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		<citationkey>AndradeJrArauSant:2015:AuCrMa</citationkey>
		<title>Automatic Creation of Maps by Using Multiple Data Sensors</title>
		<format>On-line</format>
		<year>2015</year>
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		<size>571 KiB</size>
		<author>Andrade Junior, Edemir Ferreira de,</author>
		<author>Araujo, Arnaldo de Albuquerque,</author>
		<author>Santos, Jefersson Alex dos,</author>
		<affiliation>Universidade Federal de Minas Gerais (UFMG)</affiliation>
		<affiliation>Universidade Federal de Minas Gerais (UFMG)</affiliation>
		<affiliation>Universidade Federal de Minas Gerais (UFMG)</affiliation>
		<editor>Rios, Ricardo Araujo,</editor>
		<editor>Paiva, Afonso,</editor>
		<e-mailaddress>edemir.matcomp@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 28 (SIBGRAPI)</conferencename>
		<conferencelocation>Salvador, BA, Brazil</conferencelocation>
		<date>26-29 Aug. 2015</date>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Work in Progress</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>Data fusion, remote sensing, ensemble of classifiers, mapping.</keywords>
		<abstract>An usual way to acquire information about monitored objects or areas in earth surface is by using remote sensing images. These images can be obtained by different types of sensors (e.g active and passive) and according to the sensor, distinct properties can be observed from the specified data. Typically, these sensor are specialized to encode one or few properties from the object (e.g. spectral and spatial properties), which makes necessary the utilization of diverse and different sensors to obtain many complementary information as possible. Given the amount of information collected, is essential use a capable technique to combine accordingly the different characteristics obtained. The objective of this work, which is in progress, is the development of a framework able to exploit the diversity of these different types of features, extracted from different sensors, to achieve high degrees of accuracy in the creation of thematic maps for the classification task.</abstract>
		<language>en</language>
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