Keyes, Laura and Winstanley, Adam C. and Healy, P. (2003) Comparing Learning Strategies for Topographic Object Classification. In: 2003 IEEE International Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. IEEE, pp. 3468-3470. ISBN 0780379292

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Abstract

Two methods of topographic object classification through shape are described. Unsupervised classification through clustering analysis is compared with supervised classification based on a Bayesian framework. Both are applied to the real world problem of checking and assigning feature-codes in large-scale topographic data for use in computer cartography and Geographical Information Systems (GIS). Categorisation is accompanied by a confidence measure that the classification is correct. Both types of classification were implemented and their outcomes evaluated and compared. As a case study, results and conclusions are presented on the classification and identification of archaeological feature shapes on OS large-scale maps. It was found that the supervised classification model used out-performed the unsupervised classification model to a considerable degree.

Item Type:	Book Section
Keywords:	unsupervised classification model; learning strategies; topographic object classification; clustering analysis; Bayesian classification; computer cartography; Geographical Information Systems; GIS;
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	8105
Identification Number:	https://doi.org/10.1109/IGARSS.2003.1294824
Depositing User:	Dr. Adam Winstanley
Date Deposited:	30 Mar 2017 14:30
Publisher:	IEEE
Refereed:	Yes
URI:	http://ieeexplore.ieee.org
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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