Image Mining for Generating Ontology Databases of Geographical Entities
Zhenfeng Shao 1, Jun Liu 2 and Xianqiang Zhu 1
1State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China.
2 School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.
Abstract: This paper extracts the basic geographic information from remote sensing images at first, and then studies the resolution granularity of the remote sensing images which can be applied to distinguish the features of corresponding objects by adopting global-covered remote sensing images with multi-frequency spectra and multi-resolution. Thus necessary feature information for the geographical ontology database, such as texture characteristic information can be mined and through our data mining strategy from remote sensing images based on the formal concept analysis theory, data mining methods for texture features are achieved. The emphases of this paper are the mining method for texture characteristic for generating ontology database of the geographical entity. By mining the texture characteristics, we can find the partial structure that frequently appears in the remote sensing image data, and find the restriction relationship between the central pixel and its neighborhood pixels in partial regions of images. This process is constituted by the following four steps: sampling areas partition normalized processing, characteristic data mining, building Hasse graph and generating rules. Through the computation about remote sensing image data mining, we put the uncertainty problem about characteristics form data mining up to a height of information theory and study it, and find the consolidate mathematics expression between information quantity and uncertainty about the characteristics in order to resolve the quantitative evaluation problem between information quantity and uncertainty of remote sensing image. This paper introduces the concepts-driven data mining framework to uncertainty process, so as to guide the idiographic algorithm and process during the image mining procedure. According to the characteristic of remote sensing images, combining with all kinds of GIS data, we can describe the essential characteristics that build ontology database of the geographical entity.
Keywords: image mining, ontology database, semantic-based spatial information sharing and interoperability, uncertainty
In: Wan, Y. et al. (eds) Proceeding of the 8th international symposium on spatial accuracy assessment in natural resources and environmental sciences, World Academic Union (Press).