无控制DEM匹配算法性能比较与改进研究
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摘要
数字高程模型(Digital Elevation Model, DEM)匹配是指将多源、多时相和多尺度DEM数据统一到同一坐标系的过程。无控制DEM匹配是在无地面控制点支持的情况下,由计算机自动提取DEM地形特征或几何信息来实现DEM匹配。与传统的控制点DEM匹配相比,无控制DEM匹配技术具备安全、高效、省时、省力、费用低、适用范围广等诸多优点,在测绘、遥感、导航、地理信息系统等多个领域的DEM绝对定向、DEM拼接及多源和多时相DEM数据融合中有广泛和潜在的应用前景,因而吸引着国内外众多的专家学者的关注,是目前测绘科学技术领域中的热点研究方向之-
本文在现有无控制DEM匹配,即迭代最近点(Iterative Closest Point, ICP)、最小高差(Least Z-Difference, LZD)和最小二乘3D表面匹配(Least Squares3D surface matcing,LS3D)等最小二乘表面匹配(Least Squares Surface Matching, LSSM)算法的基础上通过仿真试验比较这3类LSSM算法的性能,寻找提高匹配精度、效率和拉入范围的途径；改进k-d树和盒子结构(Boxing Structure, BS)等最近点查找算法,提出基于格网划分(Grid Partition, GP)的最邻近点查找算法,提高非规则DEM无控制匹配算法的收敛效率；提出基于“匹配度之和最大(Maximizing the Sum of Matching-Degree, MSMD)"的新模型,用于遗传算法(Genetic Algorithm, GA)求解(MGA) DEM匹配参数,解决LSSM算法拉入范围小,基于传统模型和GA的DEM匹配算法(TGA)在求解带有尺度参数的7参数转换模型时容易陷于错误全局最优解的弱点；提出融合MGA(?)(?) LSSM的无控制DEM匹配(MGA+LSSM)新算法,提高MGA的收敛效率和匹配精度,探索多尺度无控制DEM匹配的全局最优算法。
通过模拟不同地形的规则和非规则DEM数据进行仿真试验,结果显示：
(1)3种“点-点"ICP的7参数模型算法,其拉入范围、匹配精度和整体收敛趋势基本一致。它们在处理基准DEM完全包含全部的待匹配DEM时,均比仅包含部分的待匹配DEM时的拉入范围大、匹配精度高。地形越简单,拉入范围越大。地形连续且地形特征越明显,匹配精度越高。DEM数据密度越大,匹配精度越高,但收敛效率越低,拉入范围也会降低；
(2)“点-面"ICP、LZD(?)(?)LS3D算法在处理非规则DEM数据时,均比处理规则DEM数据的匹配精度高。其中,对非规则DEM数据,LS3D算法的匹配精度和收敛效率最优,‘点-面"ICP算法的拉入范围最大。对规则DEM数据,LZD算法的拉入范围、匹配精度和收敛效率最优；
(3)GP最邻近点查找算法,比穷搜索、k-d树和BS等最近点查找算法的查找速度快,且不影响LSSM算法的匹配精度；
(4)MGA匹配算法,避免了TGA算法极易陷入错误的全局最优极值的弱点。MGA+LSSM匹配算法综合了MGA拉入范围大和(?)LSSM匹配精度高、收敛速度快的优点,因而比LSSM算法有更高的稳健性和拉入范围。
编程实现的各种无控制DEM匹配算法,为仿真试验和数据分析奠定了必要的技术基础,通过归纳总结各种无控制DEM匹配算法的性能特点,为寻找适应多尺度DEM的无控制匹配算法进行了有益的探索,研究结果对多源、多时相和多尺度DEM匹配和数据融合有参考价值。
Digital Elevation Model (DEM) matching is a procedure of transforming multi-source, multi-temporal and multi-scale DEM data into a unified coordinate system. DEM matching without Ground Control Point (GCP) means that computers can automatically extract topographic features or geometric information from DEM to implement DEM matching without support of ground control points. Compared with the traditional methods with GCPs, DEM matching without GCP has the merits of safety, efficiency, time-saving, labour-saving, low-cost, and wide-application etc. It has broad and potential applications in surveying and mapping, remote sensing, navigation, geographic information system and other fields involved in DEM absolute orientation, DEM alignment and multi-source or multi-temporal DEM data fusion. Therefore, DEM matching without GCP has attracted much attention from a lot of domestic and international scholars, and is also one of the hot research topics in the field of surveying and mapping.
The algorithms of DEM matching without GCP, i.e. the Least Squares Surface Matching (LSSM) algorithms including Iterative Closest Point (ICP), Least Z-Difference (LZD) and Least Squares3D surface matching (LS3D), are comparatively studied by some emulated experiments in this dissertation, to approach rather algrithms in the matching accuracy, the convergence efficiency and the pull-in-range. The nearest neighbor searching method based on Grid Partition (GP), through improving the nearest neighbor searching methods of k-d tree and Boxing Structure (BS), is proposed to improve the matching efficiency of algorithms of DEM matching without GCP. Generally, the pull-in-range of LSSM algorithms was small and it was easy to drop into a false global optimal using the traditional DEM matching model and GA (TGA) to solve the7-parameter transformation model with the scaling factor. In order to overcome these shortcomings, a new model based on "Maximizing the Sum of Matching-Degree (MSMD)" was proposed and used GA (MGA) to calculate the parameters of DEM matching model. Another new method of DEM matching combined GA with least squares (MGA+LSSM) was proposed to improve the matching efficiency and the matching accuracy of MGA and to explore the global optimization algorithms of multi-scaling DEM matching without GCP.
The simulated tests based on regularly and irregularly distributed DEM data imitating different types of topography were carried out. The experiment results have shown:
(1) The three "point-to-point" ICP algorithms of7-parameter model were basically the same in pull-in-range, matching accuracy and overall trend of convergence. When dealing with the case that the range of the DEM to be matched was completely included in the referenced DEM, the three algorithms could get larger pull-in-range and higher matching accuracy than the case that the range of the DEM to be matched was partly included in the referenced DEM. The simper the topography was, the larger the pull-in-range was. The more continuous the topography was and the more apparent the topographic features were, the higher the matching accuracy was. The larger the point density was, the higher the matching accuracy was and the lower the convergence efficiency was and the smaller the pull-in-range was.
(2) When dealing with the irregularly distributed DEM data, the algorithms of the "point-to-plane" ICP, the LZD and the LS3D show higher matching accuracy than that of dealing with the regularly distributed DEM data. Among them, for irregularly distributed DEM data, the LS3D algorithm was the most excellent one in matching accuracy and convergence efficiency, and the "point-to-plane" ICP algorithm had the largest pull-in-range. For regularly distributed DEM data, LZD algorithm was the most excellent one in pull-in-range, matching accuracy and convergence efficiency.
(3) The nearest neighbor searching method of GP was faster than the other methods such as exhaustive search, k-d tree and BS, and no influence on the matching accuracy of LSSM algorithms.
(4) The MGA matching algorithm can avoid the defection that the TGA algorithm is easy to drop into a false global optimal. The MGA had the merit of large pull-in-range, and the LSSM had the merits of high matching accuracy and fast convergence speed. The matching algorithm of MGA+LSSM integrated the merits of both MGA and LSSM, thus, becomes more robust and has larger pull-in-range than LSSM algorithms.
A variety of algorithms of DEM matching without GCP were implemented on computers and it can provide the necessary technical foundation of simulated tests and data analysis. The summary of the performance characteristics among various DEM matching algorithms without GCP suggests a valuable exploration to find the more adaptive multi-scaling DEM matching without GCP. The research results would have helpful for multi-source, multi-temporal and multi-scale DEM matching and data fusion.
本文在现有无控制DEM匹配,即迭代最近点(Iterative Closest Point, ICP)、最小高差(Least Z-Difference, LZD)和最小二乘3D表面匹配(Least Squares3D surface matcing,LS3D)等最小二乘表面匹配(Least Squares Surface Matching, LSSM)算法的基础上通过仿真试验比较这3类LSSM算法的性能,寻找提高匹配精度、效率和拉入范围的途径；改进k-d树和盒子结构(Boxing Structure, BS)等最近点查找算法,提出基于格网划分(Grid Partition, GP)的最邻近点查找算法,提高非规则DEM无控制匹配算法的收敛效率；提出基于“匹配度之和最大(Maximizing the Sum of Matching-Degree, MSMD)"的新模型,用于遗传算法(Genetic Algorithm, GA)求解(MGA) DEM匹配参数,解决LSSM算法拉入范围小,基于传统模型和GA的DEM匹配算法(TGA)在求解带有尺度参数的7参数转换模型时容易陷于错误全局最优解的弱点；提出融合MGA(?)(?) LSSM的无控制DEM匹配(MGA+LSSM)新算法,提高MGA的收敛效率和匹配精度,探索多尺度无控制DEM匹配的全局最优算法。
通过模拟不同地形的规则和非规则DEM数据进行仿真试验,结果显示：
(1)3种“点-点"ICP的7参数模型算法,其拉入范围、匹配精度和整体收敛趋势基本一致。它们在处理基准DEM完全包含全部的待匹配DEM时,均比仅包含部分的待匹配DEM时的拉入范围大、匹配精度高。地形越简单,拉入范围越大。地形连续且地形特征越明显,匹配精度越高。DEM数据密度越大,匹配精度越高,但收敛效率越低,拉入范围也会降低；
(2)“点-面"ICP、LZD(?)(?)LS3D算法在处理非规则DEM数据时,均比处理规则DEM数据的匹配精度高。其中,对非规则DEM数据,LS3D算法的匹配精度和收敛效率最优,‘点-面"ICP算法的拉入范围最大。对规则DEM数据,LZD算法的拉入范围、匹配精度和收敛效率最优；
(3)GP最邻近点查找算法,比穷搜索、k-d树和BS等最近点查找算法的查找速度快,且不影响LSSM算法的匹配精度；
(4)MGA匹配算法,避免了TGA算法极易陷入错误的全局最优极值的弱点。MGA+LSSM匹配算法综合了MGA拉入范围大和(?)LSSM匹配精度高、收敛速度快的优点,因而比LSSM算法有更高的稳健性和拉入范围。
编程实现的各种无控制DEM匹配算法,为仿真试验和数据分析奠定了必要的技术基础,通过归纳总结各种无控制DEM匹配算法的性能特点,为寻找适应多尺度DEM的无控制匹配算法进行了有益的探索,研究结果对多源、多时相和多尺度DEM匹配和数据融合有参考价值。
Digital Elevation Model (DEM) matching is a procedure of transforming multi-source, multi-temporal and multi-scale DEM data into a unified coordinate system. DEM matching without Ground Control Point (GCP) means that computers can automatically extract topographic features or geometric information from DEM to implement DEM matching without support of ground control points. Compared with the traditional methods with GCPs, DEM matching without GCP has the merits of safety, efficiency, time-saving, labour-saving, low-cost, and wide-application etc. It has broad and potential applications in surveying and mapping, remote sensing, navigation, geographic information system and other fields involved in DEM absolute orientation, DEM alignment and multi-source or multi-temporal DEM data fusion. Therefore, DEM matching without GCP has attracted much attention from a lot of domestic and international scholars, and is also one of the hot research topics in the field of surveying and mapping.
The algorithms of DEM matching without GCP, i.e. the Least Squares Surface Matching (LSSM) algorithms including Iterative Closest Point (ICP), Least Z-Difference (LZD) and Least Squares3D surface matching (LS3D), are comparatively studied by some emulated experiments in this dissertation, to approach rather algrithms in the matching accuracy, the convergence efficiency and the pull-in-range. The nearest neighbor searching method based on Grid Partition (GP), through improving the nearest neighbor searching methods of k-d tree and Boxing Structure (BS), is proposed to improve the matching efficiency of algorithms of DEM matching without GCP. Generally, the pull-in-range of LSSM algorithms was small and it was easy to drop into a false global optimal using the traditional DEM matching model and GA (TGA) to solve the7-parameter transformation model with the scaling factor. In order to overcome these shortcomings, a new model based on "Maximizing the Sum of Matching-Degree (MSMD)" was proposed and used GA (MGA) to calculate the parameters of DEM matching model. Another new method of DEM matching combined GA with least squares (MGA+LSSM) was proposed to improve the matching efficiency and the matching accuracy of MGA and to explore the global optimization algorithms of multi-scaling DEM matching without GCP.
The simulated tests based on regularly and irregularly distributed DEM data imitating different types of topography were carried out. The experiment results have shown:
(1) The three "point-to-point" ICP algorithms of7-parameter model were basically the same in pull-in-range, matching accuracy and overall trend of convergence. When dealing with the case that the range of the DEM to be matched was completely included in the referenced DEM, the three algorithms could get larger pull-in-range and higher matching accuracy than the case that the range of the DEM to be matched was partly included in the referenced DEM. The simper the topography was, the larger the pull-in-range was. The more continuous the topography was and the more apparent the topographic features were, the higher the matching accuracy was. The larger the point density was, the higher the matching accuracy was and the lower the convergence efficiency was and the smaller the pull-in-range was.
(2) When dealing with the irregularly distributed DEM data, the algorithms of the "point-to-plane" ICP, the LZD and the LS3D show higher matching accuracy than that of dealing with the regularly distributed DEM data. Among them, for irregularly distributed DEM data, the LS3D algorithm was the most excellent one in matching accuracy and convergence efficiency, and the "point-to-plane" ICP algorithm had the largest pull-in-range. For regularly distributed DEM data, LZD algorithm was the most excellent one in pull-in-range, matching accuracy and convergence efficiency.
(3) The nearest neighbor searching method of GP was faster than the other methods such as exhaustive search, k-d tree and BS, and no influence on the matching accuracy of LSSM algorithms.
(4) The MGA matching algorithm can avoid the defection that the TGA algorithm is easy to drop into a false global optimal. The MGA had the merit of large pull-in-range, and the LSSM had the merits of high matching accuracy and fast convergence speed. The matching algorithm of MGA+LSSM integrated the merits of both MGA and LSSM, thus, becomes more robust and has larger pull-in-range than LSSM algorithms.
A variety of algorithms of DEM matching without GCP were implemented on computers and it can provide the necessary technical foundation of simulated tests and data analysis. The summary of the performance characteristics among various DEM matching algorithms without GCP suggests a valuable exploration to find the more adaptive multi-scaling DEM matching without GCP. The research results would have helpful for multi-source, multi-temporal and multi-scale DEM matching and data fusion.
引文
[1]李志林,朱庆.数字高程模型[M].武汉大学出版社,2003.
[2]汤国安,刘学军,闾国年.数字高程模型及地学分析的原理与方法[M].科学出版社,2005.
[3]Naser EL-Sheimy, Caterina Valeo,Ayman Habib. Digital Terrain Modeling: Acquisition,Manipulation, and Applications[M]. Artech House, INC. 2005.
[4]Berthold K P Horn. Closed-form solution of absolute orientation using unit quaternions[J]. Journal of the Optical Society of America.1987,4(4): 629-642.
[5]D Rosenholm.K Torlegard. Three-Dimensional Absolute Orientation of Stereo Models Using Digital Elevation Models[J]. Photogrammetric Engineering and Remote Sensing.1988,54(10):1385-1389.
[6]E H Thompson. A Method for the Construction of Orthogonal Matrices[J]. The Photogrammetric Record.1959,3(13):55-59.
[7]G H Schut. On Exact Linear Equations for the Computation of the Rotational Elements of Absolute Orientation [J]. Photogrammetria.1960, 17(1):34-37.
[8]H L Oswal,S Balasubramanian. An Exact Solution of Absolute Orientation[J]. Photogrammetric Engineering.1968,34:1079-1083.
[9]K S Arun, T S Huang,S D Blostein. Least-Squares Fitting of Two 3-D Point Sets[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.1987, Pami-9(5):698-700.
[10]Buckley Simon John, J P Mills,Mitchell Harvey Lawson. Improving the Accuracy of Photogrammetric Absolute Orientation Using Surface Matching[A]. XXth ISPRS Congress[C].Istanbul,Turkey,2004.1-6.
[11]Heinrich Ebner,Franz Mueller. Processing of Digital Three-line Imagery Using a Generalized Model for Combined Point Determination[J]. Photogrammetria.1987,41(3):173-182.
[12]Heinrich Ebner,Giinter Strunz. Combined Point Determination using Digital Terrain Models as Control Information[J]. International Archives of Photogrammetry and Remote Sensing.1988,27(B11/3):578-587.
[13]Dmitry B Goldgof, Thomas S Huang,Hua Lee. Feature Extraction and Terrain Matching[A]. IEEE Conference on Computer Vision and Pattern Recognition[C].Ann Arbor,1988.899-904.
[14]张同刚.无控制DEM匹配与差异探测及其在泥石流灾害地区的应用[D].西南交通大学,工学博士.2006.
[15]Heinrich Ebner,Timm Ohlhof. Utilization of Ground Control Points for Image Orientation without Point Identification in Image Space[J]. ISPRS Commission III Symposium:Spatial Information from Digital Photogrammetry and Computer Vision.1994,2357:206-211.
[16]Jen Jer Jaw. Control Surface in Aerial Triangulation[J]. International Archives of Photogrammetry and Remote Sensing.2000,44(B3): 444-451.
[17]Timothy D James, Tavi Murray, Nicholas E Barrand,Stuart L Barr. Extracting Photogrammetric Ground Control From Lidar Dems for Change Detection[J]. The Photogrammetric Record.2006,21(116): 312-328.
[18]Jose A Goncalves,Andre R S Marcal. Automatic Ortho-rectification of ASTER Images by Matching Digital Elevation Models[A]. The 4th International Conference on Image Analysis and Recognition[C].2007. Heidelberg:1265-1275.
[19]Pauline Miller, Jon Mills, Stuart Edwards, Paul Bryan, et al. A Robust Surface Matching Technique for Coastal Geohazard Assessment and Management[J]. ISPRS Journal of Photogrammetry and Remote Sensing. 2008,63(2):529-542.
[20]Zhengxiao Li,James Bethel. DEM Registration,Alignment and Evaluation for SAR Interferometry[A]. T.I.S.f.P.a.R. Sensing, the XXI International Congress for Photogrammetry and Remote Sensing[C].Beijing,2008. Orgnising Committee of the XXI International Congress for Photogrammetry and Remote Sensing:111-115.
[21]Massimo Fabris,Arianna Pesci. Automated DEM Extraction in Digital Aerial Photogrammetry:Precisions and Validation for Mass Movement Monitoring[J]. Annals of Geophysics.2005,48(6):973-988.
[22]Lloyd Pilgrim. Robust Estimation Applied to Surface Matching[J]. ISPRS Journal of Photogrammetry & Remote Sensing.1996,2000(B3): 1000-1007.
[23]Zhilin Li, Zhu Xu, Minyi Cen,Xiaoli Ding. Robust Surface Matching for Automated Detection of Local Deformations Using Least-Median-of-Squares Estimator[J]. Photogrammetric Engineering & Remote Sensing. 2001,67(11):1283-1292.
[24]Zhu Xu,Zhilin Li. Least Median of Squares Matching for Automated Detection of Surface Deformations[J]. nternational Archives of Photogrammetry and Remote Sensing.2000,33(B3):1000-1007.
[25]Arianna Pesci, Paolo Baldi, Alessandro Bedin, Giuseppe Casula, et al. Digital Elevation Models for Landslide Evolution Monitoring: Application on Two Areas Located in the Reno River Valley (Italy)[J]. Annals of Geophysics.2004,47(4):1339-1353.
[26]G Bitelli, M Dubbini,A Zanutta. Terrestrial Laser Scanning and Digital Photogrammetry Techniques to Monitoring Landslide Bodies[J]. International Archivies of Photogrammetry,Remote Sensing and Spational Information Sciences.2004,35(Part B5):246-251.
[27]张同刚,岑敏仪,吴兴华.无控制DEM表面差异探测研究[J].测绘科学.2006,31(3)：36-39.
[28]张同刚,岑敏仪,秦军,吴兴华.多时相DEM匹配探测泥石流地表变形的新方法[J].水土保持通报.2006,26(3)：96-100.
[29]张同刚,岑敏仪,吴兴华.基于差分模型的无控制DEM差异探测方法[J].西南交通大学学报.2006,41(1)：91-96.
[30]Tonggang Zhang,Minyi Cen. Robust DEM Co-registration Method for Terrain Changes Assessment Using Least Trimmed Squares Estimator[J]. Advances in Space Research.2008,41(11):1827-1835.
[31]Giordano Teza, Antonio Galgaro, Rinaldo Genevois,N.Zaltron. Terrestrial Laser Scanner to Detect Landslide Displacement Fields:a New Approach[J]. International Journal for Remote Sensing.2007,28(15-16): 3425-3446.
[32]Giordano Teza, Arianna Pesci, Rinaldo Genevois,Antonio Galgaro. Characterization of landslide ground surface kinematics from terrestrial laser scanning and strain field computation[J]. Geomorphology.2008, 97(3-4):424-437.
[33]O Monserrat,M Crosetto. Deformation Measurement Using Terrestrial Laser Scanning Data and Least Squares 3D Surface Matching[J]. ISPRS Journal of Photogrammetry and Remote Sensing.2008,63(1):142-154.
[34]P Baldi, S Bonvalot, P Briole,M Marsella. Digital Photogrammetry and Kinematic GPS Applied to the Monitoring of Vulcano Island,Aeolian Arc,Italy[J]. International Journal of Geophysics.2000,142(3):801-811.
[35]J P Mills, S J Buckley, H L Mitchell, P J Clarke, et al. A Geomatics Data Integration Technique for Coastal Change Monitoring[J]. Earth Surface Processes and Landforms.2005,30(6):651-664.
[36]柴登峰,舒宁,张剑清.动态DEM匹配方法研究[J].国土资源遥感.2002，1：38-42.
[37]柴登峰,舒宁,张剑清.利用匹配直线解求变换参数的方法[J].武汉大学学报·信息科学版.2002,27(2)：199-202.
[38]Qiuze Yu, Jinwen Tian,Jian Liu. A Novel Contour-based 3D Terrain Matching Algorithm Using Wavelet transform[J]. Pattern Recognition Letters.2004,25(1):87-99.
[39]Howard Schultz, Edward M Riseman,Frank R Stolle. Error Detection and DEM Fusion Using Self-Consistency[A]. The Seventh IEEE International Conference on Computer Vision[C].Kerkyra, Greece,1999.1174-1181.
[40]Y Postolov, A Krupnik.K McIntosh. Registration of Airborne Laser Data to Surfaces Generated by Photogrammetric Means[J]. International Archives of Photogrammetry and Remote Sensing.1999,32(3/W14): 95-99.
[41]Jon P Mills, Simon J Buckley,Harvey L Mitchell. Synergistic Fusion of GPS and Photogrammetrically Generated Elevation Models[J]. Photogrammetric Engineering & Remote Sensing.2003,69(4):341-349.
[42]Shih Yuan Lin, Jan Peter Muller, Jon P Mills,Pauline E Miller. An Assessment of Surface Matching for the Automated Co-registration of MOLA,HRSC and HiRISE DTMs[J]. Earth and Planetary Science Letters. 2010,294(3-4):520-533.
[43]Hans Gerd Maas. Least-Squares Matching with Airborne Laserscanning Data in a TIN Structure[J]. International Archives of Photogrammetry and Remote Sensing.2000,33(3A):548-555.
[44]Jiangwei Wu, Hongchao Ma,Qi Li. Least Squares Matching with Airborne Lidar Data for Strip Adjustmen[J]. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.2008. 37(B3b):167-172.
[45]冯义从,岑敏仪,张同刚.无控制DEM匹配与匹配优化技术[J].计算机工程与应用.2005,23：32-34.
[46]张同刚,岑敏仪,冯义从.用于无控制DEM匹配的LZD和ICP算法的比较[J].中国图象图形学报.2006,11(5)：714-719.
[47]Ko Nishino.Katsushi Ikeuchi. Robust Simultaneous Registration of Multiple Range Images[A]. The 5th Asian Conference on Computer Vision[C].Melbourne,Australia,2002.1-8.
[48]Kari Pulli. Multiview Registration for Large Data Sets[A]. Second International Conference on 3D Digital Imaging and Modeling [C].Ottawa,Canada,1999.160-168.
[49]Joaquim Salvi, Carles Matabosch, David Fofi,Josep Forest. A Review of Recent Range Image Registration Methods with Accuracy Evaluation[J]. Image and Vision Computing.2007,25(5):578-596.
[50]B M Planitz, A J Maeder,J A Williams. The Correspondence Framework for 3D Surface Matching Algorithms[J]. Computer Vision and Image Understanding.2005,97(3):347-383.
[51]冯义从,岑敏仪.三维自由表面匹配及其应用[J].测绘工程.2005,14(3)：36-40.
[52]Devrim Akca. Least Squares 3D Surface Matching[D]. Swiss Federal Institute of Technology Zurich, Doctor of Philosophy.2007.
[53]Ismail Elkhrachy. Towards an Automatic Registration for Terrestrial Laser Scanner Data[D]. Braunschweig University of Technology, Doctor of Philosophy.2008.
[54]Sameh M Yamany,Aly A Farag. Free-Form Surface Registration Using Surface Signatures[A]. IEEE International Conference on Computer Vision[C].1999.1098-1104.
[55]Andrew Edie Johnson. Spin-Images:A Representation for 3-D Surface Matching[D]. Carnegie Mellon University, Doctor of Philosophy.1997.
[56]Carles Matabosch, Elisabet Batlle, David Fofi,Joaquim Salvi. A Variant of Point-to-Plane Registration including Cycle Minimization[J]. Photogrammetric Computer Vision.2006,36(3):61-66.
[57]Zhong Zhang,Rick S Blum. A Hybrid Image Registration Technique for a Digital Camera Image Fusion Application[J]. Information Fusion.2000, 2(2):135-149.
[58]张宗华,彭翔,胡小唐.获取ICP匹配深度图像初值的研究[J].工程图学学报.2002,(1)：78-84.
[59]Barbara ZitovaJan Flusser. Image Registration Methods:a Survey[J]. Image and Vision Computing.2003,21(11):977-1000.
[60]Haili Chui,Anand Rangarajan. A New Point Matching Algorithm for Non-Rigid Registration[J]. Computer Vision and Image Understanding. 2003,89:114-141.
[61]Pierre Hellier,Christian Barillot. A Hierarchical Parametric Algorithom for Deformable Mutimodal Image Registration[J]. Computer Methods and Programs in Biomedicine.2004,75(2):107-115.
[62]潘小林,张丽艳,揭裕文,朱延娟.三维曲面部分匹配的算法研究[J].Journal of Nanjing University of Aeronautics & stronautics.2004,36(5): 543-549.
[63]朱延娟,周来水,张丽艳.散乱点云数据配准算法[J].计算机辅助设计与图形学学报.2006,18(4)：475-481.
[64]Yutaro Yamamura, Hyoungseop Kim,Akiyoshi Yamamoto. A Method for Image Registration by Maximization of Mutual Information[A]. SICE-ICASE International Joint Conference 2006[C].Bexco,Busan,Korea, 2006.1469-1473.
[65]Seok Lee, Minseok Choi, Hyungmin Kim,Frank Chongwoo Park. Geometric Direct Search Algorithms for Image Registration[J]. IEEE Transactions on Image Processing.2007,16(9):2215-2224.
[66]王蕊,李俊山,刘玲霞,罗蓉.基于几何特征的点云配准算法[J].华东理工大学学报(自然科学版).2009,35(5)：768-773.
[67]Chusong Chen, Yiping Hung,Jenbo Cheng. A Fast Automatic Method for Registration of Partially Overlapping Range Images[A]. Sixth International Conference on Computer Vision[C].Bombay,1998. 242-248.
[68]张同刚,岑敏仪,吴兴华.无控制DEM匹配的最小法向距离算法[J].自然科学进展.2006,16(7)：868-873.
[69]Paul J Besl,Neil D McKay. A Method for Registration of 3-D Shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.1992, 14(2):239-256.
[70]Zhengyou Zhang. Iterative Point Matching for Registration of Free-Form Curves and Surfaces[J]. International journal of Computer Vision.1994, 13(2):119-152.
[71]Raouf Benjemaa,Francis Schmitt. Fast Global Registration of 3D Sampled Surfaces using a Multi-z-buffer Technique[J]. Image and Vision Computing.1999,17(2):113-123.
[72]Baihua Li,Horst Holstein. Using k-d Trees for Robust 3D Point Pattern Matching[A]. Fourth International Conference on 3-D Digital Imaging and Modeling,2003[C].Banff, Alberta, Canada,2003.1-8.
[73]Andreas Nuchter, Kai Lingemann,Joachim Hertzberg. Cached k-d tree search for ICP algorithms[A]. Proceedings of the Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007)[C]. Montreal, QC, Canada,2007. IEEE Computer Society: 419-426.
[74]Tahir Rabbani, Sander Dijkman, Frank van den Heuvel,George Vosselman. An Integrated Approach for Modelling and Global Registration of Point Clouds[J]. ISPRS Journal of Photogrammetry and Remote Sensing.2007, 61(6):355-370.
[75]Yang Chen,Gerard Medioni. Object Modelling by Registration of Multiple Range Images[J]. Image and Vision Computing.1992,10(3):145-155.
[76]Devrim Akca. A New Algorithm for 3D Surface Matching[J]. International Archives of Photogrammetry Remote Sensing and Spatial Information.2004,35(7):960-965.
[77]Devrim Akca,Armin Gruen. Fast Correspondence Search for 3D Surface Matching[J]. International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences.2005, ⅩⅩⅩⅥ(3/W19): 186-191.
[78]Gtrard Blais,Martin D Levine. Registering Multiview Range Data to Create 3D Computer Objects[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.1995,17(8):820-824.
[79]郑德华.三维激光扫描数据处理的理论与方法[D].同济大学,工学博士.2005.
[80]Soon Yong Park,Murali Subbarao. An Accurate and Fast Point-to-Plane Registration Technique[J]. Pattern Recognition Letters.2003,24(16): 2967-2976.
[81]Yonghuai Liu. Constraints for Closest Point Finding[J]. Pattern Recognition Letters.2008,29(7):841-851.
[82]武殿梁,黄海量,丁玉成,赵万华.基于遗传算法和最小二乘法的曲面匹配[J].航空学报.2002,23(3)：285-288.
[83]Devrim Akca.Armin Gruen. Recent Advances in Least Squares 3D Surfaces Matching[A]. A. Grun and H. Kahmen. Optical 3-D Measurement Techniques VII[C].Vienna,Austria,2005.197-206.
[84]Evgeny Lomonosov, Dmitry Chetverikov,Aniko Ekart. Pre-registration of Arbitrarily Oriented 3D Surfaces Using a Genetic Algorithm[J]. Pattern Recognition Letters.2006,27:1201-1208.
[85]靳蕃.神经计算智能基础(原理、方法)[M].西南交通大学出版社,2000.
[86]Jean Jose Jacq,Christian Roux. Automatic Registration of 3D Images Using a Simple Genetic Algorithm with a Stochastic Performance Function[A]. IEEE. The 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Society[C].1993.126-127.
[87]Jean-Jose Jacq,Christian Roux. Registration of 3-D Images by Genetic Optimization[J]. Pattern Recognition Letters.1995,16(8):823-841.
[88]Jean Michel Rouet, Jean Jose Jacq,Christian Roux.3D Elastic Multimodality Image Registration Through a Genetic Algorithm[A]. IEEE. the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society[C].1998.663-666.
[89]Lawrence H Staib,Xianzhang Lei. Intermodality 3D Medical Image Registration with Global Search[A]. the IEEE Workshop on Biomedical Image Analysis[C].Seattle, WA, USA,1994.225-234.
[90]K Brunnstrom,A J Stoddart. Genetic Algorithms for Free-Form Surface Matching[J]. Pattern Recognition.1996,4(3):689-693.
[91]S M Yamany, M N Ahmed, E E Hemayed,A A Farag. Novel Surface Registration Using the Grid Closest Point (GCP) Transform[A]. IEEE. IEEE International Conference on Image Processing[C].Chicago, IL,USA 1998.809-813
[92]Jagath C Rajapakse,Guojun Bao. Functional MR Image Registration Using a Genetic Algorithm[A]. IEEE. The 6th International Conference on Neural Information Processing[C].Perth,WA,Australia 1999.922-927.
[93]Chi Kin Chow, Hung Tat Tsui, Tong Lee,Tze Kin Lau. Medical Image Registration and Model Construction using Genetic Algorithms [A]. I.C. Society. International Workshop on Medical Imaging and Augmented Reality[C].Washington, DC,2001.174-179.
[94]Chi Kin Chow, Hung Tat Tsui,Tong Lee. Surface Registration using a Dynamic Genetic Algorithm[J]. Pattern Recognition.2004,37(1): 105-117.
[95]Tianzi Jiang,Yong Fan. Parallel Genetic Algorithm for 3D Medical Image Analysis[A]. IEEE.2002 IEEE International Conference on Systems, Man and Cybernetics [C].2002.1-6.
[96]Craig Robertson,Robert B Fisher. Parallel Evolutionary Registration of Range Data[J]. Computer Vision and Image Understanding.2002,87(1-3): 39-50.
[97]Oscar Cord'on, Sergio Damas,Jose Santamar'ia, A CHC Evolutionary Algorithm for 3D Image Registration, in LNAI.2003, Springer. p. 404-411.
[98]Luciano Silva,Olga R P Bellon. Enhanced, Robust Genetic Algorithms for Multiview Range Image Registration[A]. The 4th International Conference on 3-D Digital Imaging and Modeling[C].2003.268-275.
[99]Luciano Silva, Olga R P Bellon, Paulo F U Gotardo,Kim L Boyer. Range Image Registration Using Enhanced Genetic Algorithm[A].2003 International Conference on Image Processing [C].2003.711-714.
[100]Luciano Silva, Olga R P Bellon,Kim L Boyer. Precision Range Image Registration Using a Robust Surface Interpenetration Measure and Enhanced Genetic Algorithms[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2005,27(5):762-776.
[101]Berthold K P Horn,John G Harris. Rigid Body Motion from Range Image Sequences[J]. Image Understanding.1991,53(1):1-13.
[102]John W Branch, Flavio Prieto,Pierre Boulanger. Correspondence Method for Registration of Range Images Using Evolutionary Algorithms[A]. IEEE. International Conference on Industrial Electronics and Control Applications,2005[C].2005.1-6.
[103]D Chetverikov, D Svirko, D Stepanov,Pavel Krsek. The Trimmed Iterative Closest Point algorithm[J]. Pattern Recognition.2002,3:545-548.
[104]Wen Peng, Ruofeng Tong, Guiping Qian,Jinxiang Dong. A Local Registration Approach of Medical Images with Niche Genetic Algorithm[A]. The 10th International Conference on Computer Supported Cooperative Work in Design,2006[C].2006.1-6.
[105]Yongming Zhao, Su Zhang, Changyan Xiao,Yazhu Chen. A Hybrid Genetic Algorithm for Multimodal Image Registration[J]. Journal of Harbin Institute of Technology(New Series).2006,13(1):82-87.
[106]郭慧,潘家祯,林大钧.基于实数编码的多种群遗传算法的点云配准[J].华东理工大学学报(自然科学版).2007,33(5)：733-736.
[107]高鹏东,彭翔,李阿蒙,刘晓利.ICP框架下基于表面间平均体积测度的深度像配准[J].计算机辅助设计与图形学学报.2007,19(6)：719-724.
[108]高鹏东,彭翔,李阿蒙,刘则毅.基于混合遗传算法和表面间平均体积度量的深度像匹配[J].计算机学报.2007,30(12)：2189-2197.
[109]彭育辉,高诚辉,何炳蔚.基于遗传算法和四元数法的曲面线扫描数据的配准[J].福州大学学报(自然科学版).2008,36(4)：541-546.
[110]杨容浩,岑敏仪,张同刚,杨佳.基于遗传算法的DEM粗匹配研究[J].工程勘察.2010,38(6)：61-65.
[111]J H Friedman,J L Bentley. An Algorithm for Finding Best Matches in Logarithmic Expected Time[J]. ACM Transactions on Mathematical Software.1977,3(3):209-226.
[112]Bastien Granger, Xavier Pennec,Alexis Roche. Rigid Point-Surface Registration using Oriented Points and an EM Variant of ICP for Computer Guided Oral Implantology[J]. Lecture Notes in Computer Science.2001,2208:752-775.
[113]Timo Zinber, Jochen Schmidt,Heinrich Niemann. A Refined ICP Algorithm for Robust 3-D Correspondence Estimation[A]. IEEE.2003 International Conference on Image Processing[C].2003.695-698.
[114]Yonghuai Liu. Improving ICP with Easy Implementation for Free-Form Surface Matching[J]. Pattern Recognition.2004,37(2):211-226.
[115]Ahmad Almhdie, Christophe Le'ger, Mohamed Deriche,Roger Le'de'e. 3D Registration using a New Implementation of the ICP Algorithm based on a Comprehensive Lookup Matrix:Application to Medical Imaging[J]. Pattern Recognition Letters.2007,28(12):1523-1533.
[116]Andreas N"uchter, Kai Lingemann,Joachim Hertzberg. Cached k-d tree search for ICP algorithms.[A]. Proceedings of the Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007)[C]. Montreal, QC, Canada,2007. IEEE Computer Society: 419-426.
[117]Andrea Censi. An Accurate Closed-form Estimate of ICP's Covariance[A]. 2007 IEEE International Conference on Robotics and Automation[C].Roma,2007. IEEE:3167-3172.
[118]Zexiao Xie, Shang Xu,Xuyong Li. A High-accuracy Method for Fine Registration of Overlapping Point Clouds[J]. Image and Vision Computing.2010,28(4):563-570.
[119]薛耀红.点云数据配准及曲面细分技术研究[D].吉林大学,理学博士.2010.
[120]郑德华,岳东杰,岳建平.基于几何特征约束的建筑物点云配准算法[J].测绘学报.2008,37(4)：464-468.
[121]Szymon Rusinkiewicz,Marc Levoy. Efficient Variants of the ICP Algorithm [A]. IEEE. The 3rd International Conference on 3-D Digital Imaging and Modeling[C].Quebec City,Canada,2001.145-152.
[122]Ayman Habib,Toni Schenk. A New Approach for Matching Surfaces from Laser Scanners and Optical Scanners [J]. International Archives of Photogrammetry and Remote Sensing.1999,32:1-7.
[123]Oscar Cordon,Ergio Damas. Image Registration with Iterated Local Search[J]. Journal of Heuristics.2006,12(1-2):73-94.
[124]Armin Gruen,Devrim Akca. Least squares 3D surface and curve matching[J]. ISPRS Journal of Photogrammetry and Remote Sensing. 2005,59:151-174.
[125]Shinji Umeyama. Least-Squares Estimation of Transformation Parameters between Two Point patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.1991,13(4):376-380.
[126]Ken Shoemake. Animating Rotation with Quaternion Curves[A]. The 12th Annual Conference on Computer Graphics and Interactive Techniques[C].1985.245-254.
[127]Berthold K P Horn, Hugh M Hilden,Shariar Negahdaripour. Closed-form Solution of Absolute Orientation using Orthonormal Matrices[J]. Journal of Optical Society of America A.1988,5(7):1127-1136.
[128]David A Forsyth. Computer Vsion a Modern Approach[M]. 清华大学出 版社,2004:598-602.
[129]Yang Chen,Gerard Medioni. Object Modelling by Registration of Multiple Range Images[J]. Image and Vision Computing.1992,10(3):145-155.
[130]Michael Potmesil. Generating Models for Solid Objects by Matching 3D Surface Segments[A]. The International Joint Conference on Artificial Intelligence[C].Kalsruche,West Germany,1983.1089-1093.
[131]Devrim Akca,Armin Gruen. Generalized Least Squares Multiple 3D Surface Matching[A]. ISPRS Workshop on Laser Scanning[C].Espoo, 2007.1-7.
[132]杨容浩,岑敏仪,张同刚,杨佳.融合遗传算法的最小二乘无控制DEM匹配[J].测绘科学.2011,36(1)：146-149.
[133]Robert Bergevin, Denis Laurendeau,Denis Poussart. Registering Range Views of Multipart Objects[J]. Computer Vision and Image Understanding.1995,61(1):1-16.
[134]Andrew O Finley.Ronald E McRoberts. Efficient K-nearest Neighbor Searches for Multi-source Forest Attribute Mapping[J]. Remote Sensing of Environment.2008,112(5):2203-2211.
[135]Jon Louis Bentley. Multidimensional Binary Search Trees Used for Associative Searching[J]. Communications of the ACM.1975,18(9): 509-516.
[136]Robert F Sproull. Refinements to nearest-neighbor searching in k-dimensional trees[J]. Algorithmica.1991,6(1-6):579-589.
[137]Timothee Jost. Fast Geometric Matching for Shape Registration[D]. University of Neuchatel, Doctor of Philosophy.2002.
[138]Michael Greenspan, Guy Godin,Jimmy Talbot. Acceleration of Binning Nearest Neighbour Methods[A]. Vision Interface 2000[C].Montreal,2000. National Research Council of Canada:337-344.
[139]Sunil Arya, David M Mount, Nathan S Netanyahu, Ruth Silverman, et al. An Optimal Algorithm for Approximate Nearest Neighbor Searching in Fixed Dimensions[J]. Journal of ACM.1998,45(6):891-923.
[140]Dmitry Chetverikov. Fast Neighborhood Search in Planar Point Sets[J]. Pattern Recogniton Letters.1991,12(7):409-412.
[141]王小平,曹立明.遗传算法——理论、应用与软件实现[M].西安交通大学出版社,2003.
[142]杨容浩,岑敏仪,张同刚,杨佳.遗传算法和最小二乘匹配相结合的DEM匹配方法[J].水土保持通报.2010,30(3)：128-133.
[143]莫鸿强.遗传算法搜索能力和编码方式研究[D].华南理工大学,工学博士.2001.
[144]朱灿.实数编码遗传算法机理分析及算法改进研究[D].中南大学,工学博士.2009.
[145]姜昌华.遗传算法在物流系统优化中的应用研究[D].华东师范大学,工学博士.2007.
[146]雷英杰,张善文,李续武,周创明.MATLAB遗传算法工具箱及应用[M].西安电子科技大学出版社,2006.
[147]Gunter Rudolph. Convergence Analysis of Canonical Genetic Algorithm[J]. IEEE Transactions on Neural Networks.1994,5(1): 96-101.
[148]陈宝林.最优化理论与算法[M].清华大学出版社,2005：282-328.
[2]汤国安,刘学军,闾国年.数字高程模型及地学分析的原理与方法[M].科学出版社,2005.
[3]Naser EL-Sheimy, Caterina Valeo,Ayman Habib. Digital Terrain Modeling: Acquisition,Manipulation, and Applications[M]. Artech House, INC. 2005.
[4]Berthold K P Horn. Closed-form solution of absolute orientation using unit quaternions[J]. Journal of the Optical Society of America.1987,4(4): 629-642.
[5]D Rosenholm.K Torlegard. Three-Dimensional Absolute Orientation of Stereo Models Using Digital Elevation Models[J]. Photogrammetric Engineering and Remote Sensing.1988,54(10):1385-1389.
[6]E H Thompson. A Method for the Construction of Orthogonal Matrices[J]. The Photogrammetric Record.1959,3(13):55-59.
[7]G H Schut. On Exact Linear Equations for the Computation of the Rotational Elements of Absolute Orientation [J]. Photogrammetria.1960, 17(1):34-37.
[8]H L Oswal,S Balasubramanian. An Exact Solution of Absolute Orientation[J]. Photogrammetric Engineering.1968,34:1079-1083.
[9]K S Arun, T S Huang,S D Blostein. Least-Squares Fitting of Two 3-D Point Sets[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.1987, Pami-9(5):698-700.
[10]Buckley Simon John, J P Mills,Mitchell Harvey Lawson. Improving the Accuracy of Photogrammetric Absolute Orientation Using Surface Matching[A]. XXth ISPRS Congress[C].Istanbul,Turkey,2004.1-6.
[11]Heinrich Ebner,Franz Mueller. Processing of Digital Three-line Imagery Using a Generalized Model for Combined Point Determination[J]. Photogrammetria.1987,41(3):173-182.
[12]Heinrich Ebner,Giinter Strunz. Combined Point Determination using Digital Terrain Models as Control Information[J]. International Archives of Photogrammetry and Remote Sensing.1988,27(B11/3):578-587.
[13]Dmitry B Goldgof, Thomas S Huang,Hua Lee. Feature Extraction and Terrain Matching[A]. IEEE Conference on Computer Vision and Pattern Recognition[C].Ann Arbor,1988.899-904.
[14]张同刚.无控制DEM匹配与差异探测及其在泥石流灾害地区的应用[D].西南交通大学,工学博士.2006.
[15]Heinrich Ebner,Timm Ohlhof. Utilization of Ground Control Points for Image Orientation without Point Identification in Image Space[J]. ISPRS Commission III Symposium:Spatial Information from Digital Photogrammetry and Computer Vision.1994,2357:206-211.
[16]Jen Jer Jaw. Control Surface in Aerial Triangulation[J]. International Archives of Photogrammetry and Remote Sensing.2000,44(B3): 444-451.
[17]Timothy D James, Tavi Murray, Nicholas E Barrand,Stuart L Barr. Extracting Photogrammetric Ground Control From Lidar Dems for Change Detection[J]. The Photogrammetric Record.2006,21(116): 312-328.
[18]Jose A Goncalves,Andre R S Marcal. Automatic Ortho-rectification of ASTER Images by Matching Digital Elevation Models[A]. The 4th International Conference on Image Analysis and Recognition[C].2007. Heidelberg:1265-1275.
[19]Pauline Miller, Jon Mills, Stuart Edwards, Paul Bryan, et al. A Robust Surface Matching Technique for Coastal Geohazard Assessment and Management[J]. ISPRS Journal of Photogrammetry and Remote Sensing. 2008,63(2):529-542.
[20]Zhengxiao Li,James Bethel. DEM Registration,Alignment and Evaluation for SAR Interferometry[A]. T.I.S.f.P.a.R. Sensing, the XXI International Congress for Photogrammetry and Remote Sensing[C].Beijing,2008. Orgnising Committee of the XXI International Congress for Photogrammetry and Remote Sensing:111-115.
[21]Massimo Fabris,Arianna Pesci. Automated DEM Extraction in Digital Aerial Photogrammetry:Precisions and Validation for Mass Movement Monitoring[J]. Annals of Geophysics.2005,48(6):973-988.
[22]Lloyd Pilgrim. Robust Estimation Applied to Surface Matching[J]. ISPRS Journal of Photogrammetry & Remote Sensing.1996,2000(B3): 1000-1007.
[23]Zhilin Li, Zhu Xu, Minyi Cen,Xiaoli Ding. Robust Surface Matching for Automated Detection of Local Deformations Using Least-Median-of-Squares Estimator[J]. Photogrammetric Engineering & Remote Sensing. 2001,67(11):1283-1292.
[24]Zhu Xu,Zhilin Li. Least Median of Squares Matching for Automated Detection of Surface Deformations[J]. nternational Archives of Photogrammetry and Remote Sensing.2000,33(B3):1000-1007.
[25]Arianna Pesci, Paolo Baldi, Alessandro Bedin, Giuseppe Casula, et al. Digital Elevation Models for Landslide Evolution Monitoring: Application on Two Areas Located in the Reno River Valley (Italy)[J]. Annals of Geophysics.2004,47(4):1339-1353.
[26]G Bitelli, M Dubbini,A Zanutta. Terrestrial Laser Scanning and Digital Photogrammetry Techniques to Monitoring Landslide Bodies[J]. International Archivies of Photogrammetry,Remote Sensing and Spational Information Sciences.2004,35(Part B5):246-251.
[27]张同刚,岑敏仪,吴兴华.无控制DEM表面差异探测研究[J].测绘科学.2006,31(3)：36-39.
[28]张同刚,岑敏仪,秦军,吴兴华.多时相DEM匹配探测泥石流地表变形的新方法[J].水土保持通报.2006,26(3)：96-100.
[29]张同刚,岑敏仪,吴兴华.基于差分模型的无控制DEM差异探测方法[J].西南交通大学学报.2006,41(1)：91-96.
[30]Tonggang Zhang,Minyi Cen. Robust DEM Co-registration Method for Terrain Changes Assessment Using Least Trimmed Squares Estimator[J]. Advances in Space Research.2008,41(11):1827-1835.
[31]Giordano Teza, Antonio Galgaro, Rinaldo Genevois,N.Zaltron. Terrestrial Laser Scanner to Detect Landslide Displacement Fields:a New Approach[J]. International Journal for Remote Sensing.2007,28(15-16): 3425-3446.
[32]Giordano Teza, Arianna Pesci, Rinaldo Genevois,Antonio Galgaro. Characterization of landslide ground surface kinematics from terrestrial laser scanning and strain field computation[J]. Geomorphology.2008, 97(3-4):424-437.
[33]O Monserrat,M Crosetto. Deformation Measurement Using Terrestrial Laser Scanning Data and Least Squares 3D Surface Matching[J]. ISPRS Journal of Photogrammetry and Remote Sensing.2008,63(1):142-154.
[34]P Baldi, S Bonvalot, P Briole,M Marsella. Digital Photogrammetry and Kinematic GPS Applied to the Monitoring of Vulcano Island,Aeolian Arc,Italy[J]. International Journal of Geophysics.2000,142(3):801-811.
[35]J P Mills, S J Buckley, H L Mitchell, P J Clarke, et al. A Geomatics Data Integration Technique for Coastal Change Monitoring[J]. Earth Surface Processes and Landforms.2005,30(6):651-664.
[36]柴登峰,舒宁,张剑清.动态DEM匹配方法研究[J].国土资源遥感.2002，1：38-42.
[37]柴登峰,舒宁,张剑清.利用匹配直线解求变换参数的方法[J].武汉大学学报·信息科学版.2002,27(2)：199-202.
[38]Qiuze Yu, Jinwen Tian,Jian Liu. A Novel Contour-based 3D Terrain Matching Algorithm Using Wavelet transform[J]. Pattern Recognition Letters.2004,25(1):87-99.
[39]Howard Schultz, Edward M Riseman,Frank R Stolle. Error Detection and DEM Fusion Using Self-Consistency[A]. The Seventh IEEE International Conference on Computer Vision[C].Kerkyra, Greece,1999.1174-1181.
[40]Y Postolov, A Krupnik.K McIntosh. Registration of Airborne Laser Data to Surfaces Generated by Photogrammetric Means[J]. International Archives of Photogrammetry and Remote Sensing.1999,32(3/W14): 95-99.
[41]Jon P Mills, Simon J Buckley,Harvey L Mitchell. Synergistic Fusion of GPS and Photogrammetrically Generated Elevation Models[J]. Photogrammetric Engineering & Remote Sensing.2003,69(4):341-349.
[42]Shih Yuan Lin, Jan Peter Muller, Jon P Mills,Pauline E Miller. An Assessment of Surface Matching for the Automated Co-registration of MOLA,HRSC and HiRISE DTMs[J]. Earth and Planetary Science Letters. 2010,294(3-4):520-533.
[43]Hans Gerd Maas. Least-Squares Matching with Airborne Laserscanning Data in a TIN Structure[J]. International Archives of Photogrammetry and Remote Sensing.2000,33(3A):548-555.
[44]Jiangwei Wu, Hongchao Ma,Qi Li. Least Squares Matching with Airborne Lidar Data for Strip Adjustmen[J]. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.2008. 37(B3b):167-172.
[45]冯义从,岑敏仪,张同刚.无控制DEM匹配与匹配优化技术[J].计算机工程与应用.2005,23：32-34.
[46]张同刚,岑敏仪,冯义从.用于无控制DEM匹配的LZD和ICP算法的比较[J].中国图象图形学报.2006,11(5)：714-719.
[47]Ko Nishino.Katsushi Ikeuchi. Robust Simultaneous Registration of Multiple Range Images[A]. The 5th Asian Conference on Computer Vision[C].Melbourne,Australia,2002.1-8.
[48]Kari Pulli. Multiview Registration for Large Data Sets[A]. Second International Conference on 3D Digital Imaging and Modeling [C].Ottawa,Canada,1999.160-168.
[49]Joaquim Salvi, Carles Matabosch, David Fofi,Josep Forest. A Review of Recent Range Image Registration Methods with Accuracy Evaluation[J]. Image and Vision Computing.2007,25(5):578-596.
[50]B M Planitz, A J Maeder,J A Williams. The Correspondence Framework for 3D Surface Matching Algorithms[J]. Computer Vision and Image Understanding.2005,97(3):347-383.
[51]冯义从,岑敏仪.三维自由表面匹配及其应用[J].测绘工程.2005,14(3)：36-40.
[52]Devrim Akca. Least Squares 3D Surface Matching[D]. Swiss Federal Institute of Technology Zurich, Doctor of Philosophy.2007.
[53]Ismail Elkhrachy. Towards an Automatic Registration for Terrestrial Laser Scanner Data[D]. Braunschweig University of Technology, Doctor of Philosophy.2008.
[54]Sameh M Yamany,Aly A Farag. Free-Form Surface Registration Using Surface Signatures[A]. IEEE International Conference on Computer Vision[C].1999.1098-1104.
[55]Andrew Edie Johnson. Spin-Images:A Representation for 3-D Surface Matching[D]. Carnegie Mellon University, Doctor of Philosophy.1997.
[56]Carles Matabosch, Elisabet Batlle, David Fofi,Joaquim Salvi. A Variant of Point-to-Plane Registration including Cycle Minimization[J]. Photogrammetric Computer Vision.2006,36(3):61-66.
[57]Zhong Zhang,Rick S Blum. A Hybrid Image Registration Technique for a Digital Camera Image Fusion Application[J]. Information Fusion.2000, 2(2):135-149.
[58]张宗华,彭翔,胡小唐.获取ICP匹配深度图像初值的研究[J].工程图学学报.2002,(1)：78-84.
[59]Barbara ZitovaJan Flusser. Image Registration Methods:a Survey[J]. Image and Vision Computing.2003,21(11):977-1000.
[60]Haili Chui,Anand Rangarajan. A New Point Matching Algorithm for Non-Rigid Registration[J]. Computer Vision and Image Understanding. 2003,89:114-141.
[61]Pierre Hellier,Christian Barillot. A Hierarchical Parametric Algorithom for Deformable Mutimodal Image Registration[J]. Computer Methods and Programs in Biomedicine.2004,75(2):107-115.
[62]潘小林,张丽艳,揭裕文,朱延娟.三维曲面部分匹配的算法研究[J].Journal of Nanjing University of Aeronautics & stronautics.2004,36(5): 543-549.
[63]朱延娟,周来水,张丽艳.散乱点云数据配准算法[J].计算机辅助设计与图形学学报.2006,18(4)：475-481.
[64]Yutaro Yamamura, Hyoungseop Kim,Akiyoshi Yamamoto. A Method for Image Registration by Maximization of Mutual Information[A]. SICE-ICASE International Joint Conference 2006[C].Bexco,Busan,Korea, 2006.1469-1473.
[65]Seok Lee, Minseok Choi, Hyungmin Kim,Frank Chongwoo Park. Geometric Direct Search Algorithms for Image Registration[J]. IEEE Transactions on Image Processing.2007,16(9):2215-2224.
[66]王蕊,李俊山,刘玲霞,罗蓉.基于几何特征的点云配准算法[J].华东理工大学学报(自然科学版).2009,35(5)：768-773.
[67]Chusong Chen, Yiping Hung,Jenbo Cheng. A Fast Automatic Method for Registration of Partially Overlapping Range Images[A]. Sixth International Conference on Computer Vision[C].Bombay,1998. 242-248.
[68]张同刚,岑敏仪,吴兴华.无控制DEM匹配的最小法向距离算法[J].自然科学进展.2006,16(7)：868-873.
[69]Paul J Besl,Neil D McKay. A Method for Registration of 3-D Shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.1992, 14(2):239-256.
[70]Zhengyou Zhang. Iterative Point Matching for Registration of Free-Form Curves and Surfaces[J]. International journal of Computer Vision.1994, 13(2):119-152.
[71]Raouf Benjemaa,Francis Schmitt. Fast Global Registration of 3D Sampled Surfaces using a Multi-z-buffer Technique[J]. Image and Vision Computing.1999,17(2):113-123.
[72]Baihua Li,Horst Holstein. Using k-d Trees for Robust 3D Point Pattern Matching[A]. Fourth International Conference on 3-D Digital Imaging and Modeling,2003[C].Banff, Alberta, Canada,2003.1-8.
[73]Andreas Nuchter, Kai Lingemann,Joachim Hertzberg. Cached k-d tree search for ICP algorithms[A]. Proceedings of the Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007)[C]. Montreal, QC, Canada,2007. IEEE Computer Society: 419-426.
[74]Tahir Rabbani, Sander Dijkman, Frank van den Heuvel,George Vosselman. An Integrated Approach for Modelling and Global Registration of Point Clouds[J]. ISPRS Journal of Photogrammetry and Remote Sensing.2007, 61(6):355-370.
[75]Yang Chen,Gerard Medioni. Object Modelling by Registration of Multiple Range Images[J]. Image and Vision Computing.1992,10(3):145-155.
[76]Devrim Akca. A New Algorithm for 3D Surface Matching[J]. International Archives of Photogrammetry Remote Sensing and Spatial Information.2004,35(7):960-965.
[77]Devrim Akca,Armin Gruen. Fast Correspondence Search for 3D Surface Matching[J]. International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences.2005, ⅩⅩⅩⅥ(3/W19): 186-191.
[78]Gtrard Blais,Martin D Levine. Registering Multiview Range Data to Create 3D Computer Objects[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.1995,17(8):820-824.
[79]郑德华.三维激光扫描数据处理的理论与方法[D].同济大学,工学博士.2005.
[80]Soon Yong Park,Murali Subbarao. An Accurate and Fast Point-to-Plane Registration Technique[J]. Pattern Recognition Letters.2003,24(16): 2967-2976.
[81]Yonghuai Liu. Constraints for Closest Point Finding[J]. Pattern Recognition Letters.2008,29(7):841-851.
[82]武殿梁,黄海量,丁玉成,赵万华.基于遗传算法和最小二乘法的曲面匹配[J].航空学报.2002,23(3)：285-288.
[83]Devrim Akca.Armin Gruen. Recent Advances in Least Squares 3D Surfaces Matching[A]. A. Grun and H. Kahmen. Optical 3-D Measurement Techniques VII[C].Vienna,Austria,2005.197-206.
[84]Evgeny Lomonosov, Dmitry Chetverikov,Aniko Ekart. Pre-registration of Arbitrarily Oriented 3D Surfaces Using a Genetic Algorithm[J]. Pattern Recognition Letters.2006,27:1201-1208.
[85]靳蕃.神经计算智能基础(原理、方法)[M].西南交通大学出版社,2000.
[86]Jean Jose Jacq,Christian Roux. Automatic Registration of 3D Images Using a Simple Genetic Algorithm with a Stochastic Performance Function[A]. IEEE. The 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Society[C].1993.126-127.
[87]Jean-Jose Jacq,Christian Roux. Registration of 3-D Images by Genetic Optimization[J]. Pattern Recognition Letters.1995,16(8):823-841.
[88]Jean Michel Rouet, Jean Jose Jacq,Christian Roux.3D Elastic Multimodality Image Registration Through a Genetic Algorithm[A]. IEEE. the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society[C].1998.663-666.
[89]Lawrence H Staib,Xianzhang Lei. Intermodality 3D Medical Image Registration with Global Search[A]. the IEEE Workshop on Biomedical Image Analysis[C].Seattle, WA, USA,1994.225-234.
[90]K Brunnstrom,A J Stoddart. Genetic Algorithms for Free-Form Surface Matching[J]. Pattern Recognition.1996,4(3):689-693.
[91]S M Yamany, M N Ahmed, E E Hemayed,A A Farag. Novel Surface Registration Using the Grid Closest Point (GCP) Transform[A]. IEEE. IEEE International Conference on Image Processing[C].Chicago, IL,USA 1998.809-813
[92]Jagath C Rajapakse,Guojun Bao. Functional MR Image Registration Using a Genetic Algorithm[A]. IEEE. The 6th International Conference on Neural Information Processing[C].Perth,WA,Australia 1999.922-927.
[93]Chi Kin Chow, Hung Tat Tsui, Tong Lee,Tze Kin Lau. Medical Image Registration and Model Construction using Genetic Algorithms [A]. I.C. Society. International Workshop on Medical Imaging and Augmented Reality[C].Washington, DC,2001.174-179.
[94]Chi Kin Chow, Hung Tat Tsui,Tong Lee. Surface Registration using a Dynamic Genetic Algorithm[J]. Pattern Recognition.2004,37(1): 105-117.
[95]Tianzi Jiang,Yong Fan. Parallel Genetic Algorithm for 3D Medical Image Analysis[A]. IEEE.2002 IEEE International Conference on Systems, Man and Cybernetics [C].2002.1-6.
[96]Craig Robertson,Robert B Fisher. Parallel Evolutionary Registration of Range Data[J]. Computer Vision and Image Understanding.2002,87(1-3): 39-50.
[97]Oscar Cord'on, Sergio Damas,Jose Santamar'ia, A CHC Evolutionary Algorithm for 3D Image Registration, in LNAI.2003, Springer. p. 404-411.
[98]Luciano Silva,Olga R P Bellon. Enhanced, Robust Genetic Algorithms for Multiview Range Image Registration[A]. The 4th International Conference on 3-D Digital Imaging and Modeling[C].2003.268-275.
[99]Luciano Silva, Olga R P Bellon, Paulo F U Gotardo,Kim L Boyer. Range Image Registration Using Enhanced Genetic Algorithm[A].2003 International Conference on Image Processing [C].2003.711-714.
[100]Luciano Silva, Olga R P Bellon,Kim L Boyer. Precision Range Image Registration Using a Robust Surface Interpenetration Measure and Enhanced Genetic Algorithms[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.2005,27(5):762-776.
[101]Berthold K P Horn,John G Harris. Rigid Body Motion from Range Image Sequences[J]. Image Understanding.1991,53(1):1-13.
[102]John W Branch, Flavio Prieto,Pierre Boulanger. Correspondence Method for Registration of Range Images Using Evolutionary Algorithms[A]. IEEE. International Conference on Industrial Electronics and Control Applications,2005[C].2005.1-6.
[103]D Chetverikov, D Svirko, D Stepanov,Pavel Krsek. The Trimmed Iterative Closest Point algorithm[J]. Pattern Recognition.2002,3:545-548.
[104]Wen Peng, Ruofeng Tong, Guiping Qian,Jinxiang Dong. A Local Registration Approach of Medical Images with Niche Genetic Algorithm[A]. The 10th International Conference on Computer Supported Cooperative Work in Design,2006[C].2006.1-6.
[105]Yongming Zhao, Su Zhang, Changyan Xiao,Yazhu Chen. A Hybrid Genetic Algorithm for Multimodal Image Registration[J]. Journal of Harbin Institute of Technology(New Series).2006,13(1):82-87.
[106]郭慧,潘家祯,林大钧.基于实数编码的多种群遗传算法的点云配准[J].华东理工大学学报(自然科学版).2007,33(5)：733-736.
[107]高鹏东,彭翔,李阿蒙,刘晓利.ICP框架下基于表面间平均体积测度的深度像配准[J].计算机辅助设计与图形学学报.2007,19(6)：719-724.
[108]高鹏东,彭翔,李阿蒙,刘则毅.基于混合遗传算法和表面间平均体积度量的深度像匹配[J].计算机学报.2007,30(12)：2189-2197.
[109]彭育辉,高诚辉,何炳蔚.基于遗传算法和四元数法的曲面线扫描数据的配准[J].福州大学学报(自然科学版).2008,36(4)：541-546.
[110]杨容浩,岑敏仪,张同刚,杨佳.基于遗传算法的DEM粗匹配研究[J].工程勘察.2010,38(6)：61-65.
[111]J H Friedman,J L Bentley. An Algorithm for Finding Best Matches in Logarithmic Expected Time[J]. ACM Transactions on Mathematical Software.1977,3(3):209-226.
[112]Bastien Granger, Xavier Pennec,Alexis Roche. Rigid Point-Surface Registration using Oriented Points and an EM Variant of ICP for Computer Guided Oral Implantology[J]. Lecture Notes in Computer Science.2001,2208:752-775.
[113]Timo Zinber, Jochen Schmidt,Heinrich Niemann. A Refined ICP Algorithm for Robust 3-D Correspondence Estimation[A]. IEEE.2003 International Conference on Image Processing[C].2003.695-698.
[114]Yonghuai Liu. Improving ICP with Easy Implementation for Free-Form Surface Matching[J]. Pattern Recognition.2004,37(2):211-226.
[115]Ahmad Almhdie, Christophe Le'ger, Mohamed Deriche,Roger Le'de'e. 3D Registration using a New Implementation of the ICP Algorithm based on a Comprehensive Lookup Matrix:Application to Medical Imaging[J]. Pattern Recognition Letters.2007,28(12):1523-1533.
[116]Andreas N"uchter, Kai Lingemann,Joachim Hertzberg. Cached k-d tree search for ICP algorithms.[A]. Proceedings of the Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007)[C]. Montreal, QC, Canada,2007. IEEE Computer Society: 419-426.
[117]Andrea Censi. An Accurate Closed-form Estimate of ICP's Covariance[A]. 2007 IEEE International Conference on Robotics and Automation[C].Roma,2007. IEEE:3167-3172.
[118]Zexiao Xie, Shang Xu,Xuyong Li. A High-accuracy Method for Fine Registration of Overlapping Point Clouds[J]. Image and Vision Computing.2010,28(4):563-570.
[119]薛耀红.点云数据配准及曲面细分技术研究[D].吉林大学,理学博士.2010.
[120]郑德华,岳东杰,岳建平.基于几何特征约束的建筑物点云配准算法[J].测绘学报.2008,37(4)：464-468.
[121]Szymon Rusinkiewicz,Marc Levoy. Efficient Variants of the ICP Algorithm [A]. IEEE. The 3rd International Conference on 3-D Digital Imaging and Modeling[C].Quebec City,Canada,2001.145-152.
[122]Ayman Habib,Toni Schenk. A New Approach for Matching Surfaces from Laser Scanners and Optical Scanners [J]. International Archives of Photogrammetry and Remote Sensing.1999,32:1-7.
[123]Oscar Cordon,Ergio Damas. Image Registration with Iterated Local Search[J]. Journal of Heuristics.2006,12(1-2):73-94.
[124]Armin Gruen,Devrim Akca. Least squares 3D surface and curve matching[J]. ISPRS Journal of Photogrammetry and Remote Sensing. 2005,59:151-174.
[125]Shinji Umeyama. Least-Squares Estimation of Transformation Parameters between Two Point patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.1991,13(4):376-380.
[126]Ken Shoemake. Animating Rotation with Quaternion Curves[A]. The 12th Annual Conference on Computer Graphics and Interactive Techniques[C].1985.245-254.
[127]Berthold K P Horn, Hugh M Hilden,Shariar Negahdaripour. Closed-form Solution of Absolute Orientation using Orthonormal Matrices[J]. Journal of Optical Society of America A.1988,5(7):1127-1136.
[128]David A Forsyth. Computer Vsion a Modern Approach[M]. 清华大学出 版社,2004:598-602.
[129]Yang Chen,Gerard Medioni. Object Modelling by Registration of Multiple Range Images[J]. Image and Vision Computing.1992,10(3):145-155.
[130]Michael Potmesil. Generating Models for Solid Objects by Matching 3D Surface Segments[A]. The International Joint Conference on Artificial Intelligence[C].Kalsruche,West Germany,1983.1089-1093.
[131]Devrim Akca,Armin Gruen. Generalized Least Squares Multiple 3D Surface Matching[A]. ISPRS Workshop on Laser Scanning[C].Espoo, 2007.1-7.
[132]杨容浩,岑敏仪,张同刚,杨佳.融合遗传算法的最小二乘无控制DEM匹配[J].测绘科学.2011,36(1)：146-149.
[133]Robert Bergevin, Denis Laurendeau,Denis Poussart. Registering Range Views of Multipart Objects[J]. Computer Vision and Image Understanding.1995,61(1):1-16.
[134]Andrew O Finley.Ronald E McRoberts. Efficient K-nearest Neighbor Searches for Multi-source Forest Attribute Mapping[J]. Remote Sensing of Environment.2008,112(5):2203-2211.
[135]Jon Louis Bentley. Multidimensional Binary Search Trees Used for Associative Searching[J]. Communications of the ACM.1975,18(9): 509-516.
[136]Robert F Sproull. Refinements to nearest-neighbor searching in k-dimensional trees[J]. Algorithmica.1991,6(1-6):579-589.
[137]Timothee Jost. Fast Geometric Matching for Shape Registration[D]. University of Neuchatel, Doctor of Philosophy.2002.
[138]Michael Greenspan, Guy Godin,Jimmy Talbot. Acceleration of Binning Nearest Neighbour Methods[A]. Vision Interface 2000[C].Montreal,2000. National Research Council of Canada:337-344.
[139]Sunil Arya, David M Mount, Nathan S Netanyahu, Ruth Silverman, et al. An Optimal Algorithm for Approximate Nearest Neighbor Searching in Fixed Dimensions[J]. Journal of ACM.1998,45(6):891-923.
[140]Dmitry Chetverikov. Fast Neighborhood Search in Planar Point Sets[J]. Pattern Recogniton Letters.1991,12(7):409-412.
[141]王小平,曹立明.遗传算法——理论、应用与软件实现[M].西安交通大学出版社,2003.
[142]杨容浩,岑敏仪,张同刚,杨佳.遗传算法和最小二乘匹配相结合的DEM匹配方法[J].水土保持通报.2010,30(3)：128-133.
[143]莫鸿强.遗传算法搜索能力和编码方式研究[D].华南理工大学,工学博士.2001.
[144]朱灿.实数编码遗传算法机理分析及算法改进研究[D].中南大学,工学博士.2009.
[145]姜昌华.遗传算法在物流系统优化中的应用研究[D].华东师范大学,工学博士.2007.
[146]雷英杰,张善文,李续武,周创明.MATLAB遗传算法工具箱及应用[M].西安电子科技大学出版社,2006.
[147]Gunter Rudolph. Convergence Analysis of Canonical Genetic Algorithm[J]. IEEE Transactions on Neural Networks.1994,5(1): 96-101.
[148]陈宝林.最优化理论与算法[M].清华大学出版社,2005：282-328.