海陆地理空间矢量数据融合技术研究
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摘要
地理空间数据的获取在GIS工程里占有很重要的地位,同一地区的空间数据往往被不同部门重复采集,这不仅造成了人力、财力的巨大浪费,还引发了空间数据的二义性等问题,给GIS部门之间的数据共享和数据集成带来极大困难。解决这一问题最有效的方法是多源地理空间数据融合技术。二十一世纪是海洋的世纪,为满足我国海洋开发及沿海城市信息化建设的需求,本文围绕海陆地理空间矢量数据的融合技术,分别针对不同来源的海图、陆图在坐标、投影方式、几何数据、要素编码等方面存在的差异及其融合结果的不确定性等问题进行了研究。主要的研究内容包括：
总结多源地理空间数据融合技术的研究内容及处理过程,讨论在实施多源地理空间数据融合前要解决的问题,并就海陆地理空间矢量数据融合的坐标系统一、投影方式统一等问题给出具体的实现过程。这些是研究海陆地理空间矢量数据融合的前提。
同一地物在不同来源的地图上通常存在着差异,其识别或匹配对于多源地理空间数据融合来说很关键。借鉴空间相似性理论,基于人眼综合已有信息来识别同名实体的思想,本文提出基于空间相似性的实体匹配算法。该算法将实体作为一个整体看待,采用加权平均法来综合实体的位置、形状等特征的相似度,各指标权重依据视觉原理和人眼识别图形的特点来确定,进而根据获得的总相似度大小确定匹配实体。其中面实体的匹配,其指标计算引入了计算机视觉和模式识别中的方法。实验结果表明该算法能得到与人眼判断一致的结果,具有良好的稳定性和可靠性,且与其它算法比较,精度与召回率有明显提高。这是解决几何数据融合的基础。
在同名实体匹配的基础上,为解决同名地物表达不一致的问题,综合不同来源数据的点位精度差异的影响,提出一种基于多评价因素的要素合并变换算法。分析确定影响合并变换的三大主要评价因素,并将其综合来确定要素的可信度,进而对要素位置进行加权平均来获得合并变换后的位置。结合海陆图的部分要素对该算法进行检验的结果表明,提高了要素的空间位置合并变换质量。这是解决几何数据融合的关键。
研究多源地理空间数据融合中要素的编码融合问题。在阐述要素分类编码的原则和方法的基础上,提出融合的要素分类编码的原则和步骤,在此原则指导下,分析海陆图要素编码的差异,并解决编码融合过程中的要素层转换、同名要素统一编码等关键问题,实现海陆图要素编码的融合。
数据融合的目的是为了提高融合后的信息量,信息不确定度的降低就相当于信息量的增加。因此在论文的最后,就多源地理空间矢量数据融合结果的不确定性进行分析。剖析矢量数据不确定性产生的各种原因,及其不确定度传播定律,在此基础上建立单源数据的不确定性模型,并通过多源矢量空间数据不确定性的联合模型建立数据源的不确定性与最终融合结果中不确定性的相互关系,以此来评定多源地理空间矢量数据融合质量。
The gathering of geospatial data is very important in GIS applications. The same spatial data is sometimes colledted by different departments, which will cause the waste of human and financial resources, and brings data ambiguity. These problems bring many difficulties to data share and data integration between different GIS departments. An effective way to solve this problem is the geospatial data conflation technique. The 21th century is the ocean's century. To satisfy the need of coastal economy development and coastal city information, conflation of geospatial vector data from a sea chart and a topographic map is studied in this paper. It solves the differences in coordinate systems, projections, geometries, codings etc and uncertainty of conflating results.
General process of geospatial data conflation from multi-sources is summaried. Then the problems to be solved before putting geospatial data conflation into practice are discussed. And the process of uniting coordinate systems and projections of a sea chart and a topographic map is presented. These are the preconduction of the research on geospatial data conflation from multi-sources.
Disparities of features that represent the same real world entity from different sources usually occur, thus their identification or matching is crutial to map conflation. Based on the spatial similarity theory and motivated by the idea of identifying the same entity through integrating known information by eyes, an entity matching algorithm is proposed in this paper. Regarding the entity as a whole, the total similarity is obtained by integrating positional similarity, shape similarity etc with a weighted average algorithm. And the weights are obtained based on vision theory and the characters of identifying graphics by eyes. Then the matching entities are obtained according to the maximum total similarity. Test results are consistent with human intuition, which show the stability and reliability of the proposed algorithm. Compared with other algorithms, precision and recall of the proposed algorithm are obviously improved. This is the base of solving the geometry conflation.
Based on the matching of same entity, in order to solve their conflict and to synthesize the influence of elements precision on different source maps, an element adjusting algorithm based on multi-evaluation factors is proposed. Three primary evaluation factors are analyzed, and the element reliability is gained by integrating the three factors. Then the adjusted position is obtained with a weighted average algorithm. Some same elements from a sea chart and a topographic map are utilized to test the proposed algorithm. The result shows that the quality of areal element adjusting is improved. This is crucial in solving the geometry conflation.
Coding conflation is also studied in this paper. Based on the explanation of principles and methods of element classification and coding, the principles and steps of conflated element classification and coding are presented. Guided by these principles, the differences of element coding from a sea chart and a topographic map are analyzed. Some key problems such as element layer transformation and the same element coding uniting are solved, and the coflation of coding is realized.
The aim of geospatial data conflation is to improve the information content after conflation. The decrease of information uncertainty means the incerease of information content. Therefore, in the final chapter, the uncertainty of the conflation results is analyzed. The reasons that causing vector data uncertainty and the law of spreading uncertainty are introduced. Then the model of data uncertainty from single source is founded. And the relation of uncertainty from single source and from conflated results is constructed accoding to the unite model of uncertainty from multi-sources. This can be used to evaluate the quality of geospatial data conflation.
总结多源地理空间数据融合技术的研究内容及处理过程,讨论在实施多源地理空间数据融合前要解决的问题,并就海陆地理空间矢量数据融合的坐标系统一、投影方式统一等问题给出具体的实现过程。这些是研究海陆地理空间矢量数据融合的前提。
同一地物在不同来源的地图上通常存在着差异,其识别或匹配对于多源地理空间数据融合来说很关键。借鉴空间相似性理论,基于人眼综合已有信息来识别同名实体的思想,本文提出基于空间相似性的实体匹配算法。该算法将实体作为一个整体看待,采用加权平均法来综合实体的位置、形状等特征的相似度,各指标权重依据视觉原理和人眼识别图形的特点来确定,进而根据获得的总相似度大小确定匹配实体。其中面实体的匹配,其指标计算引入了计算机视觉和模式识别中的方法。实验结果表明该算法能得到与人眼判断一致的结果,具有良好的稳定性和可靠性,且与其它算法比较,精度与召回率有明显提高。这是解决几何数据融合的基础。
在同名实体匹配的基础上,为解决同名地物表达不一致的问题,综合不同来源数据的点位精度差异的影响,提出一种基于多评价因素的要素合并变换算法。分析确定影响合并变换的三大主要评价因素,并将其综合来确定要素的可信度,进而对要素位置进行加权平均来获得合并变换后的位置。结合海陆图的部分要素对该算法进行检验的结果表明,提高了要素的空间位置合并变换质量。这是解决几何数据融合的关键。
研究多源地理空间数据融合中要素的编码融合问题。在阐述要素分类编码的原则和方法的基础上,提出融合的要素分类编码的原则和步骤,在此原则指导下,分析海陆图要素编码的差异,并解决编码融合过程中的要素层转换、同名要素统一编码等关键问题,实现海陆图要素编码的融合。
数据融合的目的是为了提高融合后的信息量,信息不确定度的降低就相当于信息量的增加。因此在论文的最后,就多源地理空间矢量数据融合结果的不确定性进行分析。剖析矢量数据不确定性产生的各种原因,及其不确定度传播定律,在此基础上建立单源数据的不确定性模型,并通过多源矢量空间数据不确定性的联合模型建立数据源的不确定性与最终融合结果中不确定性的相互关系,以此来评定多源地理空间矢量数据融合质量。
The gathering of geospatial data is very important in GIS applications. The same spatial data is sometimes colledted by different departments, which will cause the waste of human and financial resources, and brings data ambiguity. These problems bring many difficulties to data share and data integration between different GIS departments. An effective way to solve this problem is the geospatial data conflation technique. The 21th century is the ocean's century. To satisfy the need of coastal economy development and coastal city information, conflation of geospatial vector data from a sea chart and a topographic map is studied in this paper. It solves the differences in coordinate systems, projections, geometries, codings etc and uncertainty of conflating results.
General process of geospatial data conflation from multi-sources is summaried. Then the problems to be solved before putting geospatial data conflation into practice are discussed. And the process of uniting coordinate systems and projections of a sea chart and a topographic map is presented. These are the preconduction of the research on geospatial data conflation from multi-sources.
Disparities of features that represent the same real world entity from different sources usually occur, thus their identification or matching is crutial to map conflation. Based on the spatial similarity theory and motivated by the idea of identifying the same entity through integrating known information by eyes, an entity matching algorithm is proposed in this paper. Regarding the entity as a whole, the total similarity is obtained by integrating positional similarity, shape similarity etc with a weighted average algorithm. And the weights are obtained based on vision theory and the characters of identifying graphics by eyes. Then the matching entities are obtained according to the maximum total similarity. Test results are consistent with human intuition, which show the stability and reliability of the proposed algorithm. Compared with other algorithms, precision and recall of the proposed algorithm are obviously improved. This is the base of solving the geometry conflation.
Based on the matching of same entity, in order to solve their conflict and to synthesize the influence of elements precision on different source maps, an element adjusting algorithm based on multi-evaluation factors is proposed. Three primary evaluation factors are analyzed, and the element reliability is gained by integrating the three factors. Then the adjusted position is obtained with a weighted average algorithm. Some same elements from a sea chart and a topographic map are utilized to test the proposed algorithm. The result shows that the quality of areal element adjusting is improved. This is crucial in solving the geometry conflation.
Coding conflation is also studied in this paper. Based on the explanation of principles and methods of element classification and coding, the principles and steps of conflated element classification and coding are presented. Guided by these principles, the differences of element coding from a sea chart and a topographic map are analyzed. Some key problems such as element layer transformation and the same element coding uniting are solved, and the coflation of coding is realized.
The aim of geospatial data conflation is to improve the information content after conflation. The decrease of information uncertainty means the incerease of information content. Therefore, in the final chapter, the uncertainty of the conflation results is analyzed. The reasons that causing vector data uncertainty and the law of spreading uncertainty are introduced. Then the model of data uncertainty from single source is founded. And the relation of uncertainty from single source and from conflated results is constructed accoding to the unite model of uncertainty from multi-sources. This can be used to evaluate the quality of geospatial data conflation.
引文
[1]邬伦,刘瑜,张晶等.地理信息系统—原理、方法和应用[M].北京：科学出版社,2002
[2]崔铁军,郭黎.多源地理空间矢量数据集成与融合方法探讨[J].测绘科学技术学报.2007,24(1)：1-4页
[3]BISHR Y. Overcoming the semantic and other barriers to GIS interoperability [J]. Geographical Information Science.1998,12(4): 299-314P
[4]B. Kovalerchuk, M. Kovalerchuk, W Sumner etc. Image conflation and change detection using area ratios[C]. SPIE Defense and Security Symposium. Orlando,2005:418-429P
[5]Park J. Schema integration methodology and toolkit for heterogeneous and distributed geographic databases [OL]. (2001 August) [2008-11-01] http://misrc.umn.edu/workingpapers/abstracts/0131.aspx
[6]郭黎.空间矢量数据融合问题的研究[D].郑州：解放军信息工程大学,2003-
[7]Maria L C. Some Basic Mathematical Constraints for the Geometric Conflation Problem[C]. Proceedings of Accuracy 2006 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences. Lisbon,2006:264-274P
[8]Hoseok Kang. Geometrically and Topographically Consistent Map Conflation for Federal and Local Government [J]. Journal of the Korean Geographical Society.2004,39(5):804-818P
[9]张桥平.地图数据库实体匹配与合并技术研究[D].武汉：武汉大学,2002
[10]王家耀,钱海忠.制图综合知识及其应用[J].武汉大学学报(信息科学版).2006,31(5):382-386页
[11]Li B., F. T. Fonseca. TDD-A Comprehensive Model for Qualitative Spatial Similarity Assessment [J]. Spatial Cognition and Computation. 2006,6(1):31-62页
[12]陆守一.地理信息系统[M].北京：高等教育出版社,2004
[13]Saalfeld A. Automated Map Conflation [D]. Washington D.C:University of Maryland,1993
[14]Cobb M., Chung M., Foley H. A Rule-based Approach for the conflation of Attributed Vector Data[J]. GeoInformatica,1998,2(1):7-35P
[15]Saalfeld A. Spatial Data Acquisition and Integration [DB/OL]. http://www.ncgia.ucsb.edu/other/ucgis/reserearch_priorities/old3%20%28 7-07-97%29/acquisition.html.1996
[16]Goodchild M. F. Conflation:combining geographical information [DB/OL]. http://www.ncgia.ucsb.edu/research/ucgis/proposal.html.1996
[17]Walter V., and Fritsh D. Matching Spatial Data Sets:A Statical Approach[J]. International Journal of Geographical Information Systems, 1999,13(5):445-473P
[18]Gillman, D. Triangulations for rubber sheeting[C]. Proceedings of AutoCarto 7 ASPRS/ACSM. Washington D.C.,1985:191-199P
[19]Gabay Y., Doytsher Y. Automatic adjustment of line maps[C]. Proceedings of the GIS/LIS'94 Annual Convention. Phoenix,1994: 333-341P
[20]Martin M B, Bell A E. New image compression techniques using multiwavelet and multiwavelet packets[J]. IEEE Transactions on Image Processing.2001,10(4):500-510P-
[21]Cotronei M, Lazzaro D, Montefusco L B. Image compression through embedded multiwavelet transform coding[J]. IEEE Transactions on Image Processing.2000,9(2):184-189P
[22]Simone G, Farina A, Morabito F C et al. Image Fusion Techniques for Remote Sensing Applications [J]. Information Fusion.2002,13(3):3-15P
[23]Sarmna V S. Multisensor data fusion and decision for airborne target identification [J]. IEEE Transaction.2001,21(5):1224-1230P
[24]Edwards D., Simpson J. Integration and access of multi-source vector data [C]. Proceedings of the Joint International Symposium on Geospatial Theory, Processing and Applications. Ottawa,2002:17-19P
[25]Ahmadyfard. Object recognition by region matching using relaxation with relational constraints [D]. Surrey:University of Surrey,2003
[26]吴孟泉,宋晓东,崔伟宏.基于本体的异构空间数据的集成研究[J].武汉大学学报(信息科学版).2007,32(10)：915-918页
[27]Yong Du. Satellite image fusion with multi-scale wavelet analysis for marine applications[J]. Can. J. Remote Sensing.2003,29(1):14-23P
[28]Dave B., Martin D, Djun K, etc. GIS Conflation using Open Source Tools [OL]. [2007-11-01] http://www. jump-project. org/assets/JUMP_ Conflation_Whitepaper.pdf
[29]Lupien A., Moreland W. A General Approach to Map Conflation[C]. Proceedings of 8th International Symposium on Computer Assisted Cartography (AutoCarto 8). Baltimore,1987:630-639P
[30]Lemarie C, Raynal L. Geographic Data Matching:First Investigations for a Generic Tool[A]. Proceedings of the GIS/LIS'96 annual Convention[C]. Denver,1996:333-341P
[31]Haunert J H. Link Based Conflation of Geographic Datasets[C].8th ICA workshop on Generalisation and Multiple Representation. A Coruna,2005: 1-7P
[32]Nation Technology Alliance, Accelerating Conflation Capability for the US Government [OL]. (2005-01-24)[2007-08-08] http://www.nta.org/ docs/AcceleratingConflation.pdf
[33]GIS/Trans Ltd. Comprehensive GIS Conflation. GIS/Trans Ltd. White Paper[OL]. (1995)[2007-08-08] http://www. gistrans.com/pub/cf_whipr. pdf
[34]GIS/Trans Ltd. Transportation Model-GIS Data Integration (Case Study: Southern California Association of Governments (SCAG))[OL]. (1996) [2007-08-08] http://www.gistrans.com/
[35]ESEA Inc. Conflation FAQ [OL]. [2007-08-08] http://www.conflation. com/conflati.htm
[36]ESEA Inc. ESEA Service [OL]. [2007-08-08] http://www. conflation.com /servOl.htm
[37]Intergraph Co Ltd. Automated merging of features and entire datasets [OL]. [2007-08-08] http://www.intergraph.com/gis/dcs/dynamo/dynam o7.pdf
[38]GDT. GDT Conflation Technology [OL]. [2007-08-08] http://www. tenlinks. com/mapgis/articles/features/articles/071801 conflation.htm
[39]Martin D. JCS Conflation Suite Technical Report [OL]. (2003) [2007-08-08]. http;//www.vividsolutions.com/jcs
[40]何建邦,柯正谊,陈常松等.空间信息学及其应用-RS,GPS,GIS及其集成[M].武汉：武汉测绘科技大学出版社,1998
[41]李德仁,龚健雅,张桥平.论地图数据库合并技术[J].测绘科学.2004,29(1)：1-4页
[42]邓愫愫.数字地图合并的平差处理原理与方法[D].上海：同济大学,2007
[43]DEVOGELE T, PARENT C, SPACCAPIETRA S. On Spatial Database Integration [J]. International Journal of Geographical Information Science. 1998,12(4):335-352P
[44]Saalfeld A. Conflation:Automated Map Compilation. International Journal of Geographical Information Systems [J],1988,2(3):217-228P
[45]XIONG D. A Three-stage Computational Approach to Network Matching [J]. Transportation Research, Part C.2000,8(1-6):71-89P
[46]Stock K., Pullar D. Identifying Semantically Similar Elements in Heterogeneous Spatial Databases Using Predicate Logic Expressions [C]. Interoperating Geographic Information Systems:Second International Conference, INTEROP'99. Zurich,1999:231-252P
[47]Doytsher Y, Filin S, Ezra E. Transformation of Datasets in a Linear-based Map Conflation Framework [J]. Surveying and Land Information Systems. 2001,61(3):159-169P
[48]Filin S, Doytsher Y. A Linear Mapping Approach to Map Conflation: Matching of Polylines [J]. Surveying and Land Information Systems. 1999,59(2):107-114P
[49]Filin S, Doytsher. A Linear Conflation Approach for the Integration of Photogrammetric Information and GIS Data [J]. International archives of photogrammetry and remote sensing.2000,33:282-288P
[50]Kang H. Analytical Conflation of Spatial Data from Municipal and Federal Government Agencies [D]. Ohio State:Ohio State University. 2002
[51]Deng Susu, Tong Xiaohua. A New Least Squares Adjustment Method for Map Conflation [C]. Geoinformatics 2006:Geospatial Information Science. Proc. of SPIE.2006,6420:64201B1-64201B12
[52]胡圣武,王新洲,王宏涛.空间数据融合的研究现状及其分析[A].“数字矿业城市、数字矿山”建设信息技术学术研讨会[C].泰安,2006：36-39页
[53]胡圣武,张卷美,王新洲等.空间数据融合的基本框架[J].测绘科学.2007,32(3)：175-177页
[54]闾国年,张书亮,龚敏霞.地理信息系统集成原理与方法[M].北京：科学 出版社,2005
[55]Beeri C, Kanza Y, Safra E et al. Object Fusion in Geographic Information Systems [A]. In:Proceedings of the Thirtieth International Conference on Very Large Data Bases[C]. Toronto,2004:816-827P
[56]Fedotov G A. Information fusion for turbulence measurements in hydrophysical applications [A]. Proceedings of 4th Annual Conference on Information Fusion[C]. Montreal,2001:3-9P
[57]Shuxin Yuan, Chuang Tao. Development of Conflation Components [OL]. (1999).http://www.umich.edu/-iinet/chinadata/geoim99/proceedings/yuan shuxin.pdf
[58]Goodchild M. Conflation:Combining GIS Sources [OL]. (1998) http:// www.ncgia.ucsb.edu/research/ucgis/proposals/conflation.html
[59]J P de Knecht, J G M Schavemaker, M J T Reinders et al. Utility Map Reconstruction[J]. International Journal of Geographical Information Science.2001,15(1):7-26P
[60]彭煜玮,彭智勇.空间数据融合技术的研究[J].计算机工程.2007,33(18)：51-52页
[61]H Kang. Spatial Data Integration:A Case Study of Map Conflation with Census Bureau and Local Government Data [OL]. (2001 June) [2008-10-01]http://www.cobblestoneconcepts.com/ucgis2summer/kang/kang_m ain.htm
[62]Chen C C, Thakkar S, Knoblok C A et al. Automatically Annotating and Integrating Spatial Datasets[C]. In Proceedings of the International Symposium on Spatial and Temporal Datasets, Santorini Island, Greece, 2003
[63]刘志勇.城市地图数据库合并中的面实体匹配方法研究[D].南京：河海大学,2006
[64]刘海砚,孙群,肖强等.数字地图制图中多源数据(资料)的综合应用[J].测绘科学技术学报.2006,23(3)：161-16页
[65]吴立新,史文中.地理信息系统原理与算法[M].北京：科学出版社,2003
[66]康志军,郭小亮,张东升.测量与GIS常用坐标系及其转换[J].科技情报开发与经济.2007,17(8)：139-141页
[67]杜华.GIS中电子地图坐标系的转换研究与实现[D].贵阳：贵州大学,2007
[68]黄梦龙.栅格地图投影变换实验系统的设计与实现[D].武汉：武汉大 学，2005
[69]张宏,温永宁,刘爱利等.地理信息系统算法基础[M].北京：科学出版社,2006
[70]孙达,蒲英霞.地图投影[M].南京：南京大学出版社,2005
[71]韩雪培,廖帮固.海岸带数据集成中的空间坐标转换方法研究[J].武汉大学学报·信息科学版.2004,29(10)：933-936页
[72]柳光魁,赵永强,张守忱等.北京54和西安80坐标系转换方法及精度分析[J].测绘与空间地理信息.2007,30(2)：138-139页
[73]Volz S. Management and Conflation of Multiple Representations within an Open Federation Platform[WS/OL]. (2006-10-03). http://drops. dagstuhl. de/opus/volltexte/2006/588
[74]Rosen B., Saalfeld A. Matchi Criteria for Automatic Alignment[C]. Proceedings of AUTOCARTO 7. Washington D.C.1985:456-462P
[75]Walter V., Fritsh D. Matching Techniques for Road Network Data in Different Data Models[A]. In Proceedings of the 28th International Symposium on Automotic Technology and Automation Limited[C].1995: 663-640P
[76]Uitermark H. Ontology-based geographic data set integration [D]. Enschede:University of Twente.2001
[77]Gabay Y, Doytsher Y Automatic Adjustment of Line Maps [A]. Proceedings of the GIS/LIS'94 Annual Convention[C]. Phoenix,1994: 333-341P
[78]Mantel D, Lipeck U. Matching Cartographic Objects in Spatial Databases. Proceedings of the XXth ISPRS Congress, Comm.IV, Istanbul,2004: 172-176P
[79]Stigmar H. Matching Route Data and Topographic Data in a Real-Time Environment. Proceedings of the 10th Scandinavian Research Conference on Geographical Information Science (SCANGIS)'05, Sweden,2005: 89-107P
[80]Zhang M, Shi W, Meng L. A Generic Matching Algorithm for Line Networks of Different Resolutions [C].8th ICA Workshop on Generalisation and Multiple Representation, A Coruna,2005:1-8P
[81]JUMP. Homepage of the Java Unified Mapping Platform [OL]. http://www.jump-project.org/accessed 2006
[82]童小华,邓愫愫,史文中.基于概率的地图实体匹配方法[J].测绘学报. 2007,36(2):210-217页
[83]S. Yuan, C. Tao. Development of conflation components [A]. Proceedings of Geoinformatics'99 Conference[C]. Ann Arbor,1999:1-13P
[84]丁虹.空间相似性理论与计算模型的研究[D].武汉：武汉大学,2004
[85]A Holt, G.L.Benwell. Using Spatial Similarity for Exploratory Spatial Data Analysis:Some Directions[C]. Proceedings of the 2nd Internation Conference on GeoComputation. Dunedin,1997:279-288P
[86]A Holt. Spatial Similarity and GIS:the grouping of spatial kinds[C]. SIRC99-The 11th Annual Colloquium of the Spatial Information Research Centre University of Otago. Dunedin,1999:241-250P
[87]Marco Aiello. Computing Spatial Similarity by games [J]. Lecture Notes In Computer Science.2001,2175:99-110P
[88]Marinos K, Margarita K. A method for the formalization and integration of geographical categorizations [J]. International Journal of Geographical Information Science.2002,16(5):439-453P
[89]Hagen A multi-method assessment of map similarity[C]. Proceeding of 5th AGILE Conference on Geographic Information Science. Spain.2002: 171-182P
[90]Hardy P. Field Data Collection with mobile GIS:Dependencies between semantics and data quality [J]. GeoInformatic.2002,6(4):363-380P
[91]ASHOK SAMAL, SHARAD SETH, KEVIN CUETO. A feature-based approach to conflation of geospatial sources [J]. International Journal of Geographical Information Science.2004,18(5):459-489P
[92]Wentz E.A. Shape Analysis in GIS [C]. Proc of ACSM/ASPRS. Seattle, 1997:204-213P
[93]FOLEY H. A Multiple Criteria Based Approach to Performing Conflation in Geographical Information Systems [D]. New Orleans:Tulane University, 1997
[94]刘宏申,秦锋.确定轮廓形状匹配中形状描述函数的方法[J].华中科技大学学报(自然科学版).2005,33(4)：13-16页
[95]刘宏申.一种基于像素标识的笔画细化新方法[J].华中科技大学学报(自然科学版).2002,30(3)：80-82页
[96]Wentz E.A. Shape Analysis in GIS [C]. Proc of ACSM/ASPRS. Seattle, 1997:204-213P
[97]BERRI C, KANZA Y, SAFRA E, SAGIV Y Object Fusion in Geographic Information Systems[C]. Proceedings of the 30th VLDB Conference. Toronto,2004:816-827P
[98]邵春丽,胡鹏,黄承义等.DELAUNAY三角网的算法详述及其应用发展前景[J].测绘科学.2004,29(6)：68-71页
[99]刘颖.空间图形的表达、识别与综合[D].郑州：解放军信息工程大学,2005
[100]韦文杰,周振红,彭记永.一种改进的Delaunay三角网生长算法[J].气象与环境科学.2008,31(2)：80-82页
[101]李荣钧.模糊多准则决策理论与应用[M].北京：科学出版社,2002
[102]刘少华,程朋根,史文中.约束Delaunay三角网生成算法研究[J].测绘通报.2004,(3)：4-7页
[103]Devogele T. A New Merging Process for Data Integration Based on the Discrete Frechet distance [A]. In:Proceedings of the 10th International Symposium on Spatial Data Handling (SDH) [C]. Ottawa,2002:167-181P
[104]Pelletier S. Computing the Frechet distance between two polygonal curves [OL]. (2002 Fall).http://cgm.cs.mcgill.ca/-athens/cs 507/Projects/2002/ StephanePelletier/
[105]Thomas E, Heikki M. Computing discrete Frechet distance [R]. Vienna: Technical University of Vienna,1994
[106]Helmut Alt, Michael Godau. Computing the Frechet distance between two polygonal curves [J]. Int. J. Comput. Geometry Appl.5.1995:75-91P
[107]H. Alt, C. Knauer, C. Wenk. Matching polygonal curves with respect to the Frechet distance[C]. Proc.18th Int. Symp. Theoretical Aspect of Computer Science(STACS), Dresden,2001:63-74P
[108]Borrough P. A. Principles of GIS for Land Resources Assessment [M]. Oxford:Oxford Publication.1987
[109]焦健,曾琪明.地理属性与图形属性耦合的信息编码模型[J].遥感学报.1998,2(4)：310-315页
[110]杜新锋.煤矿地质测量信息分类编码技术及其应用研究[D].西安：煤炭科学研究总院西安分院.2005
[111]王和根.GIS信息分类编码技术的基本方法[J].干旱区地理.1993,16(4)：85-90页
[112]高艳婷.数据融合技术在海陆联合地理信息系统中的研究与设计[D].哈尔滨：哈尔滨工程大学,2007
[113]崔铁军.空间数据特点及其数据模型设计[C].中国地理信息系统协会 2000年年会论文集.北京,2000
[114]承继成,郭华东,史文中.遥感数据的不确定性问题[M].北京：科学出版社,2004
[115]Couclelis H. Living with uncertainty:GIS and the limits of Geographic Knowledge[C]. In Accuracy 2002:5th international Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences. Melbourne,2002:281-289P
[116]Goodchild M, Hunter G. A simple positional accuracy measure for linear features [J]. International Journal of Geographical Information Systems. 1997, 11(3):299-306P
[117]Heuvelink G.B.M. Error propagation in quantitative spatial modeling: Application in Geographical Information Systems [D]. University of Utrecht.1993
[118]Goodchild M F. Geographical Information Science [J]. International Journal of Geographical Information System,1992(6):31-46P
[119]Goodchild M F, Dubuc O. A Model of Error for Choropleth Maps with Applications to Geographic Information Systems[C]. Proceedings of AUTOCARTOS. ASPRS/ACSM, Falls Church.1987:65-74P
[120]Abler R F. The national Science Foundatin Center for Geographical Information and analysis [J]. Int. GISs.1987,1:303-326P
[121]汤仲安.矢量GIS线状实体等概率密度误差模型[D].武汉：武汉大学,2004
[122]郭同德.GIS中空间数据位置不确定性的模型与实验研究[D].郑州：中国人民解放军信息工程大学,2004
[123]蓝悦明.空间位置数据不确定性问题的若干理论研究[D].武汉：武汉大学,2003
[124]刘大杰,刘春.GIS空间数据不确定性与质量控制的研究现状[J].测绘工程.2001,10(1)：6-10页
[125]Chrisman N R. A Theory of Cartographic Error and Its Measurement in Digital Databases[C]. Proceedings of Auto-Carto 5. Arlington,1982:159- 169P
[126]Caspary W, Scheuring R. Error-bands as measures of geometrical accuracy[C]. In:Proceedings of EGIS'92. Munich,1992:226-233P
[127]范爱民,郭达志.误差熵不确定带模型[J].测绘学报.2001,30(1)：48-53页
[128]刘文宝.GIS空间数据的不确定性理论[D].武汉：武汉测绘科技大学,1995
[129]Tong Xiaohua, Liu Dajie. Probability density function and estimation for error of digitized map coordinates in GIS [J]. Jounal of Central South University of Technology (English Edition).2004,11(1):69-74P
[130]Lloyd R. Assessment of simulated cognitive maps:The influence of prior knowledge from cartographic maps [J]. Cartography and Geographic Information Science.2005,32(3):161-179P
[131]Sholokhov A.V, Liventsev V.A. Evaluation of the reliability of determining coordinates in navigation systems corrected for digital road maps [J]. Measurement Techniques.2007,50(5):509-515P
[132]H Todd Mowrer, Russell G Congalton. Quantifying Spatial Uncertainty in Nature Resource:Theory and Application for GIS and Remote Sensing [M]. Sleeping Beer Press.2000
[133]邬伦,于海龙,高振纪.GIS不确定性框架体系与数据不确定性研究方法[J].地理学与国土研究.2002,18(4)：1-5页
[134]Jianxin Gao. Uncertainty in GIS spatial data[C]. Geoinformatics 2006: Geospatial Information Science. Wuhan,2006:642016-1-642016-5
[135]李大军.基于信息熵的空间数据位置不确定性模型的研究[D].武汉：武汉大学,2003
[136]Zadeh L.A. Fuzzy sets as a basis for a theory of possibility [J]. Fuzzy sets and systems.1978,1(1):338-351P
[137]Ronald R.Y. On the entropy of fuzzy measures [J]. IEEE Transactions on Fuzzy Systems.1983,8(4):453-461P
[138]徐建华.现代地理学中的数学方法[M].北京：高等教育出版社,2004
[139]王中宇.测量不确定度的非统计理论[M].北京：国防工业出版社,2000
[140]Bonifazi C. A model for the time uncertainty measurements in the Auger surface detector array[J]. A stroparticle Physics.2008,28(6):523-528P
[141]李爽,姚静.基于分形的DEM数据不确定性研究[M].北京：科学出版社,2007
[142]史玉峰,史文中,靳奉祥.GIS中空间数据不确定性的混合熵模型研究[J].武汉大学学报·信息科学版.2006,31(1)：82-85页
[143]Bruin S. Heuvelink G.B.M, Brown J.D. Propagation of positional measurement errors to agricultural field boundaries and associated costs [J]. Computers and Electronics in Agriculture.2008,63(2):245-256P
[144]李金海.误差理论语测量不确定度评定[M].北京：中国计量出版社
[2]崔铁军,郭黎.多源地理空间矢量数据集成与融合方法探讨[J].测绘科学技术学报.2007,24(1)：1-4页
[3]BISHR Y. Overcoming the semantic and other barriers to GIS interoperability [J]. Geographical Information Science.1998,12(4): 299-314P
[4]B. Kovalerchuk, M. Kovalerchuk, W Sumner etc. Image conflation and change detection using area ratios[C]. SPIE Defense and Security Symposium. Orlando,2005:418-429P
[5]Park J. Schema integration methodology and toolkit for heterogeneous and distributed geographic databases [OL]. (2001 August) [2008-11-01] http://misrc.umn.edu/workingpapers/abstracts/0131.aspx
[6]郭黎.空间矢量数据融合问题的研究[D].郑州：解放军信息工程大学,2003-
[7]Maria L C. Some Basic Mathematical Constraints for the Geometric Conflation Problem[C]. Proceedings of Accuracy 2006 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences. Lisbon,2006:264-274P
[8]Hoseok Kang. Geometrically and Topographically Consistent Map Conflation for Federal and Local Government [J]. Journal of the Korean Geographical Society.2004,39(5):804-818P
[9]张桥平.地图数据库实体匹配与合并技术研究[D].武汉：武汉大学,2002
[10]王家耀,钱海忠.制图综合知识及其应用[J].武汉大学学报(信息科学版).2006,31(5):382-386页
[11]Li B., F. T. Fonseca. TDD-A Comprehensive Model for Qualitative Spatial Similarity Assessment [J]. Spatial Cognition and Computation. 2006,6(1):31-62页
[12]陆守一.地理信息系统[M].北京：高等教育出版社,2004
[13]Saalfeld A. Automated Map Conflation [D]. Washington D.C:University of Maryland,1993
[14]Cobb M., Chung M., Foley H. A Rule-based Approach for the conflation of Attributed Vector Data[J]. GeoInformatica,1998,2(1):7-35P
[15]Saalfeld A. Spatial Data Acquisition and Integration [DB/OL]. http://www.ncgia.ucsb.edu/other/ucgis/reserearch_priorities/old3%20%28 7-07-97%29/acquisition.html.1996
[16]Goodchild M. F. Conflation:combining geographical information [DB/OL]. http://www.ncgia.ucsb.edu/research/ucgis/proposal.html.1996
[17]Walter V., and Fritsh D. Matching Spatial Data Sets:A Statical Approach[J]. International Journal of Geographical Information Systems, 1999,13(5):445-473P
[18]Gillman, D. Triangulations for rubber sheeting[C]. Proceedings of AutoCarto 7 ASPRS/ACSM. Washington D.C.,1985:191-199P
[19]Gabay Y., Doytsher Y. Automatic adjustment of line maps[C]. Proceedings of the GIS/LIS'94 Annual Convention. Phoenix,1994: 333-341P
[20]Martin M B, Bell A E. New image compression techniques using multiwavelet and multiwavelet packets[J]. IEEE Transactions on Image Processing.2001,10(4):500-510P-
[21]Cotronei M, Lazzaro D, Montefusco L B. Image compression through embedded multiwavelet transform coding[J]. IEEE Transactions on Image Processing.2000,9(2):184-189P
[22]Simone G, Farina A, Morabito F C et al. Image Fusion Techniques for Remote Sensing Applications [J]. Information Fusion.2002,13(3):3-15P
[23]Sarmna V S. Multisensor data fusion and decision for airborne target identification [J]. IEEE Transaction.2001,21(5):1224-1230P
[24]Edwards D., Simpson J. Integration and access of multi-source vector data [C]. Proceedings of the Joint International Symposium on Geospatial Theory, Processing and Applications. Ottawa,2002:17-19P
[25]Ahmadyfard. Object recognition by region matching using relaxation with relational constraints [D]. Surrey:University of Surrey,2003
[26]吴孟泉,宋晓东,崔伟宏.基于本体的异构空间数据的集成研究[J].武汉大学学报(信息科学版).2007,32(10)：915-918页
[27]Yong Du. Satellite image fusion with multi-scale wavelet analysis for marine applications[J]. Can. J. Remote Sensing.2003,29(1):14-23P
[28]Dave B., Martin D, Djun K, etc. GIS Conflation using Open Source Tools [OL]. [2007-11-01] http://www. jump-project. org/assets/JUMP_ Conflation_Whitepaper.pdf
[29]Lupien A., Moreland W. A General Approach to Map Conflation[C]. Proceedings of 8th International Symposium on Computer Assisted Cartography (AutoCarto 8). Baltimore,1987:630-639P
[30]Lemarie C, Raynal L. Geographic Data Matching:First Investigations for a Generic Tool[A]. Proceedings of the GIS/LIS'96 annual Convention[C]. Denver,1996:333-341P
[31]Haunert J H. Link Based Conflation of Geographic Datasets[C].8th ICA workshop on Generalisation and Multiple Representation. A Coruna,2005: 1-7P
[32]Nation Technology Alliance, Accelerating Conflation Capability for the US Government [OL]. (2005-01-24)[2007-08-08] http://www.nta.org/ docs/AcceleratingConflation.pdf
[33]GIS/Trans Ltd. Comprehensive GIS Conflation. GIS/Trans Ltd. White Paper[OL]. (1995)[2007-08-08] http://www. gistrans.com/pub/cf_whipr. pdf
[34]GIS/Trans Ltd. Transportation Model-GIS Data Integration (Case Study: Southern California Association of Governments (SCAG))[OL]. (1996) [2007-08-08] http://www.gistrans.com/
[35]ESEA Inc. Conflation FAQ [OL]. [2007-08-08] http://www.conflation. com/conflati.htm
[36]ESEA Inc. ESEA Service [OL]. [2007-08-08] http://www. conflation.com /servOl.htm
[37]Intergraph Co Ltd. Automated merging of features and entire datasets [OL]. [2007-08-08] http://www.intergraph.com/gis/dcs/dynamo/dynam o7.pdf
[38]GDT. GDT Conflation Technology [OL]. [2007-08-08] http://www. tenlinks. com/mapgis/articles/features/articles/071801 conflation.htm
[39]Martin D. JCS Conflation Suite Technical Report [OL]. (2003) [2007-08-08]. http;//www.vividsolutions.com/jcs
[40]何建邦,柯正谊,陈常松等.空间信息学及其应用-RS,GPS,GIS及其集成[M].武汉：武汉测绘科技大学出版社,1998
[41]李德仁,龚健雅,张桥平.论地图数据库合并技术[J].测绘科学.2004,29(1)：1-4页
[42]邓愫愫.数字地图合并的平差处理原理与方法[D].上海：同济大学,2007
[43]DEVOGELE T, PARENT C, SPACCAPIETRA S. On Spatial Database Integration [J]. International Journal of Geographical Information Science. 1998,12(4):335-352P
[44]Saalfeld A. Conflation:Automated Map Compilation. International Journal of Geographical Information Systems [J],1988,2(3):217-228P
[45]XIONG D. A Three-stage Computational Approach to Network Matching [J]. Transportation Research, Part C.2000,8(1-6):71-89P
[46]Stock K., Pullar D. Identifying Semantically Similar Elements in Heterogeneous Spatial Databases Using Predicate Logic Expressions [C]. Interoperating Geographic Information Systems:Second International Conference, INTEROP'99. Zurich,1999:231-252P
[47]Doytsher Y, Filin S, Ezra E. Transformation of Datasets in a Linear-based Map Conflation Framework [J]. Surveying and Land Information Systems. 2001,61(3):159-169P
[48]Filin S, Doytsher Y. A Linear Mapping Approach to Map Conflation: Matching of Polylines [J]. Surveying and Land Information Systems. 1999,59(2):107-114P
[49]Filin S, Doytsher. A Linear Conflation Approach for the Integration of Photogrammetric Information and GIS Data [J]. International archives of photogrammetry and remote sensing.2000,33:282-288P
[50]Kang H. Analytical Conflation of Spatial Data from Municipal and Federal Government Agencies [D]. Ohio State:Ohio State University. 2002
[51]Deng Susu, Tong Xiaohua. A New Least Squares Adjustment Method for Map Conflation [C]. Geoinformatics 2006:Geospatial Information Science. Proc. of SPIE.2006,6420:64201B1-64201B12
[52]胡圣武,王新洲,王宏涛.空间数据融合的研究现状及其分析[A].“数字矿业城市、数字矿山”建设信息技术学术研讨会[C].泰安,2006：36-39页
[53]胡圣武,张卷美,王新洲等.空间数据融合的基本框架[J].测绘科学.2007,32(3)：175-177页
[54]闾国年,张书亮,龚敏霞.地理信息系统集成原理与方法[M].北京：科学 出版社,2005
[55]Beeri C, Kanza Y, Safra E et al. Object Fusion in Geographic Information Systems [A]. In:Proceedings of the Thirtieth International Conference on Very Large Data Bases[C]. Toronto,2004:816-827P
[56]Fedotov G A. Information fusion for turbulence measurements in hydrophysical applications [A]. Proceedings of 4th Annual Conference on Information Fusion[C]. Montreal,2001:3-9P
[57]Shuxin Yuan, Chuang Tao. Development of Conflation Components [OL]. (1999).http://www.umich.edu/-iinet/chinadata/geoim99/proceedings/yuan shuxin.pdf
[58]Goodchild M. Conflation:Combining GIS Sources [OL]. (1998) http:// www.ncgia.ucsb.edu/research/ucgis/proposals/conflation.html
[59]J P de Knecht, J G M Schavemaker, M J T Reinders et al. Utility Map Reconstruction[J]. International Journal of Geographical Information Science.2001,15(1):7-26P
[60]彭煜玮,彭智勇.空间数据融合技术的研究[J].计算机工程.2007,33(18)：51-52页
[61]H Kang. Spatial Data Integration:A Case Study of Map Conflation with Census Bureau and Local Government Data [OL]. (2001 June) [2008-10-01]http://www.cobblestoneconcepts.com/ucgis2summer/kang/kang_m ain.htm
[62]Chen C C, Thakkar S, Knoblok C A et al. Automatically Annotating and Integrating Spatial Datasets[C]. In Proceedings of the International Symposium on Spatial and Temporal Datasets, Santorini Island, Greece, 2003
[63]刘志勇.城市地图数据库合并中的面实体匹配方法研究[D].南京：河海大学,2006
[64]刘海砚,孙群,肖强等.数字地图制图中多源数据(资料)的综合应用[J].测绘科学技术学报.2006,23(3)：161-16页
[65]吴立新,史文中.地理信息系统原理与算法[M].北京：科学出版社,2003
[66]康志军,郭小亮,张东升.测量与GIS常用坐标系及其转换[J].科技情报开发与经济.2007,17(8)：139-141页
[67]杜华.GIS中电子地图坐标系的转换研究与实现[D].贵阳：贵州大学,2007
[68]黄梦龙.栅格地图投影变换实验系统的设计与实现[D].武汉：武汉大 学，2005
[69]张宏,温永宁,刘爱利等.地理信息系统算法基础[M].北京：科学出版社,2006
[70]孙达,蒲英霞.地图投影[M].南京：南京大学出版社,2005
[71]韩雪培,廖帮固.海岸带数据集成中的空间坐标转换方法研究[J].武汉大学学报·信息科学版.2004,29(10)：933-936页
[72]柳光魁,赵永强,张守忱等.北京54和西安80坐标系转换方法及精度分析[J].测绘与空间地理信息.2007,30(2)：138-139页
[73]Volz S. Management and Conflation of Multiple Representations within an Open Federation Platform[WS/OL]. (2006-10-03). http://drops. dagstuhl. de/opus/volltexte/2006/588
[74]Rosen B., Saalfeld A. Matchi Criteria for Automatic Alignment[C]. Proceedings of AUTOCARTO 7. Washington D.C.1985:456-462P
[75]Walter V., Fritsh D. Matching Techniques for Road Network Data in Different Data Models[A]. In Proceedings of the 28th International Symposium on Automotic Technology and Automation Limited[C].1995: 663-640P
[76]Uitermark H. Ontology-based geographic data set integration [D]. Enschede:University of Twente.2001
[77]Gabay Y, Doytsher Y Automatic Adjustment of Line Maps [A]. Proceedings of the GIS/LIS'94 Annual Convention[C]. Phoenix,1994: 333-341P
[78]Mantel D, Lipeck U. Matching Cartographic Objects in Spatial Databases. Proceedings of the XXth ISPRS Congress, Comm.IV, Istanbul,2004: 172-176P
[79]Stigmar H. Matching Route Data and Topographic Data in a Real-Time Environment. Proceedings of the 10th Scandinavian Research Conference on Geographical Information Science (SCANGIS)'05, Sweden,2005: 89-107P
[80]Zhang M, Shi W, Meng L. A Generic Matching Algorithm for Line Networks of Different Resolutions [C].8th ICA Workshop on Generalisation and Multiple Representation, A Coruna,2005:1-8P
[81]JUMP. Homepage of the Java Unified Mapping Platform [OL]. http://www.jump-project.org/accessed 2006
[82]童小华,邓愫愫,史文中.基于概率的地图实体匹配方法[J].测绘学报. 2007,36(2):210-217页
[83]S. Yuan, C. Tao. Development of conflation components [A]. Proceedings of Geoinformatics'99 Conference[C]. Ann Arbor,1999:1-13P
[84]丁虹.空间相似性理论与计算模型的研究[D].武汉：武汉大学,2004
[85]A Holt, G.L.Benwell. Using Spatial Similarity for Exploratory Spatial Data Analysis:Some Directions[C]. Proceedings of the 2nd Internation Conference on GeoComputation. Dunedin,1997:279-288P
[86]A Holt. Spatial Similarity and GIS:the grouping of spatial kinds[C]. SIRC99-The 11th Annual Colloquium of the Spatial Information Research Centre University of Otago. Dunedin,1999:241-250P
[87]Marco Aiello. Computing Spatial Similarity by games [J]. Lecture Notes In Computer Science.2001,2175:99-110P
[88]Marinos K, Margarita K. A method for the formalization and integration of geographical categorizations [J]. International Journal of Geographical Information Science.2002,16(5):439-453P
[89]Hagen A multi-method assessment of map similarity[C]. Proceeding of 5th AGILE Conference on Geographic Information Science. Spain.2002: 171-182P
[90]Hardy P. Field Data Collection with mobile GIS:Dependencies between semantics and data quality [J]. GeoInformatic.2002,6(4):363-380P
[91]ASHOK SAMAL, SHARAD SETH, KEVIN CUETO. A feature-based approach to conflation of geospatial sources [J]. International Journal of Geographical Information Science.2004,18(5):459-489P
[92]Wentz E.A. Shape Analysis in GIS [C]. Proc of ACSM/ASPRS. Seattle, 1997:204-213P
[93]FOLEY H. A Multiple Criteria Based Approach to Performing Conflation in Geographical Information Systems [D]. New Orleans:Tulane University, 1997
[94]刘宏申,秦锋.确定轮廓形状匹配中形状描述函数的方法[J].华中科技大学学报(自然科学版).2005,33(4)：13-16页
[95]刘宏申.一种基于像素标识的笔画细化新方法[J].华中科技大学学报(自然科学版).2002,30(3)：80-82页
[96]Wentz E.A. Shape Analysis in GIS [C]. Proc of ACSM/ASPRS. Seattle, 1997:204-213P
[97]BERRI C, KANZA Y, SAFRA E, SAGIV Y Object Fusion in Geographic Information Systems[C]. Proceedings of the 30th VLDB Conference. Toronto,2004:816-827P
[98]邵春丽,胡鹏,黄承义等.DELAUNAY三角网的算法详述及其应用发展前景[J].测绘科学.2004,29(6)：68-71页
[99]刘颖.空间图形的表达、识别与综合[D].郑州：解放军信息工程大学,2005
[100]韦文杰,周振红,彭记永.一种改进的Delaunay三角网生长算法[J].气象与环境科学.2008,31(2)：80-82页
[101]李荣钧.模糊多准则决策理论与应用[M].北京：科学出版社,2002
[102]刘少华,程朋根,史文中.约束Delaunay三角网生成算法研究[J].测绘通报.2004,(3)：4-7页
[103]Devogele T. A New Merging Process for Data Integration Based on the Discrete Frechet distance [A]. In:Proceedings of the 10th International Symposium on Spatial Data Handling (SDH) [C]. Ottawa,2002:167-181P
[104]Pelletier S. Computing the Frechet distance between two polygonal curves [OL]. (2002 Fall).http://cgm.cs.mcgill.ca/-athens/cs 507/Projects/2002/ StephanePelletier/
[105]Thomas E, Heikki M. Computing discrete Frechet distance [R]. Vienna: Technical University of Vienna,1994
[106]Helmut Alt, Michael Godau. Computing the Frechet distance between two polygonal curves [J]. Int. J. Comput. Geometry Appl.5.1995:75-91P
[107]H. Alt, C. Knauer, C. Wenk. Matching polygonal curves with respect to the Frechet distance[C]. Proc.18th Int. Symp. Theoretical Aspect of Computer Science(STACS), Dresden,2001:63-74P
[108]Borrough P. A. Principles of GIS for Land Resources Assessment [M]. Oxford:Oxford Publication.1987
[109]焦健,曾琪明.地理属性与图形属性耦合的信息编码模型[J].遥感学报.1998,2(4)：310-315页
[110]杜新锋.煤矿地质测量信息分类编码技术及其应用研究[D].西安：煤炭科学研究总院西安分院.2005
[111]王和根.GIS信息分类编码技术的基本方法[J].干旱区地理.1993,16(4)：85-90页
[112]高艳婷.数据融合技术在海陆联合地理信息系统中的研究与设计[D].哈尔滨：哈尔滨工程大学,2007
[113]崔铁军.空间数据特点及其数据模型设计[C].中国地理信息系统协会 2000年年会论文集.北京,2000
[114]承继成,郭华东,史文中.遥感数据的不确定性问题[M].北京：科学出版社,2004
[115]Couclelis H. Living with uncertainty:GIS and the limits of Geographic Knowledge[C]. In Accuracy 2002:5th international Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences. Melbourne,2002:281-289P
[116]Goodchild M, Hunter G. A simple positional accuracy measure for linear features [J]. International Journal of Geographical Information Systems. 1997, 11(3):299-306P
[117]Heuvelink G.B.M. Error propagation in quantitative spatial modeling: Application in Geographical Information Systems [D]. University of Utrecht.1993
[118]Goodchild M F. Geographical Information Science [J]. International Journal of Geographical Information System,1992(6):31-46P
[119]Goodchild M F, Dubuc O. A Model of Error for Choropleth Maps with Applications to Geographic Information Systems[C]. Proceedings of AUTOCARTOS. ASPRS/ACSM, Falls Church.1987:65-74P
[120]Abler R F. The national Science Foundatin Center for Geographical Information and analysis [J]. Int. GISs.1987,1:303-326P
[121]汤仲安.矢量GIS线状实体等概率密度误差模型[D].武汉：武汉大学,2004
[122]郭同德.GIS中空间数据位置不确定性的模型与实验研究[D].郑州：中国人民解放军信息工程大学,2004
[123]蓝悦明.空间位置数据不确定性问题的若干理论研究[D].武汉：武汉大学,2003
[124]刘大杰,刘春.GIS空间数据不确定性与质量控制的研究现状[J].测绘工程.2001,10(1)：6-10页
[125]Chrisman N R. A Theory of Cartographic Error and Its Measurement in Digital Databases[C]. Proceedings of Auto-Carto 5. Arlington,1982:159- 169P
[126]Caspary W, Scheuring R. Error-bands as measures of geometrical accuracy[C]. In:Proceedings of EGIS'92. Munich,1992:226-233P
[127]范爱民,郭达志.误差熵不确定带模型[J].测绘学报.2001,30(1)：48-53页
[128]刘文宝.GIS空间数据的不确定性理论[D].武汉：武汉测绘科技大学,1995
[129]Tong Xiaohua, Liu Dajie. Probability density function and estimation for error of digitized map coordinates in GIS [J]. Jounal of Central South University of Technology (English Edition).2004,11(1):69-74P
[130]Lloyd R. Assessment of simulated cognitive maps:The influence of prior knowledge from cartographic maps [J]. Cartography and Geographic Information Science.2005,32(3):161-179P
[131]Sholokhov A.V, Liventsev V.A. Evaluation of the reliability of determining coordinates in navigation systems corrected for digital road maps [J]. Measurement Techniques.2007,50(5):509-515P
[132]H Todd Mowrer, Russell G Congalton. Quantifying Spatial Uncertainty in Nature Resource:Theory and Application for GIS and Remote Sensing [M]. Sleeping Beer Press.2000
[133]邬伦,于海龙,高振纪.GIS不确定性框架体系与数据不确定性研究方法[J].地理学与国土研究.2002,18(4)：1-5页
[134]Jianxin Gao. Uncertainty in GIS spatial data[C]. Geoinformatics 2006: Geospatial Information Science. Wuhan,2006:642016-1-642016-5
[135]李大军.基于信息熵的空间数据位置不确定性模型的研究[D].武汉：武汉大学,2003
[136]Zadeh L.A. Fuzzy sets as a basis for a theory of possibility [J]. Fuzzy sets and systems.1978,1(1):338-351P
[137]Ronald R.Y. On the entropy of fuzzy measures [J]. IEEE Transactions on Fuzzy Systems.1983,8(4):453-461P
[138]徐建华.现代地理学中的数学方法[M].北京：高等教育出版社,2004
[139]王中宇.测量不确定度的非统计理论[M].北京：国防工业出版社,2000
[140]Bonifazi C. A model for the time uncertainty measurements in the Auger surface detector array[J]. A stroparticle Physics.2008,28(6):523-528P
[141]李爽,姚静.基于分形的DEM数据不确定性研究[M].北京：科学出版社,2007
[142]史玉峰,史文中,靳奉祥.GIS中空间数据不确定性的混合熵模型研究[J].武汉大学学报·信息科学版.2006,31(1)：82-85页
[143]Bruin S. Heuvelink G.B.M, Brown J.D. Propagation of positional measurement errors to agricultural field boundaries and associated costs [J]. Computers and Electronics in Agriculture.2008,63(2):245-256P
[144]李金海.误差理论语测量不确定度评定[M].北京：中国计量出版社