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Library Application of a Bayesian network for land-cover classification from a Landsat 7 ETM+ image

Application of a Bayesian network for land-cover classification from a Landsat 7 ETM+ image

Application of a Bayesian network for land-cover classification from a Landsat 7 ETM+ image

Resource information

Date of publication
december 2011
Resource Language
ISBN / Resource ID
AGRIS:US201400106853
Pages
6569-6586

This article describes the use of a Bayesian network (BN) for the classification of land cover from satellite imagery in northern Swaziland. The main objective of this work was to apply and evaluate the efficacy of a BN for land-cover classification using gap-filled and terrain-corrected Landsat 7 Enhanced Thematic Mapper Plus (ETM+) imagery acquired on 15 May 2007. The posterior probabilities (parameters) were estimated using the expectation-maximization (EM) and conjugate gradient descent (CGD) algorithms. A comparison of the results obtained from the algorithms indicates similar and excellent overall classification accuracies of 93.01%, and kappa coefficient values of 0.9143. The main result obtained in this study is that both algorithms considered here provide relatively similar and accurate solutions for the classification of the multispectral image although the EM algorithm is marginally competitive relative to CGD algorithm when measured in terms of the Brier score and the logarithmic loss.

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Authors and Publishers

Author(s), editor(s), contributor(s)

Dlamini, Wisdom M.

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Geographical focus