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		<secondarykey>INPE-16128-PRE/10731</secondarykey>
		<isbn>85-17-00017-X</isbn>
		<citationkey>LeonardiSouzFons:2003:CoDeAl</citationkey>
		<title>Comparação do desempenho de algorítmos de normalização radiométrica de imagens de satélite</title>
		<format>CD-ROM, Online.</format>
		<year>2003</year>
		<secondarytype>PRE CN</secondarytype>
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		<author>Leonardi, Silvia Shizue,</author>
		<author>Souza, Sonia Mara,</author>
		<author>Fonseca, Leila Maria Garcia,</author>
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		<group>DPI-INPE-MCT-BR</group>
		<group>DEA-INPE-MCT-BR</group>
		<group>DPI-INPE-MCT-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>
		<electronicmailaddress>silvia@dpi.inpe.br</electronicmailaddress>
		<electronicmailaddress>sonia@dea.inpe.br</electronicmailaddress>
		<electronicmailaddress>leila@dpi.inpe.br</electronicmailaddress>
		<editor>Epiphanio, José Carlos Neves,</editor>
		<editor>Fonseca, Leila Maria Garcia,</editor>
		<e-mailaddress>silvia@dpi.inpe.br</e-mailaddress>
		<conferencename>Simpósio Brasileiro de Sensoriamento Remoto, 11 (SBSR).</conferencename>
		<conferencelocation>Belo Horizonte</conferencelocation>
		<date>5-10 abr. 2003</date>
		<publisher>INPE</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<pages>2063 - 2069</pages>
		<booktitle>Anais</booktitle>
		<organization>Instituto Nacional de Pesquisas Espaciais</organization>
		<transferableflag>1</transferableflag>
		<keywords>remote sensing, image processing, radiometric normalization.</keywords>
		<abstract>At present, the applications involving satellite images in multi-temporal approaches have become very frequently for analyzing natural, environmental and meteorological resources. However, multitemporal set of images present radiometric differences, which can introduce errors in the data interpretation. Several radiometric correction methods have been proposed. In this paper, four of these methods were chosen to be tested and compared: RABIM (Automatic registration based on Mutual Information), ASCR (Automatic Sacttergram-Controlled Regression), UMV (Averages and Variances Normalization) and Radiometric Rectification, proposed by Hall et al. (1991). The main goal of all these four methods is to normalize radiometrically the multitemporal image set (adjust and reference), in order to make possible radiometric comparisons between them.</abstract>
		<area>SRE</area>
		<type>Processamento de Imagens Digitais / Digital Image Processing</type>
		<language>pt</language>
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