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		<identifier>8JMKD3MGPDW34P/3Q5DQ25</identifier>
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		<issn>2179-4820</issn>
		<citationkey>MarujoFonsKortBend:2017:SpNoLa</citationkey>
		<title>Spectral normalization between Landsat-8/OLI, Landsat- 7/ETM+ and CBERS-4/MUX bands through linear regression and spectral unmixing</title>
		<format>Pendrive, On-line.</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
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		<author>Marujo, Rennan F. B.,</author>
		<author>Fonseca, Leila Maria Garcia,</author>
		<author>Korting, Thales Sehn,</author>
		<author>Bendini, Hugo do Nascimento,</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>273-282</pages>
		<booktitle>Anais</booktitle>
		<tertiarytype>Full papers</tertiarytype>
		<transferableflag>1</transferableflag>
		<abstract>Monitoring changes on Earth's surface is a difficult task commonly performed using multi-spectral remote sensing. The increasing availability of remote sensing platforms providing data makes multi-source approaches promising, since it can increase temporal revisit rate. However, Digital image processing techniques are needed to integrate the data, since sensors can be quite different in terms of acquisition characteristics. This work addresses the spectral normalizing of three medium spatial resolution sensors: Landsat- 8/OLI, Landsat-7/ETM+ and CBERS-4/MUX, through linear regression and linear mixture model approaches. The results showed slight better results when using the linear regression approach.</abstract>
		<area>SRE</area>
		<language>pt</language>
		<targetfile>35marujo_bendini.pdf</targetfile>
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		<url>http://mtc-m16c.sid.inpe.br/rep-/sid.inpe.br/mtc-m16c/2017/12.01.20.02</url>
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