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The National Agricultural Library is one of four national libraries of the United States, with locations in Beltsville, Maryland and Washington, D.C. It houses one of the world's largest and most accessible agricultural information collections and serves as the nexus for a national network of state land-grant and U.S. Department of Agriculture field libraries. In fiscal year 2011 (Oct 2010 through Sept 2011) NAL delivered more than 100 million direct customer service transactions.

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Journal Article

Статья в журнале

Application of a Bayesian network for land-cover classification from a Landsat 7 ETM+ image  [2011]

Dlamini, Wisdom M.;

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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.
Журнал
International journal of remote sensing
ISSN : 1366-5901

Библиографическая информация

Язык:
English
Тип:
Journal Article
В АГРИСе с:
2014
Том:
10 v. 32
Выпуск:
21
Стартовая страница:
6569
Конечная страница:
6586
Издатель:
Taylor & Francis
Все названия:
"Application of a Bayesian network for land-cover classification from a Landsat 7 ETM+ image"@eng
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Библиографическая информация

Язык:
English
Тип:
Journal Article
В АГРИСе с:
2014
Том:
10 v. 32
Выпуск:
21
Стартовая страница:
6569
Конечная страница:
6586
Издатель:
Taylor & Francis
Все названия:
"Application of a Bayesian network for land-cover classification from a Landsat 7 ETM+ image"@eng