Comparison between maximum likelihood, artificial neural network and decision trees technique for land cover mapping using remotely sensed data

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Main Author:	Gelungan, Cassindra
Format:	Thesis
Published:	2010
Subjects:	Unspecified
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id	my-utm-ep.16329
record_format	uketd_dc
spelling	my-utm-ep.163292011-10-24T08:39:54Z Comparison between maximum likelihood, artificial neural network and decision trees technique for land cover mapping using remotely sensed data 2010-00 Gelungan, Cassindra Unspecified 2010-00 Thesis http://eprints.utm.my/id/eprint/16329/ masters Universiti Teknologi Malaysia, Faculty of Geoinformation and Real Estate Faculty of Geoinformation and Real Estate
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
topic	Unspecified
spellingShingle	Unspecified Gelungan, Cassindra Comparison between maximum likelihood, artificial neural network and decision trees technique for land cover mapping using remotely sensed data
description
format	Thesis
qualification_level	Master's degree
author	Gelungan, Cassindra
author_facet	Gelungan, Cassindra
author_sort	Gelungan, Cassindra
title	Comparison between maximum likelihood, artificial neural network and decision trees technique for land cover mapping using remotely sensed data
title_short	Comparison between maximum likelihood, artificial neural network and decision trees technique for land cover mapping using remotely sensed data
title_full	Comparison between maximum likelihood, artificial neural network and decision trees technique for land cover mapping using remotely sensed data
title_fullStr	Comparison between maximum likelihood, artificial neural network and decision trees technique for land cover mapping using remotely sensed data
title_full_unstemmed	Comparison between maximum likelihood, artificial neural network and decision trees technique for land cover mapping using remotely sensed data
title_sort	comparison between maximum likelihood, artificial neural network and decision trees technique for land cover mapping using remotely sensed data
granting_institution	Universiti Teknologi Malaysia, Faculty of Geoinformation and Real Estate
granting_department	Faculty of Geoinformation and Real Estate
publishDate	2010
_version_	1747815017145696256

Comparison between maximum likelihood, artificial neural network and decision trees technique for land cover mapping using remotely sensed data

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