucette P., Agouris P., Stefanidis A., Automated Road Extraction from High Resolution Multispectral Imagery. Photogrammetric Engineering & Remote Sensing, 2004. 70(12):1405-1416.
[178] Gao J., Wu L., Automatic Extraction of Road Networks in Urban Areas from Ikonos Imagery Based on Spatial Reasoning. Proceedings of ISPRS XXth Congress. 2004. July 12-23.
[179]Tarku K., Bretschneider T. R., Leedham C. G, Unsupervised Detection of Roads in High Resolution Panchromatic Satellite Images. Proceedings of the International Workshop Advanced Image Technology (IWAIT-2004). 15-19.
[180]Hu X., Tao C. V., Hu Y., Automatic Road Extraction from Dense Urban Area by Integrated Processing of High Resolution Imagery and Lidar Data. Proceedings of ISPRS XXth Congress.2004. 12-23.
[181] Zhang Y. Wang R., Multi-resolution and Multi-spectral Image Fusion for Urban Object Extraction.Proceedings of ISPRS XXth Congress. 2004.12-23.
[182] Song M., Civco D., Road Extraction Using SVM and Image Segmentation. Photogrammetric Engineering & Remote Sensing. 2004. 70(12):1365-1371.
[183] Zhang Q. P., Couloigner I., Automated Road Network Extraction from High Resolution Multi-spectral Imagery. ASPRS 2006 Annual Conference. 2006.
[184] Zhang Q., Couloigner I., Quality Assessment of Automated Road Extraction Based on Line Segment Matching. Proceedings of International Conference on Computational Science and its Applications (ICCSA2006). Glasgow, Scotland. 2006.