Conference Publication Details
Mandatory Fields
Keyes L.;Winstanley A.;Healy P.
International Geoscience and Remote Sensing Symposium (IGARSS)
Comparing Learning Strategies for Topographic Object Classification
Optional Fields
Fitting distribution models Learning strategies Topographic object classification
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