The semi-automated classification of sedimentary organic matter in palynological preparations

Weller, Andrew F., Corcoran, Jonathan, Harris, Anthony J. and Ware, J. Andrew (2005) The semi-automated classification of sedimentary organic matter in palynological preparations. Computers & Geosciences, 31 10: 1213-1223. doi:10.1016/j.cageo.2005.03.011


Author Weller, Andrew F.
Corcoran, Jonathan
Harris, Anthony J.
Ware, J. Andrew
Title The semi-automated classification of sedimentary organic matter in palynological preparations
Journal name Computers & Geosciences   Check publisher's open access policy
ISSN 0098-3004
Publication date 2005-12
Sub-type Article (original research)
DOI 10.1016/j.cageo.2005.03.011
Volume 31
Issue 10
Start page 1213
End page 1223
Total pages 11
Place of publication Oxford ; New York
Publisher Pergamon Press
Language eng
Subject 04 Earth Sciences
0406 Physical Geography and Environmental Geoscience
Abstract The capture, analysis and classification of sedimentary organic matter in palynological preparations have been semi-automated. First, the morphological and textural discriminatory features used in classification schemes are measured using a computer-controlled stage and a digital camera mounted on a microscope in combination with Halcon image analysis algorithms. Second, the Exhaustive CHi-square Automatic Interaction Detector classification tree algorithm is applied to all feature measurements to establish their saliency as classification discriminators. Thirdly, the results of the classification tree algorithm are used to determine the inputs used by the actual classifier, which consists of a series of artificial neural networks (ANNs). The Gamma test (GT) is introduced as a tool to help facilitate the best use of limited data and to ensure that the ANNs are not over trained. The results show that the system developed is able to achieve an average correct classification rate of 87%. This is encouraging enough to prompt further research that could result in a commercially viable system. In the future, work will concentrate on refining the image capture component of the system and increasing the size of those databases that have been shown both empirically and by the GT to be too small to facilitate the construction of accurate classifiers.
Keyword Artificial neural networks
CHAID
Classification tree
Gamma test
Image analysis
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
School of Geography, Planning and Environmental Management Publications
 
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Created: Wed, 17 Mar 2010, 08:42:01 EST by Ms May Balasaize on behalf of Faculty of Science