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		<identifier>8JMKD3MGPAW/3PK8MLE</identifier>
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		<lastupdate>2017:09.11.17.08.18 sid.inpe.br/banon/2001/03.30.15.38 eteduardotavares@gmail.com</lastupdate>
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		<citationkey>TavaresSant:2017:ExInSt</citationkey>
		<title>Exploiting indexing structures for large scale Remote Sensing Image Classification</title>
		<format>On-line</format>
		<year>2017</year>
		<numberoffiles>1</numberoffiles>
		<size>341 KiB</size>
		<author>Tavares, Eduardo de Araújo,</author>
		<author>dos Santos, Jefersson Alex,</author>
		<affiliation>Universidade Federal de Minas Gerais</affiliation>
		<affiliation>Universidade Federal de Minas Gerais</affiliation>
		<editor>Torchelsen, Rafael Piccin,</editor>
		<editor>Nascimento, Erickson Rangel do,</editor>
		<editor>Panozzo, Daniele,</editor>
		<editor>Liu, Zicheng,</editor>
		<editor>Farias, Mylène,</editor>
		<editor>Viera, Thales,</editor>
		<editor>Sacht, Leonardo,</editor>
		<editor>Ferreira, Nivan,</editor>
		<editor>Comba, João Luiz Dihl,</editor>
		<editor>Hirata, Nina,</editor>
		<editor>Schiavon Porto, Marcelo,</editor>
		<editor>Vital, Creto,</editor>
		<editor>Pagot, Christian Azambuja,</editor>
		<editor>Petronetto, Fabiano,</editor>
		<editor>Clua, Esteban,</editor>
		<editor>Cardeal, Flávio,</editor>
		<e-mailaddress>eteduardotavares@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)</conferencename>
		<conferencelocation>Niterói, RJ, Brazil</conferencelocation>
		<date>17-20 Oct. 2017</date>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Work in Progress</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>remote sensing, image indexing.</keywords>
		<abstract>The rapid increase on the volume of data generated by remote sensing systems boosted by the evolution of satellites and the popularization of their imagery has enabled a wide range of new Earth Observation applications. At the same time, it created the challenge of how to efficiently deal with these collections of data. In this work we evaluate the use of indexing techniques for speeding up remote sensing image retrieval aiming automatic large scale geographical mapping in the future. Three CNNs are employed as feature extractors and compared to three low-level features on retrieval tasks performed on a dataset of aerial images with the LSH algorithm. Preliminary results showed a recall level of almost 50% when only roughly 5% of the samples of the evaluated dataset needed to be considered.</abstract>
		<language>en</language>
		<targetfile>2017_sibgrapi camera ready.pdf</targetfile>
		<usergroup>eteduardotavares@gmail.com</usergroup>
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		<citingitemlist>sid.inpe.br/sibgrapi/2017/09.12.13.04 9</citingitemlist>
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