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		<isbn>978-85-17-00088-1</isbn>
		<label>59229</label>
		<citationkey>JijonCentMach:2017:ClDaHi</citationkey>
		<title>Classificação de dados hiperespectrais AVIRIS baseada na codificação binária</title>
		<format>Internet</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
		<numberoffiles>1</numberoffiles>
		<size>679 KiB</size>
		<author>Jijon, Mario Ernesto,</author>
		<author>Centeno, Jorge Antonio Silva,</author>
		<author>Machado, Alvaro Muriel Lima,</author>
		<electronicmailaddress>majijpa@hotmail.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>1177-1185</pages>
		<booktitle>Anais</booktitle>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<abstract>Hyperspectral sensors provide images in hundreds of spectral bands that allows to discriminate objects with more details. But, the number of available training samples is limited. Thus, the dimensionality reduction is very important for the classification of high dimensional data.  The approach developed in this work was the binary coding that was applied in hyperspectral data for dimensionality reduction.  This encoding is based on a simple code and applied to a spectrum of reflectance pixel by pixel. Furthermore it seeks to develop a spectral representation that facilitates the identification of classes and their separability through the establishment of spectral libraries that stocks a number of spectra.  For that, several experiments that allow the comparison of land cover classifications were tested.  The alternatives that were performed on binary encoding were applied to a number of spectral regions (spectral libraries).  Each alternative has been tested to a binary code through one to various thresholds.  The results of these experiments show that the use of the binary encoding based on three thresholds and by regions allow the thematic mapping image classification and also reduce the dimensionality of hyperspectral data, being then more efficient than the use of  one threshold for all the bands.</abstract>
		<area>SRE</area>
		<type>Geoprocessamento e aplicações</type>
		<language>pt</language>
		<targetfile>59229.pdf</targetfile>
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