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		<isbn>978-85-17-00088-1</isbn>
		<label>59294</label>
		<citationkey>SilvaPoppBaptMore:2017:InMéCo</citationkey>
		<title>Influência dos métodos de correção atmosférica FLAASH e QUAC na determinação do índice NDBSI de solos tropicais mediante dados hiperespectrais do sensor AVIRIS</title>
		<format>Internet</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
		<numberoffiles>1</numberoffiles>
		<size>1852 KiB</size>
		<author>Silva, Daniela Pereira da,</author>
		<author>Poppiel, Raul Roberto,</author>
		<author>Baptista, Gustavo Macedo de Mello,</author>
		<author>Moreira, Emmanuel Carlos G.,</author>
		<electronicmailaddress>daniela.pereira.ufg@gmail.com</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>4134-4141</pages>
		<booktitle>Anais</booktitle>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<abstract>Hyperspectral remote sensing allows to obtain information about a target in the natural environment in different regions of the spectrum, which allows a wide range of data on its situation, and it is possible to extract the spectral features of reflectance / absorption that identify the composition of the materials in pictures. There are several methods to perform the atmospheric correction in hyperspectral data. The objective of this study was to verify the influence of the atmospheric correction on exposed soil of the municipality of São João dAliança, Goiás, using the NDBSI spectral index in AVIRIS images. To determine the influence of the atmospheric correction of the images processed by FLAASH and QUAC, including the uncorrected radiance image, the NDBSI index applied to the soils of the study area was used. Then, Pearson correlation coefficients were determined. The highest correlation between the radiance data and the atmospheric correction algorithms was for the FLAASH method, followed by the QUAC. Changes were observed in the inclination of the curves related to the spatial variation of the targets along the transect in the image. Intermediate values are related to areas of partially exposed soil, or partially covered. Although the curve of the QUAC method is closer to that of radiance, the FLAASH values are more correlated. The FLAASH method showed a slightly superior performance to the QUAC, since the data had a highly linear relationship with the radiance data in determining the NDBSI index.</abstract>
		<area>SRE</area>
		<type>Radiometria e sensores</type>
		<language>pt</language>
		<targetfile>59294.pdf</targetfile>
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