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Proceedings Paper

A novel approach to spatial data fusion based on Bayesian network
Author(s): Jiejun Huang; Peipei Qi; Youchuan Wan; Fawang Ye
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Paper Abstract

New technologies and automated systems (such as multi-sensor systems) allow us to collect and store a large amount of spatial data in a quite efficient and inexpensive way. Especially, the advent of remote sensing and GIS has great enhanced our capabilities to capture spatial data. However raw data are seldom useful without some kind of processing, it needs more powerful technologies to deal with the databases. Therefore, spatial data fusion and data mining have been used in this domain. They can improve the efficiency and accuracy of spatial information utilization. In this paper, we focused on how to fusion spatial data for decision making by learning Bayesian networks. A review is presented on spatial data fusion. We propose a method of spatial data fusion based on Bayesian networks, which is optimized by using the theory of Particle Swarm Optimization (PSO). And then we showed a case study for spatial data fusion based on the approach. The experimental results are given to illustrate the practical feasibility of the proposed technique. Eventually, we conclude with a summary and a statement of future work.

Paper Details

Date Published: 29 December 2008
PDF: 8 pages
Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 728516 (29 December 2008); doi: 10.1117/12.815453
Show Author Affiliations
Jiejun Huang, Wuhan Univ. of Technology (China)
Peipei Qi, Wuhan Univ. of Technology (China)
Youchuan Wan, Wuhan Univ. (China)
Fawang Ye, National Key Laboratory of RS Information and Imagery Analysis (China)

Published in SPIE Proceedings Vol. 7285:
International Conference on Earth Observation Data Processing and Analysis (ICEODPA)
Deren Li; Jianya Gong; Huayi Wu, Editor(s)

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