Earth's surface space is a complex huge system and character with hierarchical structures. Entities, patterns and processes
all show inherent hierarchy structure in nature. The character of Scale-dependence is corresponded with hierarchy. Many
research works have demonstrated that scale-dependence is a basic characteristic of Geo-spatial space. Therefore, the
multi-scale or hierarchical approach needs to be introduced in the course of spatial information analysis, monitoring,
modeling and management. It is well know that image analyze result was influenced by the window size that was
selected. The original fixed window cannot suit with the object spatial character. In this letter, we first propose an
optimal window selection method, based on the spectral information in a local block region, for choosing the suitable
window size adaptively. Secondly, the object spatial information is learned based on the selected optimal window size.
Thirdly, both the spectral and spatial information were used in image classification. In this paper, the proposed algorithm
can obtain the multi-scale features effectively and the features we get at different scale level have an obvious stability
with property. In the experiment on the QuickBird image data, the proposed algorithm clearly improves the classification
accuracies than fixed window sizes and reduces the salt and pepper effect and error. It is suitable to form multi-scale
hierarchy image-sets and select the objects at different scale levels.
Proc. SPIE. 6420, Geoinformatics 2006: Geospatial Information Science
KEYWORDS: Internet, Data storage, Computing systems, Geographic information systems, Data acquisition, Software development, Local area networks, Computer architecture, Web services, Classification systems
Traditional GIS has many flaws in managing distributed, large-scale, heterogeneous spatial data. Nowadays, more and more countries build their own spatial data grid to provide abundant data services. But data grid system is a complicated one and involved in lots of entities which are different in many ways, including type, performance, runtime environment, etc. It is necessary to divide the whole system into different layers, which has great importance to simplify the system complexity, improving system stability and establish an efficient, extensible, self-adaptive and self-organizing system. Here, after carefully analyzing various system entities, the article describe the idea that entities are divided into three kinds service-type entities, management-type entities and draw-type entities that is the basis of clear level classification of the spatial data grid and establishment of a mixed three-tier architecture based on grid computing and P2P technologies.