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		<isbn>978-85-17-00088-1</isbn>
		<label>59887</label>
		<citationkey>CoelhoCarvBarr:2017:RJClDi</citationkey>
		<title>Mapeamento da cobertura da terra no Parque Estadual da Serra da Concórdia (PESC) - RJ através de classificação digital híbrida</title>
		<format>Internet</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
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		<size>644 KiB</size>
		<author>Coelho, Raphael Corrêa de Souza,</author>
		<author>Carvalho, Marcus Vinícius Alves de,</author>
		<author>Barros, Rafael Silva de,</author>
		<electronicmailaddress>raphaelcoelhof3@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>4635-4642</pages>
		<booktitle>Anais</booktitle>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<abstract>The objective of this study is to evaluate different spectral indices (EVI, NDVI, Modified NDVI, NDWI), transformed images (PCA and IHS), and the Linear Spectral Mixing Model (fraction-image: Soil) in an application of GEOBIA: Geographic Object-Based Image Analysis (knowledge modeling: heuristic approach integrated to the discovery of patterns: geographic data mining) in images from the REIS-2 (Earth Imaging System-2) sensor of the RapidEye satellite. The study area is the Parque Estadual da Serra da Concórdia (PESC), a Nature Conservation Unit (UC) inserted in the Atlantic Forest Biome in the Rio de Janeiro state, Brazil. The first step consisted of the atmospheric correction of the images using 6S algorithm. This process presented a satisfactory result, due to being in agreement with the Scientific Literature. It was observed that the PCA (Principal Component Analysis) and HIS (Intensity, Hue and Saturation) images, besides helping to elaborate the class descriptors, also contributed to reduce the internal heterogeneity of the classes in the segmentation process. The Modified NDVI, generated from the change of the Red band (630-685nm) by the Red-Edge band (690 to 730 nm) was to highlight well objects of vegetation. The thematic mapping generated reached global accuracy of 87.76% and Kappa Index of 84.54%.</abstract>
		<area>SRE</area>
		<type>Processamento de imagens</type>
		<language>pt</language>
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