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		<identifier>8JMKD3MGP6W34M/3PSLT84</identifier>
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		<isbn>978-85-17-00088-1</isbn>
		<label>59284</label>
		<citationkey>CasarotiCentPrun:2017:CoUsCo</citationkey>
		<title>Comparação do uso combinado de variáveis espectrais e índices de vegetação calculados a partir das bandas Red e Red Edge para classificação de uma imagem RapidEye</title>
		<format>Internet</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
		<numberoffiles>1</numberoffiles>
		<size>913 KiB</size>
		<author>Casaroti, Carla Jaqueline,</author>
		<author>Centeno, Jorge Antonio Silva,</author>
		<author>Prunzel, Jaqueline,</author>
		<electronicmailaddress>carlacasaroti@gmail.com</electronicmailaddress>
		<editor>Gherardi, Douglas Francisco Marcolino,</editor>
		<editor>Aragão, Luiz Eduardo Oliveira e Cruz de,</editor>
		<e-mailaddress>daniela.seki@inpe.br</e-mailaddress>
		<conferencename>Simpósio Brasileiro de Sensoriamento Remoto, 18 (SBSR)</conferencename>
		<conferencelocation>Santos</conferencelocation>
		<date>28-31 maio 2017</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<pages>3584-3591</pages>
		<booktitle>Anais</booktitle>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<abstract>This paper consists on describing the steps involving two classifications, using the OBIA (Object-oriented Image Analysis) approach along with a RapidEye high spatial resolution image, in order to compare the classification accuracy using the usual red band and the red edge band, to classify the vegetation land cover. To classify the geographic objects yielded from the multiresolution segmentation, spectral descriptors from the bands and NDVIs (Normalized Difference Vegetation Index) from the usual band red and the band red edge, as well as a Digital Elevation Model (DEM) were used. To make the descriptors'' choice, a selection was made towards the attributes, which could better separate the classes of interest regarding the samples. The two classifications were performed, using the selected descriptors to each one, and then the global accuracy as well as the coefficient Kappa and confusion matrix were compared. The global accuracy from the first classification using the usual red band was of 87% and the other one was 90%, indicating that, the red edge band could improve in 3% the classification accuracy when used. As main steps of the released methodology we had: classes of interest definition, choice of the segmentation parameters, class descriptors selection for the two classifications, and at last the two classifications.</abstract>
		<area>SRE</area>
		<type>Classificação e mineração de dados</type>
		<language>pt</language>
		<targetfile>59284.pdf</targetfile>
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