NSSC OpenIR  > 空间技术部
Alternative TitleStudy on 3D Subdivision Mode and Encoding in Heliocentric Coordination Systemormalsize
胡雅斯; 宋君君; 时蓬; 段然
Source Publication空间科学学报
Keyword空间剖分 Sdog-r 编码 多分辨率
Abstract随着空间数据的大量增长, 对数据可视化和数据存取效率提出了更高要求, 迫切需要对数据进行有效的组织和管理. 对庞大的日地空间, 采用SDOG-R方法将日地空间剖分为不同分辨率等级的格网, 并针对该网格提出相应的编 码方案. 以太阳风模型数据为例, 给出了具体的组织实例, 经实验验证, 该 剖分模型不仅解决了球心处网格过密问题, 还满足了径向分辨率大于经纬球 面分辨率的需求. 基于三维立体剖分的太阳风LOD空间数据模型, 不但能提供 多分辨率数据, 而且显著提高了大规模数据检索和存取速度, 有效地支持海量 空间数据的组织管理.
Other AbstractWith the ever-increasing of space data, a reasonable data-arrangement is strongly required for high efficiency in data accessing and visualization. In this paper, basing on the characteristics of solar-wind data, a SDOG-R grid model is employed, which is radial independent division in the adaptive SDOG grid. For huge Sun-Earth space, it is divided into multi-resolution grids. The original data, which is output from a SIP-CESE solar wind model, are re-sampled, and put into the subdivided grids. The grid model is applied to both regular and irregular sampling data, and fully maintains the characteristics of original data to the best and encodes grids with an improved CDZ curve. Specific examples of data organization are given. The experiments prove that the grid model not only resolves the problem that grids are too dense at two poles and the spherical center, but also meets the need for higher resolution in radius than in latitude and longitude. Besides, 3D LOD spatial data model can not only provide multi-resolution data, but also significantly improve the large-scale mass data retrieval and access efficiency, and can support the organization and management of massive spatial data effectively.
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Document Type期刊论文
Corresponding Author胡雅斯
Recommended Citation
GB/T 7714
胡雅斯,宋君君,时蓬,等. 基于日心坐标系的三维立体剖分模型及编码[J]. 空间科学学报,2016,36(1):106-116.
APA 胡雅斯,宋君君,时蓬,&段然.(2016).基于日心坐标系的三维立体剖分模型及编码.空间科学学报,36(1),106-116.
MLA 胡雅斯,et al."基于日心坐标系的三维立体剖分模型及编码".空间科学学报 36.1(2016):106-116.
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