基于航空影像的真正射影像制作关键技术研究
|
摘要
数字正射影像(DOM)作为一种重要的基础地理信息产品，兼备丰富的纹理信息和地图的几何属性，其制作技术在过去几十年中得到了极大的发展。一方面、DOM制作的主要目的在于消除地形起伏和相机倾斜等引起的投影变形，正射纠正过程中利用的是定向后的影像和数字高程模型(DEM)，并未考虑如人工建筑物等空间目标，从而导致这些目标的中心投影变形依然存在。另一方面、随着数码相机成像技术的不断提高，目前已经能够获取小于0.1米分辨率的航空影像，例如建筑物、桥梁等人工地物目标在影像上清晰可见。正射纠正时如果不对这些目标加以考虑，势必会导致DOM中出现建筑物倾斜、遮挡等问题，如果将GIS矢量地图叠加到DOM上，会表现出道路矢量线横穿建筑物以及建筑物矢量轮廓无法与其自身套合等问题，严重时将会导致DOM失去地理参考价值，不足以作为基础底图使用。
真正射影像(True Orthophoto)即是在上述背景下提出来的一种更高级的正射产品。真正射影像中如建筑物、桥梁等人工目标能够纠正到其正确的位置上，不会对其他地物目标(如道路)造成遮挡，真正意义上兼备了丰富的纹理信息和地形图的几何属性，受到了国内外摄影测量学者的广泛关注。
真正射影像与DOM最显著的差异在于正射纠正的同时分析地物的可见性。可见区域的纹理获取与制作DOM相同，即灰度内插和赋值；遮蔽区域的纹理利用相邻影像中的可见部分予以修补。因此，制作真正射影像中最为关键的技术环节为可见性分析，也可以称为遮蔽区域检测，各种真正射影像制作方法的本质差异就在于遮蔽检测方法的不同。从国内外研究成果上看，遮蔽检测主要利用数字表面模型(DSM)和数字建筑物模型(DBM)。鉴于上述分析，本文的研究目的即为如何通过影像匹配的方式自动获取DSM，并利用DSM或DBM检测遮蔽区域，结合相邻影像的可见部分纹理修补遮蔽区域用以制作真正射影像，主要研究内容包括以下方面：
顾及面特征匹配的数字表面模型获取算法。面特征相比较点、边缘等特征，具有区域闭合的特点，将面特征匹配引入到多影像多基元的匹配算法中，一方面、面特征的匹配可以为面特征边界点及其他点(特征点、格网点、边缘关键点等)的匹配提供初值，面特征边界点自身的匹配结果具有很高的可靠性，可作为点、线混合概率松弛法全局一致性匹配的“锚点”使用；另一方面、多种匹配基元的相互融合，可以获取数目巨大的匹配特征，有助于获取密集、精确及可靠的数字表面模型；
基于DSM的遮蔽区域检测。改进一种现有的基于DSM的遮蔽区域检测算法，本文称为基于高程约束的遮蔽检测算法，该方法以摄影中心对应的地底点为起始点同时假定地底点附近4个DSM格网点可见，采用螺旋扫描的方式自内向外依次检测DSM格网点的可见性。
基于DBM的遮蔽区域检测。提出一种基于三角网的遮蔽检测算法，该方法以单个三角形为检测单元，通过反演中心投影成像时的视觉状态，对三角形进行视觉排序，依次将三角形反投影到像方，同时合并所有像方投影区域构建投影多边形，根据三角形像方投影区域与投影多边形关联情况判断视觉状态。
真正射影像制作。采用“权值影像“的方式结合相邻影像的可见部分纹理修补遮蔽区域。“权值影像”是指对可用于修补主影像遮蔽区域的所有副影像计算一个质量评价标准，即权值，选择最大权值对应的副影像作为修补源影像。
Digital orthophoto(DOM) as an important basic geographic information products thathas both richly textures and geometric properties of the map, and its technology of productivehas been greatly evolved over the past few decades. The main purpose of DOM generationaims at eliminating projection distortion of terrain and camera tilt. We know thatorthorectified using for both orientation images and Digital Elevation Model(DEM), notinclude spatial object such as buildings that still exist the problem of projection distortion. Onthe other hand, with the development of digital camera technology, it has been able toacquired aerial images that resolution is less than0.1m, it means that the spatial object such asbuildings and bridges is very clearly in the images. The problem of both buildings tilt andocclusion will be yet existed if we are not considering these objects when orthorectifiedprocessing. As a result of there will be shown two mainly contradictions if the vector maps ofGIS are superimposed on to the DOM, firstly the buildings are acrossed by vector lines ofroads, and secondly vector outlines of the buildings cannot be fitted to itself. It will be loss thevalue of geo-referenced and cannot be used as a basic map if the problems of abovementioned are more seriously.
True Orthophoto belongs to more advanced ortho products, and it is proposed in theabove context. The buildings and bridges are rectified to correct position in true orthophoto,and it will not be occluded other objects(such as roads). Both richly textures and geometricproperties of the map are really possessed by true orthophoto, so it has been widely concernedby lots of scholars of photogrammetry.
The most significant difference between true orthophoto and DOM is visibility analysiswhen orthorectified processing. The textures of visible region are provided by grayinterpolation and assignment that is equivalent to tradition method of rectification. Thetextures of occluded region are repaired by visible parts of neighbour images. The visibilityanalysis is the most significant technology in the true orthophoto generating, it is also calledocclusion area detection. Now, the methods of visibility analysis that mainly making use ofDigital Surface Model and Digital Building Model are different according to various methodsof true orthophoto generation. As a result of this paper mainly aims at automatically obtainingDSM through image matching, and detecting occlusion areas through DSM or DBM, andgenerating true orthophoto by means of textures repairing.The main research contents areintroduced as follows:
The matching algorithm of DSM is taken into account feature planars that have regional closure compared to feature points and edges, which is called Multiple Primitive Multi-ImageMatching. The matching of the feature planars can provide approximate value using formatching others points include feature points, grid points, key points of edges and boundarypoints of feature planars. The matching results of the boundary points of feature planars havevery high reliability, there can be served as ‘anchor’ using for point-line hybird globalconsistency matching with probability relaxation technique. On the other hand, a hugenumber of matching features through merging multiple matching primitives have ability toprovide dense, precise, and reliable results.
An existence method of occlusion detection based on DSM is improved, and it has beenknown as Occlusion Detection based on Elevation Constraint. This method assumes fourDSM cells that are surrounding the nadir points are visible, and it detects the visibility of theDSM cells starting from the nadir point in a spiral mode from inside to outside.
The occluded regions can be detected with the DBM, so-called Occlusion Detectionbased on TIN. The triangle is the detection unit in this method. Firstly, visual sorting oftriangles are primarily processed through inversing visual status of central projection.Secondly, it successively projects the triangles of sorted to image space, and projectionpolygons of image space can be constructed through merging all projection areas of triangles.The relationship between projection areas of triangles and projection polygons can be used fordetecting visual status.
True orthophoto generation combines master image and slave images, and occludedregions are repaired through ‘Weight Image’, it means a quality measures in a so-calledweight, which is computed for each individual available slave images. Texture repair issimply picking the slave image with the highest weight.
真正射影像(True Orthophoto)即是在上述背景下提出来的一种更高级的正射产品。真正射影像中如建筑物、桥梁等人工目标能够纠正到其正确的位置上，不会对其他地物目标(如道路)造成遮挡，真正意义上兼备了丰富的纹理信息和地形图的几何属性，受到了国内外摄影测量学者的广泛关注。
真正射影像与DOM最显著的差异在于正射纠正的同时分析地物的可见性。可见区域的纹理获取与制作DOM相同，即灰度内插和赋值；遮蔽区域的纹理利用相邻影像中的可见部分予以修补。因此，制作真正射影像中最为关键的技术环节为可见性分析，也可以称为遮蔽区域检测，各种真正射影像制作方法的本质差异就在于遮蔽检测方法的不同。从国内外研究成果上看，遮蔽检测主要利用数字表面模型(DSM)和数字建筑物模型(DBM)。鉴于上述分析，本文的研究目的即为如何通过影像匹配的方式自动获取DSM，并利用DSM或DBM检测遮蔽区域，结合相邻影像的可见部分纹理修补遮蔽区域用以制作真正射影像，主要研究内容包括以下方面：
顾及面特征匹配的数字表面模型获取算法。面特征相比较点、边缘等特征，具有区域闭合的特点，将面特征匹配引入到多影像多基元的匹配算法中，一方面、面特征的匹配可以为面特征边界点及其他点(特征点、格网点、边缘关键点等)的匹配提供初值，面特征边界点自身的匹配结果具有很高的可靠性，可作为点、线混合概率松弛法全局一致性匹配的“锚点”使用；另一方面、多种匹配基元的相互融合，可以获取数目巨大的匹配特征，有助于获取密集、精确及可靠的数字表面模型；
基于DSM的遮蔽区域检测。改进一种现有的基于DSM的遮蔽区域检测算法，本文称为基于高程约束的遮蔽检测算法，该方法以摄影中心对应的地底点为起始点同时假定地底点附近4个DSM格网点可见，采用螺旋扫描的方式自内向外依次检测DSM格网点的可见性。
基于DBM的遮蔽区域检测。提出一种基于三角网的遮蔽检测算法，该方法以单个三角形为检测单元，通过反演中心投影成像时的视觉状态，对三角形进行视觉排序，依次将三角形反投影到像方，同时合并所有像方投影区域构建投影多边形，根据三角形像方投影区域与投影多边形关联情况判断视觉状态。
真正射影像制作。采用“权值影像“的方式结合相邻影像的可见部分纹理修补遮蔽区域。“权值影像”是指对可用于修补主影像遮蔽区域的所有副影像计算一个质量评价标准，即权值，选择最大权值对应的副影像作为修补源影像。
Digital orthophoto(DOM) as an important basic geographic information products thathas both richly textures and geometric properties of the map, and its technology of productivehas been greatly evolved over the past few decades. The main purpose of DOM generationaims at eliminating projection distortion of terrain and camera tilt. We know thatorthorectified using for both orientation images and Digital Elevation Model(DEM), notinclude spatial object such as buildings that still exist the problem of projection distortion. Onthe other hand, with the development of digital camera technology, it has been able toacquired aerial images that resolution is less than0.1m, it means that the spatial object such asbuildings and bridges is very clearly in the images. The problem of both buildings tilt andocclusion will be yet existed if we are not considering these objects when orthorectifiedprocessing. As a result of there will be shown two mainly contradictions if the vector maps ofGIS are superimposed on to the DOM, firstly the buildings are acrossed by vector lines ofroads, and secondly vector outlines of the buildings cannot be fitted to itself. It will be loss thevalue of geo-referenced and cannot be used as a basic map if the problems of abovementioned are more seriously.
True Orthophoto belongs to more advanced ortho products, and it is proposed in theabove context. The buildings and bridges are rectified to correct position in true orthophoto,and it will not be occluded other objects(such as roads). Both richly textures and geometricproperties of the map are really possessed by true orthophoto, so it has been widely concernedby lots of scholars of photogrammetry.
The most significant difference between true orthophoto and DOM is visibility analysiswhen orthorectified processing. The textures of visible region are provided by grayinterpolation and assignment that is equivalent to tradition method of rectification. Thetextures of occluded region are repaired by visible parts of neighbour images. The visibilityanalysis is the most significant technology in the true orthophoto generating, it is also calledocclusion area detection. Now, the methods of visibility analysis that mainly making use ofDigital Surface Model and Digital Building Model are different according to various methodsof true orthophoto generation. As a result of this paper mainly aims at automatically obtainingDSM through image matching, and detecting occlusion areas through DSM or DBM, andgenerating true orthophoto by means of textures repairing.The main research contents areintroduced as follows:
The matching algorithm of DSM is taken into account feature planars that have regional closure compared to feature points and edges, which is called Multiple Primitive Multi-ImageMatching. The matching of the feature planars can provide approximate value using formatching others points include feature points, grid points, key points of edges and boundarypoints of feature planars. The matching results of the boundary points of feature planars havevery high reliability, there can be served as ‘anchor’ using for point-line hybird globalconsistency matching with probability relaxation technique. On the other hand, a hugenumber of matching features through merging multiple matching primitives have ability toprovide dense, precise, and reliable results.
An existence method of occlusion detection based on DSM is improved, and it has beenknown as Occlusion Detection based on Elevation Constraint. This method assumes fourDSM cells that are surrounding the nadir points are visible, and it detects the visibility of theDSM cells starting from the nadir point in a spiral mode from inside to outside.
The occluded regions can be detected with the DBM, so-called Occlusion Detectionbased on TIN. The triangle is the detection unit in this method. Firstly, visual sorting oftriangles are primarily processed through inversing visual status of central projection.Secondly, it successively projects the triangles of sorted to image space, and projectionpolygons of image space can be constructed through merging all projection areas of triangles.The relationship between projection areas of triangles and projection polygons can be used fordetecting visual status.
True orthophoto generation combines master image and slave images, and occludedregions are repaired through ‘Weight Image’, it means a quality measures in a so-calledweight, which is computed for each individual available slave images. Texture repair issimply picking the slave image with the highest weight.
引文
[1] Axelsson, P. DEM generation from laser scanner data using adaptive TIN models[J], InternationalArchives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIII,1999, B4(1):110-117.
[2] Ayman F. Habib, Eui-Myoung Kim, Chang-Jae Kim. New methodologies for true orthophotogeneration[J]. Photogrammetric Engineering and Remote Sensing,2007,73(1):25-36.
[3] Babak, A. Automatic Recognition and3D Reconstruction of Buildings[D]. University of Stuggart,2000.
[4] Baillard. C, Dissard. O. A Stereo Matching Algorithm for Urban Digital Elevation Models[J]. PE&RS,2000,66(9):1119-1128.
[5] Baillard. C, Schmid. C, Zisserman. A. Automatic Line Matching and3D Reconstruction of Buildingsfrom Multiple Views[J]. IAPRS,1999,32(3):69-80.
[6] Baltsavias E. A Comparison between Photogrammetry and Laser Scanning[J]. ISPRS Journal ofPhotogrammetry and Remote Sensing,1999,54(1):83-94.
[7] Baltsavias E. Multiphoto Geometrically Constrained Matching[M]. Zuich:Mitteilungen,1991.
[8] Baltsavias E, Zhang L, Eisenbeiss H. DSM Generation and Interior Orientation Determination ofIKONOS Images Using a Testfield in Switzerland[J]. Journal of Photogrammetrie, Fernerkundung,Geoinformation,2006,(1):41-54.
[9] Baltsavias E, Stallmann, D. Advancement in Matching of SPOT Images by Integration of SensorGeometry and Treatment of Radiometric Differences[J]. IAPRS,1992,29(B4):916-924.
[10] Bentley, J. L. Multidimensional binary search trees used for associative searching. Communicationsof the ACM18(9):509-517.
[11] Bertalmio M, Vese L, Sapiro G. Simultaneous Structure and Texture Image Inpainting[J]. IEEETransactions on Image Processing,2003,12(8):882-889.
[12] Brockalband D.C, Tam A.P. Stereo Elevation Determination Techniques for SPOT Imagery[J]. PE&RS,1991,57(8):1065-1073.
[13] Canny. J. A Computational Approach to Edge Detection[J]. IEEE Transactions on Pattern Analysis andMachine Intelligence,1986,8(6):679-698.
[14] C. Beasy. Lidar and Photogrammetry, How Do I Choose?, Eagle Mapping.http://www.eaglemapping.com/publications/Lidar%20-%20Photo%20Article.pdf,2007.
[15] Chris Harris,Mike Stephens. A COMBINED CORNER AND EDGE DETECTOR[C]. Proceedings ofthe4th Alvey Vision Conference,1988,147-151.
[16] Chunsun Zhang, Emmanuel Baltsavias. Knowledge-based image analysis for3D edge extraction androad reconstruction[J], ISPRS Congress,2000,33(3):1008-1115.
[17] David. G. Lowe. Object recognition from local scale-invariant features[C]. In International Conferenceon Computer Vision,1999,1150-1157.
[18] David. G. Lowe. Distinctive image features from scale invariant keypoints[J]. International Journal ofComputer Vision,2004,60(2):91-110.
[19] D. Skarlatos. Orthophotography Production in Urban Areas[J]. Photogrammetric Record,1999,16(94):643-650.
[20] Fahmi Amhar, Josef Jansa. The generation of true orthophotos using a3D building model inconjunction with a conventional DTM[J]. International Archives of Photogrammetry and Remote Sensing,32(1998):16-22.
[21] Fan Dazhao, LeiRong, Ji Song. Least Square Matching Model AMMGC-LSM for Multi-Line-ArrayDigital Images[J], ISPRS Congress,2008,37(1):795-798.
[22] F.Leberl, A.Irschara, T.Pock. Point Clouds: Lidar versus3D Vision[J]. Photogrammetric Engineering&Remote Sensing,2010,76(10):1123-1134.
[23] Gabet L, Giraudon G, Renouard L. Automatic Generation of High Resolution Urban Zone DigitalElevation Models[J]. International Journal of Photogrammetry and Remote Sensing,1997,52(1):33-47.
[24] George E. Karras, Lazaros Grammatikopoulos, Llias Kaliperakis, Elli Petsa. Generation ofOrthoimages and Perspective Views with Automatic Visibility Checking and Texture Blending[J].Photogrammetric Engineering&Remote Sensing,2007,73(4):403-411.
[25] Gouet V, Montesinos P, Deriche R. Evaluation de Detecteurs Depoints Dint’Eret Pour la Couleur[J]. InReconnaissance des formes et Intelligence Artificielle,2000,10(1):257-266.
[26] Grimson. Object Recognition by Computer:The Role of Gepmetric Constraints[M].London:MIT Press,1990.
[27] Gruen A, Baltsavias E. High Precision Image Matching for Digital Terrain Model Generation[J].ISPRS Commision III Symposium,19-22, August,1986, Vol.26, Part3/1,284-296.
[28] Gruen A, Baltsavias E. Adaptive Least Squares Correlation with Geometrical Constraints[J]. TechnicalConference Computer Vision for Robots,2-6, December,1985, Vol.595,72-82.
[29] Gruen A, Zhang L. Automatic DTM Generation from Three-Line-Scanner(TLS) Images[J]. IAPRS,2002,34(3):131-137.
[30] Guoqing Zhou, Weirong Chen, John A.Kelmelis, Deyan Zhang. A Comprehensive Study on UrbanTrue Orthorectification[J]. IEEE Transactions on Geoscience and Remote Sensing,2005,43(9):2138-2147.
[31] Hans P. Morevec. TOWARDS AUTOMATIC VISUAL OBSTACLE AVOIDANCE[C]. Proceedings ofthe5th International Joint Conference on Artificial Intelligence,1977,584-591.
[32] H Bay, A Ess, T Tuytelaars. SURF:Speeded-up robust features[J]. Computer Vision and ImageUnderstanding,2008,110:346-359.
[33] Herbet Bay, Tinne Tuytelaars, Luc Van Gool. Surf:Speeded up robust features[J]. In Proceedings of theninth European Conference on Computer Vision,2006.
[34] Heipke C. A Global Approach for Least Squares Image Matching and Surface Construction in ObjectSpace. PERS,58(3):317-323.
[35] Hsia J-S, Newton I. A Method for the Automated Production of Digital Terrain Models Using aCombination of Feature Points, Grid Points, and Filling Back Points[J]. PE&RS,1999,5(6):713-719.
[36] Jiann-Yeou Rau, Nai-Yu Chen, Liang-Chien Chen. True Orthophoto Generation of Built-Up AreasUsing Multi-View Images[J]. Photogrammetric Engineering&Remote Sensing,2002,68(6):581-588.
[37] J.Matas, O.Chun, M.Urban, T.Pajdla. Robust Wide Baseline Stereo from Maximally Stable ExtremalRegions[J]. Image and Vision Computing,2004,22(10):761-767.
[38] Johannes Bauer, Niko Sunderhauf, Peter Protzel. Comparing Several Implementations of TwoRecently Published Feature Detectors[J]. In Proceedings of the International Conference on Intelligent andAutonomous Systems,2007.
[39] Ki-In Bang, Ayman F. Habib. Comprehensive Analysis of Alternative Methodologies for TrueOrthophoto Generation from High Resolution Satellite and Aerial Imagery[C]. ASPRS2007AnnualConference, Tampa, Florida,2007.
[40] Kim, Z. Multi-View3D Object Description with Uncertain Reasoning and Machine Leaning[D].USA:University of Southern California,2001.
[41] K. Jacobsen. DEM generation from satellite data[J].23rd Sumposium of the European Association ofRemote Sensing Laboratories,2003:513-525.
[42] Kuzmin. Polygon-based true orthophoto generation[J]. XXth ISPRS Congress,2004,529-531.
[43] Lotti J-L, Giraudon G. Adaptive Window Algorithm for Aerial Image Stereo[J]. IAPRS,1994,30(Part3/1):517-254.
[44] Lu Y, Kubik K, Bennamoun M. Image Segmentation and Image Matching for3D TerrainReconstruction[J].14thInternational Conference on Pattern Recognition,1998,(2):1535-1538.
[45] Maillet G, Paparoditis N, Taillandier F. TIN Surface Reconstruction from Multiple Calibrated AerialImages in Urban Areas[J]. IEEE International Conference on Image Processing,2002,(3):521-524.
[46] Markus Niederost. Detection and Reconstruction of Buildings for Automatic Map Updating[D]. ETHZurich:Institute of Echnology Zurich,2003.
[47] Marr.D, Poffio.T. A computational theory of human stereo vision[J]. Proceedings of the Royal Societyof London,1979, B204:301-328.
[48] Michael Donoser, Horst Bischof. Efficient Maximally Stable Extremal Region (MSER) Tracking[J].IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2006.
[49] Okutomi M, Kanade T. A Multiple-baseline Stereo[J]. IEEE Transactions on Pattern Analysis andMachine Intelligence,1993,15(4):353-363.
[50] Pateraki M, Baltsavias E, Recke U. Experiences on Automatic Image Matching for DSM Generationwith ADS40Pushbroom Sensor Data[J]. IAPRS,2004,35(B2):83-92.
[51] Pateraki M, Baltsavias E. Analysis of a DSM Generation Algorithm for the ADS40AirbornePushbroom Sensor[J]. Optical3-D Measurement Techniques VI,83-91.
[52] Paul M. Dare. Shadow Analysis in High-Resolution Satellite Imagery of Urban Areas[J].Photogrammetric Engineering&Remote Sensing,2005,71(2):169-177.
[53] Per-Erik Forssen, David G. Lowe. Shape Descriptors for Maximally Stable Extremal Regions[J]. IEEE11th International Conference on Computer Vision,2007.
[54] Rau, J. Y. Hidden Compensation and Shadow Enhancement for True-Orthophoto Generation[J]. InProceedings of Asian Conference on Remote Sensing,2000.
[55] Saint Marc. P, Madioni. G. Adaptive Smoothing: A General Tool for Early Vision[J]. PAMI,1991,13(6):514-529.
[56] Schlueter M. Multi-image Matching in Object Space on the Basis of a General3-D Surface ModelInstead of Common2.5-D Surface Models and Its Application for Urban Scenes[J]. IAPRS,1998,32(4):545-552.
[57] Schmid C, Zisserman A. Automatic line matching across views[J]. Proc. CVPR,666-671.Shao J, Mohr R, Fraser C. Multi-Image Matching using Segment Features[J]. IAPRS,2000,33(B3):837-844.
[58] Shao XiaoWei, Liu Zhengkai, Li Houqiang. An Image Inpainting Approach based on the PoissonEquation[C]. The Second International Conference on Document Image Analysis for Libraries, Lyon,France,2006.
[59] Sheng, Y. Minimising Algorithm-Induced Artefacts in True OrthoImage Generation: A Direct MethodImplemented in the Vector Domain[J]. The Photogrammetric Record,2007,22(118):151-163.
[60] Sithole, Vosselman. Experimental comparison of filtering algorithms for bare-earth extraction fromairborne laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing59(1-2):85-101.
[61] Stephen M. Smith,J. Michael Brady. SUSAN-a new approach to low level image processing[J].International Journal of Computer Vision,1997,23(1):45-78.
[62] Stepan Obdrzalek, Jiri Matas. Object Recognition Using Local Affine Frames on Maximally StableExtremal Regions[J]. Toward Category-Level Object Recognition,2006.
[63] Thomas H.Cormen, Charles E.Leiserson, Ronald L.Rivest, Clifford Stein. Introdution to Algorithms,Third Edition[M]. London:MIT Press,2009.
[64] Toutin, Th. DEM from Stereo Landsat7ETM+Data over High Relief Areas[J]. International Jourmalof Remote Sensing,2002,42(10):2121-2129.
[65] Trinder J.C, Vuillemin A, Donnelly B. A Study of Procedures and Tests on DEM Software for SPOTImages[J]. IAPRS,1994,30(Part4):449-456.
[66] Volker, G.. Multidimensional Access Methods. ACM Computing Surveys (CSUR)130(2):170-231.
[67] Vosselman, Sithole. Experimental comparison of filtering algorithms for bare-earth extraction fromairborne laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing,2004,59(1-2):85-101.
[68] Wiman H. Area Based Segmentation and Matching of Aerial Images for Geometric Description ofBuildings[D]. Sweden:Royal Institute of Technology,1997.
[69] Wolfgang F rstner. A Framework for Low Level Feature Extraction[C]. European Conference onComputer Vision,1994,383-394.
[70] Wrobel E. Least-Squares Methods for Surface Reconstruction from Images[J]. IJPRS,1991,(46):67-84.
[71] W Schichler, Anthony Thorpe. Operational procedure for automatic true orthophoto generation[J].International Archives of Photogrammetry andRemote Sensing,32(1998):527-532.
[72] W Schichler, Anthony Thorpe. Surface estimation based on LIDAR[J]. Proceedings of the2001-ASPRS Annual Convention,2001.
[73] Xiong X, Chen Y, Li T. A Remote Sensing Image Subpixel Matching Algorithm Combined Edge withGrey[J]. IEEE International Conference on Intelligent Processing Systems, ICIPS,1997,(2):1450-1454.
[74] Yahya Alshawabkeh. A New True Ortho-Photo Methodology for Complex ArchaeologicalApplication[J]. University of Oxford,2009, Archaeometry52(3):517-530.
[75] Ya-Fan Su, Homer H. Chen. A-Three-Stage Approach to Shadow Field Estimation From PartialBoundary Information[J]. IEEE Transactions on Image Processing,2010,19(10):2749-2760.
[76] Y. Li, P. Gong, T. Sasagawa. Integrated shadow removal based on photogrammetry and imageanalysis[J]. International Journal of Remote Sensing,2005,26(18):3911-3929.
[77] Yongwei Sheng, Peng Gong, Gregory S. Biging. True Orthoimage Production for Forested Areas fromLarge-Scale Aerial Photographs[J]. Photogrammetric Engineering&Remote Sensing,2003,69(3):259-266.
[78] Yongwei Sheng. Minimising Algorithm-Induced Artefacts in True Ortho-Image Generation: A DirectMethod Inplemented in the Vector Domain[J]. The Photogrammetric Record,2007,22(118):151-163.
[79] Y.Wang. An Operational System for Sensor Modeling and DEM Generation of Satellite PushbroomSensor Images[J], ISPRS Congress,2008,37(1):745-750.
[80] Zhihao Qin, Wenjuan Li, Manchun Li. A Methodology for True Orthorectification of Large-ScaleUrban Aerial Images and Automatic Detection of Building Occlusions Using Digital Surface Model[J].IEEE International Geosciences and Remote Sensing Symposium,2003,729-731.
[81] Zhang B, Miller M. Adaptive Automatic Terrain Extraction[J]. SPIE,1997, Vol.3072,27-36.
[82] Zhang Li. Automatic Digital Surface Model (DSM) Generation from Linear Array Images.Switzerland:Swiss Federal Institute of Technology Zurich.PhD thesis,2005.
[83] Zhang L, Gruen A. Multi-image matching for DSM generation from IKONOS imagery[J]. ISPRSJournal of Photogrammetry and Remote Sensing,2006,60:195-211.
[84] Zhang Zuxun, Zhang Jianqing, Mingshen Liao, Zhang Li. Automatic Registration of multi-sourceImagery based on Global Image Matching[J]. PE&RS,2000,66(5):625-629.
[85]边馥苓,王潇.真正射影像生成中遮蔽区域的补偿[J].测绘科学,2009,34(3):81-83.
[86]陈亚婷,严泰来.朱德海.基于辛普森面积的多边形凹凸性识别算法[J].地理与地理信息科学,2010,26(6):28-31.
[87]陈鹰,李铁军,熊兴华.带约束条件的特征与最小二乘影像匹配[J].中国图象图形学报,1998,3(4):299-303.
[88]陈宇,白征东.基于多极值网格搜索法的快速影像匹配技术研究[J].测绘通报,2010,(4):28-30.
[89]杜全叶.无地面控制的航空影像与LiDAR数据自动高精度配准[D].武汉:武汉大学,2010.
[90]管海燕. LiDAR与影像结合的地物分类及房屋重建研究[D].武汉:武汉大学,2009.
[91]胡翔云.航空遥感影像线状地物与房屋的自动提取[D].武汉:武汉大学,2001.
[92]黄先锋.利用机载LIDAR数据重建3D建筑物模型的关键技术研究[D].武汉:武汉大学,2006.
[93]黄元元.基于视觉特征的图像检索技术研究[D].南京:南京理工大学,2003.
[94]江万寿.航空影像多视匹配与规则建筑物自动提取方法研究[D].武汉:武汉大学,2004.
[95]柯涛.旋转多基线数字近景摄影测量[D].武汉:武汉大学,2008.
[96]林怡,陈鹰.用立体影像匹配和数学形态变换自动生成DEM[J].中国图象图形学报,2003,8(4):447-452.
[97]赖旭东.机载激光雷达数据处理中若干关键技术的研究[D].武汉:武汉大学,2006.
[98]李治江.彩色影像色调重建的理论与实践[D].武汉:武汉大学,2005.
[99]李德仁,袁修孝.误差处理与可靠性理论[M].武汉:武汉大学出版社,2002.
[100]刘春明,方漪.寻找三维散乱数据点拓扑结构的一种算法[J],青岛大学学报(工程技术版),2003,18(3):20-24.
[101]刘勇奎,高云,黄有群.一个有效的多边形裁剪算法[J].软件学报,2003,14(4):845-856.
[102]吕震.反求工程CAD建模中的特征技术研究[D].杭州:浙江大学,2003.
[103] M.de Berg.计算几何－算法与应用(第二版)[M].北京:清华大学出版社,2005.
[104]潘慧波,胡友健.从LiDAR数据中获取DSM生成真正射影像[J].测绘工程,2009,18(3):47-50.
[105]潘俊.自动化的航空影像色彩一致性处理及接缝线网络生成方法研究[D].武汉:武汉大学,2008.
[106]潘俊,王密.基于扫描线填充的快速镶堪算法[J].测绘信息与工程,2006,31(5):8-9.
[107]齐民友,刘禄勤,龚小庆,王文祥.概率论与数理统计[M].北京:高等教育出版社,2002.
[108]史照良,沈泉飞,曹敏.像素工厂中真正射影像的生产及其精度分析[J].测绘科学技术学报,2007,24(5):332-335.
[109]沈晶.基于机载LiDAR数据的DEM综合生成和建筑物提取[D].武汉:武汉大学,2009.
[110]孙杰,马洪超,汤璇.机载LiDAR正射影像镶嵌线智能优化研究[J].武汉大学学报信息科学版,2011,36(3):325-328.
[111]孙杰,马洪超,钟良.利用LiDAR点云的真正射影像遮蔽检测[J].武汉大学学报信息科学版,2011,36(8):948-951.
[112]汤美华.空载光达点云及地形图辅助生产真实正射影像之研究[D].台湾:成功大学,2006.
[113]王树根，王军利，王爱萍.基于整体变分模型的影像阴影检测算法研究[J].武汉大学学报信息科学版,2006,31(8):663-666.
[114]王潇,江万寿,谢俊峰.一种新的真正射影像生成算法[J].武汉大学学报信息科学版,2009,34(10):1250-1254.
[115]王晓南.基于灭点的真正射影像阴影自动剔除方法[J].测绘通报,2011,4:17-19.
[116]吴晓良.影像匹配的松弛途径[D].武汉:武汉测绘科技大学,1993.
[117]谢文寒,周国清.城市大比例尺真正射影像阴影与遮挡问题的研究[J].测绘学报,2010，39(1):52-58.
[118]严蔚敏,吴伟民.数据结构[M].北京:清华大学出版社,1997.
[119]袁修孝,明洋.一种综合利用像方和物方信息的多影像匹配方法[J].测绘学报,2009,38(3):216-222.
[120]熊兴华,陈鹰,钱曾波.一种快速、高精度和稳健的影像匹配算法[J].测绘学报,2005,34(1):40-45.
[121]章毓敏.图象处理和分析[M].北京:清华大学出版社,1999.
[122]朱庆,吴波,赵杰.基于自适应三角形约束的可靠影像匹配方法[J].计算机学报,2005,28(10):1734-1739.
[123]左志权,张祖勋,张剑清,曹辉. DSM辅助下城区大比例尺正射影像镶嵌线智能检测[J].测绘学报,2011,40(1):84-89.
[124]钟成,黄先锋,李德仁,李卉.真正射影像生成的多边形性反演成像遮蔽检测方法[J].测绘学报,2010,39(1):59-64.
[125]郑南宁.计算机视觉与模式识别[M].北京:国防工业出版色,1998.
[126]中国测绘科学研究院. http://www.casm.ac.cn,2011.
[127]张祖勋,张剑清.数字摄影测量学[M].武汉:武汉大学出版社,1997.
[128]张剑清,潘励,王树根.摄影测量学[M].武汉:武汉大学出版社,2003.
[129]张力,张继贤.基于多基线影像匹配的高分辨率遥感影像DEM自动生成[J].测绘科学,2008,33Suppl:35-39.
[130]张力,张祖勋,张剑清. Wallis滤波在影像匹配中的应用[J].武汉测绘科技大学学报,1999,24(1)24-27.
[131]张勇.铅垂线辅助空中三角测量的应用研究[D].武汉:武汉大学,2006.
[132]张宏伟.矢量与遥感影像的自动匹配[D].武汉:武汉大学,2004.
[133]张鹏林,关泽群,王新洲.时间序列影像特征点提取与匹配算法研究[J],武汉大学学报(信息科学版),2004,29(4):329-332.
[134]张栋.基于LiDAR数据和航空影像的城市房屋三维重建[D].武汉:武汉大学,2005.
[135]张志友.基于LiDAR数据和航空影像的城市房屋三维重建[D].北京:北京交通大学,2008.
[136]朱小强.基于LiDAR点云和航空影像的城市三维重建[D].合肥:合肥工业大学,2009.
[2] Ayman F. Habib, Eui-Myoung Kim, Chang-Jae Kim. New methodologies for true orthophotogeneration[J]. Photogrammetric Engineering and Remote Sensing,2007,73(1):25-36.
[3] Babak, A. Automatic Recognition and3D Reconstruction of Buildings[D]. University of Stuggart,2000.
[4] Baillard. C, Dissard. O. A Stereo Matching Algorithm for Urban Digital Elevation Models[J]. PE&RS,2000,66(9):1119-1128.
[5] Baillard. C, Schmid. C, Zisserman. A. Automatic Line Matching and3D Reconstruction of Buildingsfrom Multiple Views[J]. IAPRS,1999,32(3):69-80.
[6] Baltsavias E. A Comparison between Photogrammetry and Laser Scanning[J]. ISPRS Journal ofPhotogrammetry and Remote Sensing,1999,54(1):83-94.
[7] Baltsavias E. Multiphoto Geometrically Constrained Matching[M]. Zuich:Mitteilungen,1991.
[8] Baltsavias E, Zhang L, Eisenbeiss H. DSM Generation and Interior Orientation Determination ofIKONOS Images Using a Testfield in Switzerland[J]. Journal of Photogrammetrie, Fernerkundung,Geoinformation,2006,(1):41-54.
[9] Baltsavias E, Stallmann, D. Advancement in Matching of SPOT Images by Integration of SensorGeometry and Treatment of Radiometric Differences[J]. IAPRS,1992,29(B4):916-924.
[10] Bentley, J. L. Multidimensional binary search trees used for associative searching. Communicationsof the ACM18(9):509-517.
[11] Bertalmio M, Vese L, Sapiro G. Simultaneous Structure and Texture Image Inpainting[J]. IEEETransactions on Image Processing,2003,12(8):882-889.
[12] Brockalband D.C, Tam A.P. Stereo Elevation Determination Techniques for SPOT Imagery[J]. PE&RS,1991,57(8):1065-1073.
[13] Canny. J. A Computational Approach to Edge Detection[J]. IEEE Transactions on Pattern Analysis andMachine Intelligence,1986,8(6):679-698.
[14] C. Beasy. Lidar and Photogrammetry, How Do I Choose?, Eagle Mapping.http://www.eaglemapping.com/publications/Lidar%20-%20Photo%20Article.pdf,2007.
[15] Chris Harris,Mike Stephens. A COMBINED CORNER AND EDGE DETECTOR[C]. Proceedings ofthe4th Alvey Vision Conference,1988,147-151.
[16] Chunsun Zhang, Emmanuel Baltsavias. Knowledge-based image analysis for3D edge extraction androad reconstruction[J], ISPRS Congress,2000,33(3):1008-1115.
[17] David. G. Lowe. Object recognition from local scale-invariant features[C]. In International Conferenceon Computer Vision,1999,1150-1157.
[18] David. G. Lowe. Distinctive image features from scale invariant keypoints[J]. International Journal ofComputer Vision,2004,60(2):91-110.
[19] D. Skarlatos. Orthophotography Production in Urban Areas[J]. Photogrammetric Record,1999,16(94):643-650.
[20] Fahmi Amhar, Josef Jansa. The generation of true orthophotos using a3D building model inconjunction with a conventional DTM[J]. International Archives of Photogrammetry and Remote Sensing,32(1998):16-22.
[21] Fan Dazhao, LeiRong, Ji Song. Least Square Matching Model AMMGC-LSM for Multi-Line-ArrayDigital Images[J], ISPRS Congress,2008,37(1):795-798.
[22] F.Leberl, A.Irschara, T.Pock. Point Clouds: Lidar versus3D Vision[J]. Photogrammetric Engineering&Remote Sensing,2010,76(10):1123-1134.
[23] Gabet L, Giraudon G, Renouard L. Automatic Generation of High Resolution Urban Zone DigitalElevation Models[J]. International Journal of Photogrammetry and Remote Sensing,1997,52(1):33-47.
[24] George E. Karras, Lazaros Grammatikopoulos, Llias Kaliperakis, Elli Petsa. Generation ofOrthoimages and Perspective Views with Automatic Visibility Checking and Texture Blending[J].Photogrammetric Engineering&Remote Sensing,2007,73(4):403-411.
[25] Gouet V, Montesinos P, Deriche R. Evaluation de Detecteurs Depoints Dint’Eret Pour la Couleur[J]. InReconnaissance des formes et Intelligence Artificielle,2000,10(1):257-266.
[26] Grimson. Object Recognition by Computer:The Role of Gepmetric Constraints[M].London:MIT Press,1990.
[27] Gruen A, Baltsavias E. High Precision Image Matching for Digital Terrain Model Generation[J].ISPRS Commision III Symposium,19-22, August,1986, Vol.26, Part3/1,284-296.
[28] Gruen A, Baltsavias E. Adaptive Least Squares Correlation with Geometrical Constraints[J]. TechnicalConference Computer Vision for Robots,2-6, December,1985, Vol.595,72-82.
[29] Gruen A, Zhang L. Automatic DTM Generation from Three-Line-Scanner(TLS) Images[J]. IAPRS,2002,34(3):131-137.
[30] Guoqing Zhou, Weirong Chen, John A.Kelmelis, Deyan Zhang. A Comprehensive Study on UrbanTrue Orthorectification[J]. IEEE Transactions on Geoscience and Remote Sensing,2005,43(9):2138-2147.
[31] Hans P. Morevec. TOWARDS AUTOMATIC VISUAL OBSTACLE AVOIDANCE[C]. Proceedings ofthe5th International Joint Conference on Artificial Intelligence,1977,584-591.
[32] H Bay, A Ess, T Tuytelaars. SURF:Speeded-up robust features[J]. Computer Vision and ImageUnderstanding,2008,110:346-359.
[33] Herbet Bay, Tinne Tuytelaars, Luc Van Gool. Surf:Speeded up robust features[J]. In Proceedings of theninth European Conference on Computer Vision,2006.
[34] Heipke C. A Global Approach for Least Squares Image Matching and Surface Construction in ObjectSpace. PERS,58(3):317-323.
[35] Hsia J-S, Newton I. A Method for the Automated Production of Digital Terrain Models Using aCombination of Feature Points, Grid Points, and Filling Back Points[J]. PE&RS,1999,5(6):713-719.
[36] Jiann-Yeou Rau, Nai-Yu Chen, Liang-Chien Chen. True Orthophoto Generation of Built-Up AreasUsing Multi-View Images[J]. Photogrammetric Engineering&Remote Sensing,2002,68(6):581-588.
[37] J.Matas, O.Chun, M.Urban, T.Pajdla. Robust Wide Baseline Stereo from Maximally Stable ExtremalRegions[J]. Image and Vision Computing,2004,22(10):761-767.
[38] Johannes Bauer, Niko Sunderhauf, Peter Protzel. Comparing Several Implementations of TwoRecently Published Feature Detectors[J]. In Proceedings of the International Conference on Intelligent andAutonomous Systems,2007.
[39] Ki-In Bang, Ayman F. Habib. Comprehensive Analysis of Alternative Methodologies for TrueOrthophoto Generation from High Resolution Satellite and Aerial Imagery[C]. ASPRS2007AnnualConference, Tampa, Florida,2007.
[40] Kim, Z. Multi-View3D Object Description with Uncertain Reasoning and Machine Leaning[D].USA:University of Southern California,2001.
[41] K. Jacobsen. DEM generation from satellite data[J].23rd Sumposium of the European Association ofRemote Sensing Laboratories,2003:513-525.
[42] Kuzmin. Polygon-based true orthophoto generation[J]. XXth ISPRS Congress,2004,529-531.
[43] Lotti J-L, Giraudon G. Adaptive Window Algorithm for Aerial Image Stereo[J]. IAPRS,1994,30(Part3/1):517-254.
[44] Lu Y, Kubik K, Bennamoun M. Image Segmentation and Image Matching for3D TerrainReconstruction[J].14thInternational Conference on Pattern Recognition,1998,(2):1535-1538.
[45] Maillet G, Paparoditis N, Taillandier F. TIN Surface Reconstruction from Multiple Calibrated AerialImages in Urban Areas[J]. IEEE International Conference on Image Processing,2002,(3):521-524.
[46] Markus Niederost. Detection and Reconstruction of Buildings for Automatic Map Updating[D]. ETHZurich:Institute of Echnology Zurich,2003.
[47] Marr.D, Poffio.T. A computational theory of human stereo vision[J]. Proceedings of the Royal Societyof London,1979, B204:301-328.
[48] Michael Donoser, Horst Bischof. Efficient Maximally Stable Extremal Region (MSER) Tracking[J].IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2006.
[49] Okutomi M, Kanade T. A Multiple-baseline Stereo[J]. IEEE Transactions on Pattern Analysis andMachine Intelligence,1993,15(4):353-363.
[50] Pateraki M, Baltsavias E, Recke U. Experiences on Automatic Image Matching for DSM Generationwith ADS40Pushbroom Sensor Data[J]. IAPRS,2004,35(B2):83-92.
[51] Pateraki M, Baltsavias E. Analysis of a DSM Generation Algorithm for the ADS40AirbornePushbroom Sensor[J]. Optical3-D Measurement Techniques VI,83-91.
[52] Paul M. Dare. Shadow Analysis in High-Resolution Satellite Imagery of Urban Areas[J].Photogrammetric Engineering&Remote Sensing,2005,71(2):169-177.
[53] Per-Erik Forssen, David G. Lowe. Shape Descriptors for Maximally Stable Extremal Regions[J]. IEEE11th International Conference on Computer Vision,2007.
[54] Rau, J. Y. Hidden Compensation and Shadow Enhancement for True-Orthophoto Generation[J]. InProceedings of Asian Conference on Remote Sensing,2000.
[55] Saint Marc. P, Madioni. G. Adaptive Smoothing: A General Tool for Early Vision[J]. PAMI,1991,13(6):514-529.
[56] Schlueter M. Multi-image Matching in Object Space on the Basis of a General3-D Surface ModelInstead of Common2.5-D Surface Models and Its Application for Urban Scenes[J]. IAPRS,1998,32(4):545-552.
[57] Schmid C, Zisserman A. Automatic line matching across views[J]. Proc. CVPR,666-671.Shao J, Mohr R, Fraser C. Multi-Image Matching using Segment Features[J]. IAPRS,2000,33(B3):837-844.
[58] Shao XiaoWei, Liu Zhengkai, Li Houqiang. An Image Inpainting Approach based on the PoissonEquation[C]. The Second International Conference on Document Image Analysis for Libraries, Lyon,France,2006.
[59] Sheng, Y. Minimising Algorithm-Induced Artefacts in True OrthoImage Generation: A Direct MethodImplemented in the Vector Domain[J]. The Photogrammetric Record,2007,22(118):151-163.
[60] Sithole, Vosselman. Experimental comparison of filtering algorithms for bare-earth extraction fromairborne laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing59(1-2):85-101.
[61] Stephen M. Smith,J. Michael Brady. SUSAN-a new approach to low level image processing[J].International Journal of Computer Vision,1997,23(1):45-78.
[62] Stepan Obdrzalek, Jiri Matas. Object Recognition Using Local Affine Frames on Maximally StableExtremal Regions[J]. Toward Category-Level Object Recognition,2006.
[63] Thomas H.Cormen, Charles E.Leiserson, Ronald L.Rivest, Clifford Stein. Introdution to Algorithms,Third Edition[M]. London:MIT Press,2009.
[64] Toutin, Th. DEM from Stereo Landsat7ETM+Data over High Relief Areas[J]. International Jourmalof Remote Sensing,2002,42(10):2121-2129.
[65] Trinder J.C, Vuillemin A, Donnelly B. A Study of Procedures and Tests on DEM Software for SPOTImages[J]. IAPRS,1994,30(Part4):449-456.
[66] Volker, G.. Multidimensional Access Methods. ACM Computing Surveys (CSUR)130(2):170-231.
[67] Vosselman, Sithole. Experimental comparison of filtering algorithms for bare-earth extraction fromairborne laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing,2004,59(1-2):85-101.
[68] Wiman H. Area Based Segmentation and Matching of Aerial Images for Geometric Description ofBuildings[D]. Sweden:Royal Institute of Technology,1997.
[69] Wolfgang F rstner. A Framework for Low Level Feature Extraction[C]. European Conference onComputer Vision,1994,383-394.
[70] Wrobel E. Least-Squares Methods for Surface Reconstruction from Images[J]. IJPRS,1991,(46):67-84.
[71] W Schichler, Anthony Thorpe. Operational procedure for automatic true orthophoto generation[J].International Archives of Photogrammetry andRemote Sensing,32(1998):527-532.
[72] W Schichler, Anthony Thorpe. Surface estimation based on LIDAR[J]. Proceedings of the2001-ASPRS Annual Convention,2001.
[73] Xiong X, Chen Y, Li T. A Remote Sensing Image Subpixel Matching Algorithm Combined Edge withGrey[J]. IEEE International Conference on Intelligent Processing Systems, ICIPS,1997,(2):1450-1454.
[74] Yahya Alshawabkeh. A New True Ortho-Photo Methodology for Complex ArchaeologicalApplication[J]. University of Oxford,2009, Archaeometry52(3):517-530.
[75] Ya-Fan Su, Homer H. Chen. A-Three-Stage Approach to Shadow Field Estimation From PartialBoundary Information[J]. IEEE Transactions on Image Processing,2010,19(10):2749-2760.
[76] Y. Li, P. Gong, T. Sasagawa. Integrated shadow removal based on photogrammetry and imageanalysis[J]. International Journal of Remote Sensing,2005,26(18):3911-3929.
[77] Yongwei Sheng, Peng Gong, Gregory S. Biging. True Orthoimage Production for Forested Areas fromLarge-Scale Aerial Photographs[J]. Photogrammetric Engineering&Remote Sensing,2003,69(3):259-266.
[78] Yongwei Sheng. Minimising Algorithm-Induced Artefacts in True Ortho-Image Generation: A DirectMethod Inplemented in the Vector Domain[J]. The Photogrammetric Record,2007,22(118):151-163.
[79] Y.Wang. An Operational System for Sensor Modeling and DEM Generation of Satellite PushbroomSensor Images[J], ISPRS Congress,2008,37(1):745-750.
[80] Zhihao Qin, Wenjuan Li, Manchun Li. A Methodology for True Orthorectification of Large-ScaleUrban Aerial Images and Automatic Detection of Building Occlusions Using Digital Surface Model[J].IEEE International Geosciences and Remote Sensing Symposium,2003,729-731.
[81] Zhang B, Miller M. Adaptive Automatic Terrain Extraction[J]. SPIE,1997, Vol.3072,27-36.
[82] Zhang Li. Automatic Digital Surface Model (DSM) Generation from Linear Array Images.Switzerland:Swiss Federal Institute of Technology Zurich.PhD thesis,2005.
[83] Zhang L, Gruen A. Multi-image matching for DSM generation from IKONOS imagery[J]. ISPRSJournal of Photogrammetry and Remote Sensing,2006,60:195-211.
[84] Zhang Zuxun, Zhang Jianqing, Mingshen Liao, Zhang Li. Automatic Registration of multi-sourceImagery based on Global Image Matching[J]. PE&RS,2000,66(5):625-629.
[85]边馥苓,王潇.真正射影像生成中遮蔽区域的补偿[J].测绘科学,2009,34(3):81-83.
[86]陈亚婷,严泰来.朱德海.基于辛普森面积的多边形凹凸性识别算法[J].地理与地理信息科学,2010,26(6):28-31.
[87]陈鹰,李铁军,熊兴华.带约束条件的特征与最小二乘影像匹配[J].中国图象图形学报,1998,3(4):299-303.
[88]陈宇,白征东.基于多极值网格搜索法的快速影像匹配技术研究[J].测绘通报,2010,(4):28-30.
[89]杜全叶.无地面控制的航空影像与LiDAR数据自动高精度配准[D].武汉:武汉大学,2010.
[90]管海燕. LiDAR与影像结合的地物分类及房屋重建研究[D].武汉:武汉大学,2009.
[91]胡翔云.航空遥感影像线状地物与房屋的自动提取[D].武汉:武汉大学,2001.
[92]黄先锋.利用机载LIDAR数据重建3D建筑物模型的关键技术研究[D].武汉:武汉大学,2006.
[93]黄元元.基于视觉特征的图像检索技术研究[D].南京:南京理工大学,2003.
[94]江万寿.航空影像多视匹配与规则建筑物自动提取方法研究[D].武汉:武汉大学,2004.
[95]柯涛.旋转多基线数字近景摄影测量[D].武汉:武汉大学,2008.
[96]林怡,陈鹰.用立体影像匹配和数学形态变换自动生成DEM[J].中国图象图形学报,2003,8(4):447-452.
[97]赖旭东.机载激光雷达数据处理中若干关键技术的研究[D].武汉:武汉大学,2006.
[98]李治江.彩色影像色调重建的理论与实践[D].武汉:武汉大学,2005.
[99]李德仁,袁修孝.误差处理与可靠性理论[M].武汉:武汉大学出版社,2002.
[100]刘春明,方漪.寻找三维散乱数据点拓扑结构的一种算法[J],青岛大学学报(工程技术版),2003,18(3):20-24.
[101]刘勇奎,高云,黄有群.一个有效的多边形裁剪算法[J].软件学报,2003,14(4):845-856.
[102]吕震.反求工程CAD建模中的特征技术研究[D].杭州:浙江大学,2003.
[103] M.de Berg.计算几何－算法与应用(第二版)[M].北京:清华大学出版社,2005.
[104]潘慧波,胡友健.从LiDAR数据中获取DSM生成真正射影像[J].测绘工程,2009,18(3):47-50.
[105]潘俊.自动化的航空影像色彩一致性处理及接缝线网络生成方法研究[D].武汉:武汉大学,2008.
[106]潘俊,王密.基于扫描线填充的快速镶堪算法[J].测绘信息与工程,2006,31(5):8-9.
[107]齐民友,刘禄勤,龚小庆,王文祥.概率论与数理统计[M].北京:高等教育出版社,2002.
[108]史照良,沈泉飞,曹敏.像素工厂中真正射影像的生产及其精度分析[J].测绘科学技术学报,2007,24(5):332-335.
[109]沈晶.基于机载LiDAR数据的DEM综合生成和建筑物提取[D].武汉:武汉大学,2009.
[110]孙杰,马洪超,汤璇.机载LiDAR正射影像镶嵌线智能优化研究[J].武汉大学学报信息科学版,2011,36(3):325-328.
[111]孙杰,马洪超,钟良.利用LiDAR点云的真正射影像遮蔽检测[J].武汉大学学报信息科学版,2011,36(8):948-951.
[112]汤美华.空载光达点云及地形图辅助生产真实正射影像之研究[D].台湾:成功大学,2006.
[113]王树根，王军利，王爱萍.基于整体变分模型的影像阴影检测算法研究[J].武汉大学学报信息科学版,2006,31(8):663-666.
[114]王潇,江万寿,谢俊峰.一种新的真正射影像生成算法[J].武汉大学学报信息科学版,2009,34(10):1250-1254.
[115]王晓南.基于灭点的真正射影像阴影自动剔除方法[J].测绘通报,2011,4:17-19.
[116]吴晓良.影像匹配的松弛途径[D].武汉:武汉测绘科技大学,1993.
[117]谢文寒,周国清.城市大比例尺真正射影像阴影与遮挡问题的研究[J].测绘学报,2010，39(1):52-58.
[118]严蔚敏,吴伟民.数据结构[M].北京:清华大学出版社,1997.
[119]袁修孝,明洋.一种综合利用像方和物方信息的多影像匹配方法[J].测绘学报,2009,38(3):216-222.
[120]熊兴华,陈鹰,钱曾波.一种快速、高精度和稳健的影像匹配算法[J].测绘学报,2005,34(1):40-45.
[121]章毓敏.图象处理和分析[M].北京:清华大学出版社,1999.
[122]朱庆,吴波,赵杰.基于自适应三角形约束的可靠影像匹配方法[J].计算机学报,2005,28(10):1734-1739.
[123]左志权,张祖勋,张剑清,曹辉. DSM辅助下城区大比例尺正射影像镶嵌线智能检测[J].测绘学报,2011,40(1):84-89.
[124]钟成,黄先锋,李德仁,李卉.真正射影像生成的多边形性反演成像遮蔽检测方法[J].测绘学报,2010,39(1):59-64.
[125]郑南宁.计算机视觉与模式识别[M].北京:国防工业出版色,1998.
[126]中国测绘科学研究院. http://www.casm.ac.cn,2011.
[127]张祖勋,张剑清.数字摄影测量学[M].武汉:武汉大学出版社,1997.
[128]张剑清,潘励,王树根.摄影测量学[M].武汉:武汉大学出版社,2003.
[129]张力,张继贤.基于多基线影像匹配的高分辨率遥感影像DEM自动生成[J].测绘科学,2008,33Suppl:35-39.
[130]张力,张祖勋,张剑清. Wallis滤波在影像匹配中的应用[J].武汉测绘科技大学学报,1999,24(1)24-27.
[131]张勇.铅垂线辅助空中三角测量的应用研究[D].武汉:武汉大学,2006.
[132]张宏伟.矢量与遥感影像的自动匹配[D].武汉:武汉大学,2004.
[133]张鹏林,关泽群,王新洲.时间序列影像特征点提取与匹配算法研究[J],武汉大学学报(信息科学版),2004,29(4):329-332.
[134]张栋.基于LiDAR数据和航空影像的城市房屋三维重建[D].武汉:武汉大学,2005.
[135]张志友.基于LiDAR数据和航空影像的城市房屋三维重建[D].北京:北京交通大学,2008.
[136]朱小强.基于LiDAR点云和航空影像的城市三维重建[D].合肥:合肥工业大学,2009.