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    Comparing Learning Strategies for Topographic Object Classification

    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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    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:
    Depositing User: Dr. Adam Winstanley
    Date Deposited: 30 Mar 2017 14:30
    Publisher: IEEE
    Refereed: Yes
    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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