Paper Info
Title
Different Bayesian network models in the classification of remote sensing images
Abstract
In this paper we study the application of Bayesian network models to classify multispectral and hyperspectral remote sensing images. Different models of Bayesian networks as: Naive Bayes (NB), Tree Augmented Naive Bayes (TAN) and General Bayesian Networks (GBN), are applied to the classification of hyperspectral data. In addition, several Bayesian multi-net models: TAN multi-net, GBN multi-net and the model developed by Gurwicz and Lerner, TAN-Based Bayesian Class-Matched multi-net (tBCM2) (see [1]) are applied to the classification of multispectral data. A comparison of the results obtained with the different classifiers is done.
Year
DOI
Venue
2007
10.1007/978-3-540-77226-2_2
IDEAL
Keywords
Field
DocType
general bayesian networks,tan multi-net,different classifier,bayesian network,different bayesian network model,bayesian network model,tan-based bayesian class-matched multi-net,bayesian multi-net model,gbn multi-net,tree augmented naive bayes,naive bayes
Pattern recognition,Naive Bayes classifier,Computer science,Multispectral image,Remote sensing,Hyperspectral imaging,Bayesian network,Artificial intelligence,Machine learning,Multispectral data,Bayesian probability
Conference
Volume
ISSN
ISBN
4881
0302-9743
3-540-77225-1
Citations 
PageRank 
References 
2
0.37
6
Authors
2
Name
Order
Citations
PageRank
Cristina Solares1467.89
Ana Maria Sanz220.37