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		<doi>10.1007/978-3-030-58814-4_17</doi>
		<isbn>978-303058813-7</isbn>
		<issn>03029743</issn>
		<citationkey>BittencourtMoreSantSant:2020:ApClBu</citationkey>
		<title>An approach to classify burned areas using few previously validated samples</title>
		<year>2020</year>
		<date>01-04 July</date>
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		<author>Bittencourt, Olga de Oliveira,</author>
		<author>Morelli, Fabiano,</author>
		<author>Santos Júnior, Cícero Alves dos,</author>
		<author>Santos, Rafael Duarte Coelho dos,</author>
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		<group>LABAC-COCTE-INPE-MCTIC-GOV-BR</group>
		<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>
		<electronicmailaddress>olga.bittencourt@inpe.br</electronicmailaddress>
		<electronicmailaddress>fabiano.morelli@inpe.br</electronicmailaddress>
		<electronicmailaddress>cicero.junior@inpe.br</electronicmailaddress>
		<electronicmailaddress>rafael.santos@inpe.br</electronicmailaddress>
		<editor>Gervasi, O.,</editor>
		<editor>Murgante, B.,</editor>
		<editor>Misra, S.,</editor>
		<editor>Garau, C.,</editor>
		<editor>Blecic, I.,</editor>
		<editor>Taniar, D.,</editor>
		<editor>Apduhan, B. O.,</editor>
		<editor>Rocha, A. M. A. C.,</editor>
		<editor>Tarantino, E.,</editor>
		<editor>Torre, C. M.,</editor>
		<editor>Karaca, Y.,</editor>
		<conferencename>International Conference on Computational Science and Its Applications,20</conferencename>
		<conferencelocation>Cagliari, Italy</conferencelocation>
		<booktitle>Proceedings</booktitle>
		<publisher>Springer</publisher>
		<documentstage>not transferred</documentstage>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<secondarytype>PRE CI</secondarytype>
		<versiontype>publisher</versiontype>
		<pages>239-254</pages>
		<keywords>Remote sensing · Burned forest classification keyword · Forest fire survey and monitoring.</keywords>
		<abstract>Monitoring the large number of active fires and their consequences in such an extensive area such as the Brazilian territory is an important task. Machine Learning techniques are a promising approach to contribute to this area, but the challenge is the building of rich example datasets, whose previous examples are unavailable in many areas. Our aim in this article is to move towards the development of an approach to detect burned areas in regions for which there is no previously validated samples. We deal with that by presenting some experiments to classify burned areas through Machine Learning techniques that combine remote sensing data from nearby areas and it can distinguish between burned and non burned polygons with good results.</abstract>
		<area>COMP</area>
		<language>en</language>
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		<notes>Lecture Notes in Computer Science, v.12253</notes>
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