测绘学报

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顾及多细节层次的三维R树索引扩展方法

龚俊1,朱庆2,张叶廷3,李晓明4,周东波5   

  1. 1. 江西师范大学
    2. 武汉大学 测绘遥感信息工程国家重点实验室
    3. 武汉大学
    4. 武汉大学测绘遥感信息工程国家重点实验室
    5. 立得空间信息技术有限公司
  • 收稿日期:2009-11-18 修回日期:2010-02-26 出版日期:2011-04-25 发布日期:2011-04-25
  • 通讯作者: 龚俊

An Efficient 3D R-tree Extension Method Concerned with Levels of Detail

  • Received:2009-11-18 Revised:2010-02-26 Online:2011-04-25 Published:2011-04-25

摘要: 多细节层次表达是三维GIS的重要特征之一。为提高细节层次模型的管理效率,本文提出一种扩展多细节层次功能的三维R树索引方法,通过全局优化和三维聚类分析建立动态三维R树索引,研制了先自下而上、后自上而下全局搜索的节点选择算法和基于k-medoids聚类算法的节点分裂算法,保证节点尺寸均匀、形状规则以及重叠减少。基于良好的三维树形结构,本文扩展了传统的三维R树索引结构,实现R树索引和细节层次模型的无缝集成。为验证本文方法的有效性,通过仿真实验,结果证明了本文方法能很大程度地提升多细节层次三维城市模型数据库的空间查询效率,具有较好的应用前景和实用价值。

Abstract: Levels of detail (LOD) is the key feature of 3D GIS. Aiming at the critical issues of LOD models organization, this paper presents an extended dynamic 3D R-tree structure concerned with LODs, which improves existing node-choosing and node-splitting sub-algorithms. In the node-choosing process, a globally-optimising approach is adopted which is first bottom-up then top-down, and that an improved clustering algorithm based on k-medoids is applied to the node-splitting process. This method makes more even node size, more regular node shape, and less node overlap. Furthermore, a method of integrating R-tree and LOD is put forward based on such kind of good R-tree structure. In order to verify the validity of this method , the simulated experimental analysis results approve that this approach improves 3D query performance greatly relative to existing ones and succeeds in integrating LOD models, which denotes good application prospect and practical value.