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Proceedings Paper

The study on rough set in GIS and remote sensing
Author(s): Qing-wei Zhu; Da-zhi Guo; Hao Zhang
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Paper Abstract

Recently, spatial data mining and knowledge discovery are developed rapidly. In this paper, first, the developing status of spatial data mining and knowledge discovery are concluded; then deeply studying data mining in GIS and remote sensing are performed; third, Rough sets were executed with TM imagery to carry out data mining and knowledge discovery and finally acquiring feature rules of the relation between soil erosion and three elements: Vegetation cover, Slope and cropland. The paper gives support to land use and land reclaim effective. There are four parts are composed. In the first part some DM background are narrated and some problems hidden, which are very important for us to use, are brought forward. This is to say, much hiding information that is the thing we want most is waste. Then spatial data mining and knowledge discovery (short-hand SDNKD) were appeared in this background. An exhaustive study Dm is beyond the scope of this paper. In this study, we focus on two perspectives 1) integration DM with GIS. 2) Integration DM with RS. In the second part, we describe method of spatial data mining including: Apriori Algorithm, Rough Sets Theory, Inductive Learning, Clustering and so on, and emphasis on Rough Sets. Relationship among RS, GIS and DM are interpreted. Integration GIS with DM and Integration RS with DM are explained by fig1 and fig2 in the third part. An explicit example about soil erosion is depicted to explain the relations through Rough Set and some rules are acquired to give support to land use and land reclaim effective in the fourth part. At last, we conclude our study.

Paper Details

Date Published: 2 December 2005
PDF: 8 pages
Proc. SPIE 6045, MIPPR 2005: Geospatial Information, Data Mining, and Applications, 60451L (2 December 2005); doi: 10.1117/12.651214
Show Author Affiliations
Qing-wei Zhu, China Univ. of Mining and Technology (China)
Xi'an Univ. of Science and Technology (China)
Da-zhi Guo, China Univ. of Mining and Technology (China)
Hao Zhang, China Univ. of Mining and Technology (China)


Published in SPIE Proceedings Vol. 6045:
MIPPR 2005: Geospatial Information, Data Mining, and Applications
Jianya Gong; Qing Zhu; Yaolin Liu; Shuliang Wang, Editor(s)

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