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Proceedings Paper

Bayesian networks for mapping salinity using multitemporal Landsat TM imagery
Author(s): Dongming Huo; Jingxiong Zhang; Jiabing Sun; Gaohuan Liu
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Paper Abstract

Bayesian networks are used for reasoning under uncertainty. This paper examines the use of Bayesian networks for integrating multi-temporal remotely sensed data with landform data derived from digital elevation models (DEM) and groundwater data to produce maps showing areas affected by salinity in the Yellow River Delta of China. Incorporating prior knowledge about the relationships between input attributes and their relationship with salinity, a conditional probabilistic network is used to impose a known relationship between input attributes and salinity status. The results are compared with maximum likelihood classification techniques using single-date Landsat TM imagery. They show a large improvement on the maximum likelihood classifier. The network is used to produce a time-series of landcover and salinity maps for the Yellow River Delta.

Paper Details

Date Published: 20 September 2001
PDF: 6 pages
Proc. SPIE 4555, Neural Network and Distributed Processing, (20 September 2001); doi: 10.1117/12.441695
Show Author Affiliations
Dongming Huo, Wuhan Univ. (China)
Jingxiong Zhang, Wuhan Univ. (China)
Jiabing Sun, Wuhan Univ. (China)
Gaohuan Liu, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 4555:
Neural Network and Distributed Processing
Xubang Shen; Jianguo Liu, Editor(s)

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