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

By X. Rosalind. Wang, Adrian J. Brown and Ben Upcroft


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

Topics: 090600 ELECTRICAL AND ELECTRONIC ENGINEERING, 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
Publisher: IEEE
Year: 2005
DOI identifier: 10.1109/ICIF.2005.1591910
OAI identifier: oai:eprints.qut.edu.au:40437

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