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		<isbn>978-85-17-00088-1</isbn>
		<label>59805</label>
		<citationkey>NadasRodrTrinRiba:2017:AnDeCl</citationkey>
		<title>Análise do desempenho do classificador automático MAXVER para uso e cobertura do solo na bacia do rio Mampituba ? SC</title>
		<format>Internet</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
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		<size>1196 KiB</size>
		<author>Nadas, Micael Babosa,</author>
		<author>Rodrigues, Tailise Faggion,</author>
		<author>Trinca, Wladimir Alexandre,</author>
		<author>Ribas, Rodrigo Pinheiro,</author>
		<electronicmailaddress>micael_nadas@hotmail.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>4451-4458</pages>
		<booktitle>Anais</booktitle>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<abstract>This study evaluates the thematic accuracy of the maximum likelihood classifier in a medium spatial resolution imaging satellite Landsat-8. The study area refers to the basin of the Mampituba river in Santa Catarina - Brazil. The analyzed classes were agriculture area, urban area, hydrography, exposed soil and vegetation, where we made a deeper study about the vegetal formations inside the area. The methodology consisted in first of all the discussion about the tools used in image classification such as GIS (Geographic Information System), Remote Sensing and GPS (Global Positioning System) Then, the acquisition of free Landsat 8 images, image processing, classifier training, classification, data analysis and results. The quality of the thematic map was assessed using the kappa statistic, overall accuracy, producer''s accuracies and user. The results show that automatic classification given by the classifier gives excelent results for kappa (90,09%) and overall accuracy (93,80%). Among the classes evaluated, the fragment hydrography and bare soil were those with the best accuracies and precisions. The recognition of other classes as agriculture area, urban area, vegetation, depending on the complexity of the landscape and its small dimensions in the study area, depends on the use of image interpretation techniques for further details, making it necessary a new field verification to improve and validate the results.</abstract>
		<area>SRE</area>
		<type>Landsat OLI</type>
		<language>pt</language>
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