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		<isbn>978-85-17-00088-1</isbn>
		<label>59454</label>
		<citationkey>SantosGlerVell:2017:MoLiMi</citationkey>
		<title>Modelo linear de mistura espectral com dados de sensores de diferentes resoluções espaciais do CBERS4</title>
		<format>Internet</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
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		<size>802 KiB</size>
		<author>Santos, João Flávio Costa dos,</author>
		<author>Gleriani, José Marinaldo,</author>
		<author>Velloso, Sidney Geraldo Silveira,</author>
		<electronicmailaddress>joao.flavio@ufv.br</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>5202-5208</pages>
		<booktitle>Anais</booktitle>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<abstract>Spectral mixture models have been used for a wide variety of applications and the linear model has been widely used. In order to improve the performance of the models, new approaches have been used as nonlinear (logistic) functions in MLP (Multi-Layer Perceptron) networks, nonlinear modeling expressing the interaction of the photon with neighboring covers, or use of endmenbers from spectral libraries. With the launch of the CBERS-4, and the presence of PAN-MS and MUX sensors with three channels with the same spectral resolution, but with different spatial resolutions, there are new research possibilities. We have the opportunity to verify if the use of purer endmenbers, where data acquired with the same illumination/observation geometry and atmospheric conditions, can improve the fit of the model, or if the spatial resolution interferes with the fit of the model. It was verified that the use of endmembers collected in PAN-MS images used in the MUX image did not improve the fit of the model, probably by forcing the modeling of non-existent mixed pixel values in 20m spatial resolution, linear mixture model generated with PAN-MS data resulted in a better fit.</abstract>
		<area>SRE</area>
		<type>CBERS</type>
		<language>pt</language>
		<targetfile>59454.pdf</targetfile>
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