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connection with each interrelated subject as a branch of DMKD.
So the methods of Spatial Data Mining and Knowledge
Discovery are numerous, they are mainly Statistical method,
Induction method, Clustering method, Association Rules
Mining method, Spatial Analysis method, Exploratory Data
Analysis, Rough Set method, Artificial Neural Networks,
Genetic Algorithm, Dempster-Shafer method etc. Inductive
Learning method is a very important method of Data Mining
and Knowledge Discovery, and it can sum up the determinant
regulation and mode from large amount of experiential data.
The Inductive Learning methods in common use are Attribution
Orient Inductive (AOI) and Decision Tree (DT)[6], Currently,
some scholars have already made some beneficial trials on the
application of Decision Tree method in remote sensing
information extraction.
4. CONCLUSION
Remote sensing information extraction based on knowledge is a
kind of good method, it can synthetically use all the remote
sensing information such as spectrum, texture, shape etc. These
knowledge can use solely, and can use together. We can choose
one kind of knowledge or several kinds of knowledge according
to our need. It attains wonderful effect in information extraction.
REFERENCES
[1] Guang YANG, Xiangnan Liu, 2004a. The present research
condition and development trend of remotely sensed imagery
interpretation. Remote Sensing for Land & Resources, 2, pp. 7-
15.
[2] Ping ZHAO, 2003b. Knowledge-based Landuse/cover
classification in the typical test areas of the lower reaches of
Yangtze River, Nanjing University.
[3] Tashpolati-TIYIP, Jianli Ding, 2002a. Summary of sarellite
remote sensing information extraction techniques based on
knowledge. Journal of Xinjiang University (Natural Science
Edition), 19(2), pp. 129-135.
[4] Xiaotao LI, 2004b. Application research on remote sensing
image classification based on geostatistics and ANN, ShanDong
University of Science and Technology.
[5] Huiqin ZHAI, Sumin WANG, Rong LEI, 2004a.
Classification and recognition of remote sensing image by
auxiliary of GIS. Geospatial Information, 2(4), pp 8-10.
[6] Kaichang DI, 2001. Spatial Data Mining and Knowledge
Discovery. Wuhan, pp. 28-31.
