DocumentCode :
451035
Title :
Applying incremental EM to Bayesian classifiers in the learning of hyperspectral remote sensing data
Author :
Wang, X. Rosalind ; Brown, Adrian J. ; Upcroft, Ben
Author_Institution :
Centre for Autonomous Syst., Sydney Univ., NSW, Australia
Volume :
1
fYear :
2005
fDate :
25-28 July 2005
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.
Keywords :
belief networks; expectation-maximisation algorithm; image classification; infrared detectors; infrared imaging; infrared spectroscopy; learning (artificial intelligence); remote sensing; Bayesian network classifier; electromagnetic spectrum; expectation maximisation; geological investigation; hyperspectral remote sensor data; image spectroscopy; incremental EM method; incremental learning; robotic planetary mission; tree-augmented-Naive Bayes structure; Bayesian methods; Geology; Hyperspectral imaging; Hyperspectral sensors; Image sensors; Orbital robotics; Remote sensing; Robot sensing systems; Space vehicles; Spectroscopy; Bayesian networks; Hyperspectral Imaging; Incremental EM;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Fusion, 2005 8th International Conference on
Print_ISBN :
0-7803-9286-8
Type :
conf
DOI :
10.1109/ICIF.2005.1591910
Filename :
1591910
Link To Document :
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