29 December 2008 A novel approach to spatial data fusion based on Bayesian network
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Proceedings Volume 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA); 728516 (2008); doi: 10.1117/12.815453
Event: International Conference on Earth Observation Data Processing and Analysis, 2008, Wuhan, China
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.
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Jiejun Huang, Peipei Qi, Youchuan Wan, Fawang Ye, "A novel approach to spatial data fusion based on Bayesian network", Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 728516 (29 December 2008); doi: 10.1117/12.815453; https://doi.org/10.1117/12.815453
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KEYWORDS
Data fusion

Particle swarm optimization

Particles

Remote sensing

Evolutionary algorithms

Agriculture

Algorithms

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