Conference Publication Details
Mandatory Fields
Keyes, L;Winstanley, A;Healy, P
IGARSS 2003: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS I - VII, PROCEEDINGS
Comparing learning strategies for topographic object classification
2003
January
Published
1
1 ()
Optional Fields
3468
3470
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.
Grant Details