Applying incremental EM to Bayesian classifiers in the learning of hyperspectral remote sensing data

Wang, X. R., Brown, A. J. and Upcroft, B. (2005). Applying incremental EM to Bayesian classifiers in the learning of hyperspectral remote sensing data. In: , 2005 7th International Conference on Information Fusion (FUSION). 7th International Conference on Information Fusion, Philadelphia, U.S., (606-613). 25-28 July, 2005.


Author Wang, X. R.
Brown, A. J.
Upcroft, B.
Title of paper Applying incremental EM to Bayesian classifiers in the learning of hyperspectral remote sensing data
Conference name 7th International Conference on Information Fusion
Conference location Philadelphia, U.S.
Conference dates 25-28 July, 2005
Proceedings title 2005 7th International Conference on Information Fusion (FUSION)
Place of Publication New York, U.S.
Publisher IEEE
Publication Year 2005
Sub-type Fully published paper
DOI 10.1109/ICIF.2005.1591910
ISBN 0-7803-9286-8
Volume 1
Start page 606
End page 613
Total pages 8
Language eng
Abstract/Summary In this paper, we apply the incremental EM method to Bayesian Network Classifiers to learn and interpret hyperspectral sensor data in robotic planetary missions. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. Many spacecraft carry spectroscopic equipment as wavelengths outside the visible light in the electromagnetic spectrum give much greater information about an object. The algorithm used is an extension to the standard Expectation Maximisation (EM). The incremental method allows us to learn and interpret the data as they become available. Two Bayesian network classifiers were tested: the Naive Bayes, and the Tree-Augmented-Naive Bayes structures. Our preliminary experiments show that incremental learning with unlabelled data can improve the accuracy of the classifier.
Subjects 0801 Artificial Intelligence and Image Processing
Keyword Bayesian networks
Incremental EM
Hyperspectral imaging
Q-Index Code EX

 
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Created: Mon, 18 Jan 2010, 16:23:09 EST by Tara Johnson on behalf of Faculty Of Engineering, Architecture & Info Tech