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

Wang, X. Rosalind., Brown, Adrian J., & Upcroft, Ben (2005) Applying incremental EM to Bayesian classifiers in the learning of hyperspectral remote sensing data. In Proceedings 8th International Conference on Information Fusion, 2005, IEEE, Wyndham Philadelphia at Franklin Plaza Philadelphia, PA, USA.

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Abstract

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.

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ID Code: 40437
Item Type: Conference Paper
Keywords: Bayesian networks, incremental EM, Hyperspectral imaging, Orbital robotics, robotic planetary mission, electromagnetic spectrum, expectation maximisation, geological investigation, hyperspectral remote sensor data, image spectroscopy, incremental learning, tree-augmented-Naive Bayes structure , Robot sensing systems, Remote sensing, Space vehicles
DOI: 10.1109/ICIF.2005.1591910
ISBN: 0780392868 Print
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
Copyright Owner: Copyright 2005 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 10 May 2011 13:48
Last Modified: 13 Aug 2011 15:56

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