基于特征基元的空间数据计算模式及其地学应用

将地学计算的定义外延为空间数据计算，对空间数据计算的基本单元问题进行了初步的探讨，提出了基于特征基元的空间数据计算一般模式，并根据计算行为模式及计算侧重点的不同，将空间数据计算过程分为深度计算与主动计算，总结了“数据→特征→知识”的一般计算过程，并就此进行阐释。根据地理实体的形态和功能过程的不同来刻画形态和功能过程差异的空间数据，将地学空间数据划分为反映固态基质信息的陆地空间数据、反映液态基质信息的陆地水文空间数据、反映液态基质信息的海洋流体空间数据和反映气态基质信息的大气流体空间数据四类。基于陆地、水文、海洋和大气相关空间现象和空间过程分析，结合提出的深度计算与主动计算理论，对这四类空间数据的深度计算与主动计算过程进行了初步探讨。以基于特征的遥感信息提取和目标识别工作为例，对上述理论进行了说明和验证。最后对空间数据计算模式相关问题进行总结和展望。

Spatial Data Computing Pattern and Its Geo-Application

With the development in satellite sensor technology, data acquisition technology developed rapidly; and with the start of a series of space-based observation network for Earth science, such as EOS, GTOS, ECOS, GOOS and etc., high performance processing and analysis of tremendous data become the bottleneck we face. According to the functional differences between different data carrier of terrene, ocean and atmosphere, this paper divides spatial data into four classes：terrestrial-solid based spatial data, terrestrial-liquid based spatial data, marine-floating based spatial data and atmospheric-floating based spatial data. Then this paper introduces the concept of the basic unit in which the features or characters are homogenous and then proposes their actually existing style in the four types of spatial data mentioned above. 
Furthermore, this paper simply reviews geocomputation and expands it to geo-spatial computation. Then this paper discusses the connotation and classification of geo-spatial computation and summarizes the general computing procedure: data→ features→ knowledge. According to the differences of the computational behavior and the computing emphasis, this paper divides geo-spatial computation into two classes: deep-computation and active-computation. Deep-computation (from data to features) is to extract the basic units through certain methods, such as clustering, so deep-computation emphasizes particularly on computing amount. Active-computation (from features to knowledge) is based on the basic units obtained by deep-computation. Firstly the spatial relationships between the units are computed, and the decisions can be made effectively and efficiently with domain knowledge and domain models through web services, so deep-computation emphasizes particularly on intelligence of computation.
Consequently, this paper analyzes the computing pattern of the four types of spatial data mentioned above. What's more, a case study of information extraction and target recognition from remote sensing image based on features was done to illustrate and testify the ideas mentioned above. In the end, this paper summarizes the relative problems about spatial data computation and expects the direction of future researches.