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

Informations sur la ressource

Date of publication: 
décembre 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.

Auteurs et éditeurs

Author(s), editor(s), contributor(s): 
Dlamini, Wisdom M.
Publisher(s): 

Taylor & Francis Group publishes books for all levels of academic study and professional development, across a wide range of subjects and disciplines.
Taylor & Francis Group publishes quality peer-reviewed journals under the Routledge and Taylor & Francis imprints. The newest part of the group, Cogent OA, offers a purely open access program.

Fournisseur de données

Concentration géographique

Catégories apparentées

Partagez cette page