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		<issn>2179-4820</issn>
		<citationkey>AndradeBittMoreSant:2017:ClSeÁr</citationkey>
		<title>Classificação semiautomática de áreas queimadas com o uso de redes neurais</title>
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		<year>2017</year>
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		<author>Andrade, Ronaldo Nelis de,</author>
		<author>Bittencourt, Olga,</author>
		<author>Morelli, Fabiano,</author>
		<author>Santos, Rafael,</author>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<editor>Davis Jr., Clodoveu A. (UFMG),</editor>
		<editor>Queiroz, Gilberto R. de (INPE),</editor>
		<e-mailaddress>lubia@dpi.inpe.br</e-mailaddress>
		<conferencename>Simpósio Brasileiro de Geoinformática, 18 (GEOINFO)</conferencename>
		<conferencelocation>Salvador</conferencelocation>
		<date>04-06 dez. 2017</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<pages>92-97</pages>
		<booktitle>Anais</booktitle>
		<tertiarytype>Short papers</tertiarytype>
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		<abstract>This paper presents an approach to improve the semi-automatic detection of burned areas through the use of neural networks. The approach is validated over a selected study area in the Brazilian Cerrado against reference data derived from data classified by experts. Methods are still being developed and improved, and initial results corroborate the validity of the approach, which will be extended to other study areas.</abstract>
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