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		<issn>2179-4820</issn>
		<citationkey>CostaFoKoSiBeSo:2017:SeOpRe</citationkey>
		<title>Segmentation of optical remote sensing images for detecting homogeneous regions in space and time</title>
		<format>Pendrive, On-line.</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
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		<size>4411 KiB</size>
		<author>Costa, Wanderson S.,</author>
		<author>Fonseca, Leila M. G.,</author>
		<author>Korting, Thales S.,</author>
		<author>Simões, Margareth G.,</author>
		<author>Bendini, Hugo N.,</author>
		<author>Souza, Ricardo Cartaxo M.,</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>Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)</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>40-51</pages>
		<booktitle>Anais</booktitle>
		<tertiarytype>Full papers</tertiarytype>
		<transferableflag>1</transferableflag>
		<abstract>With the amount of multitemporal and multiresolution images grow- ing exponentially, the number of image segmentation applications is recently increasing and, simultaneously, new challenges arise. Hence, there is a need to explore new segmentation concepts and techniques that make use of the tempo- ral dimension. This paper describes a spatio-temporal segmentation that adapts the traditional region growing technique to detect homogeneous regions in space and time in optical remote sensing images. Tests were conducted by consider- ing the Dynamic Time Warping measure as the homogeneity criterion. Study cases on high temporal resolution for sequences of MODIS and Landsat-8 OLI vegetation indices products provided satisfactory outputs and demonstrated the potential of the spatio-temporal segmentation method.</abstract>
		<area>SRE</area>
		<language>pt</language>
		<targetfile>5costa_souza.pdf</targetfile>
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		<url>http://mtc-m16c.sid.inpe.br/rep-/sid.inpe.br/mtc-m16c/2017/12.01.19.11</url>
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