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		<doi>10.1007/978-3-642-33275-3_98</doi>
		<issn>0302-9743</issn>
		<label>lattes: 9840759640842299 2 NegriDutrSant:2012:StApMi</label>
		<citationkey>NegriDutrSant:2012:StApMi</citationkey>
		<title>Stochastic Approaches of Minimum Distance Method for Region Based Classification</title>
		<year>2012</year>
		<secondarytype>PRE PI</secondarytype>
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		<size>2090 KiB</size>
		<author>Negri, Rogerio Galanti,</author>
		<author>Dutra, Luciano Vieira,</author>
		<author>Sant'Anna, Sidnei JoÃo Siqueira,</author>
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		<group>DPI-OBT-INPE-MCTI-GOV-BR</group>
		<group>DPI-OBT-INPE-MCTI-GOV-BR</group>
		<group>DPI-OBT-INPE-MCTI-GOV-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></electronicmailaddress>
		<electronicmailaddress>dutra@dpi.inpe.br</electronicmailaddress>
		<e-mailaddress>dutra@dpi.inpe.br</e-mailaddress>
		<journal>Lecture Notes in Computer Science</journal>
		<volume>7441</volume>
		<number>2012</number>
		<pages>797-804</pages>
		<secondarymark>C_ADMINISTRAÇÃO,_CIÊNCIAS_CONTÁBEIS_E_TURISMO C_ASTRONOMIA_/_FÍSICA C_BIOTECNOLOGIA B5_CIÊNCIAS_BIOLÓGICAS_I C_CIÊNCIAS_BIOLÓGICAS_III B1_CIÊNCIAS_SOCIAIS_APLICADAS_I B3_DIREITO C_EDUCAÇÃO C_ENGENHARIAS_I B3_ENGENHARIAS_II C_ENGENHARIAS_III B4_ENSINO_DE_CIÊNCIAS_E_MATEMATICA B5_GEOCIÊNCIAS B2_INTERDISCIPLINAR B5_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA B3_MEDICINA_I B3_MEDICINA_II B3_PSICOLOGIA</secondarymark>
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		<contenttype>External Contribution</contenttype>
		<versiontype>finaldraft</versiontype>
		<keywords>Classification process, Image simulations, Minimum average distance, Minimum distance, Region-based, Remote sensing image classification, Second variation, Simple approach, Simulation studies, Stochastic approach, stochastic distances, Imagens de Sensoriamento Remoto, Reconhecimento de Padroes, Segmentação de imagens.</keywords>
		<abstract>Normally remote sensing image classification is performed pixelwise which produces a noisy classification. One way of improving such results is dividing the classification process in two steps. First, uniform regions by some criterion are detected and afterwards each unlabeled region is assigned to class of the "nearest" class using a so-called stochastic distance. The statistics are estimated by taking in account all the reference pixels. Three variations are investigated. The first variation is to assign to the unlabeled region a class that has the minimum average distance between this region and each one of reference samples of that class. The second is to assign the class of the closest reference sample. The third is to assign the most frequent class of the k closest reference regions. A simulation study is done to assess the performances. The simulations suggested that the most robust and simple approach is the second variation.</abstract>
		<area>SRE</area>
		<language>en</language>
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		<citingitemlist>sid.inpe.br/mtc-m21/2012/07.13.15.00.20 2</citingitemlist>
		<dissemination>WEBSCI; PORTALCAPES; COMPENDEX.</dissemination>
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		<notes>17th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2012</notes>
		<notes>Buenos Aires</notes>
		<notes>3 September 2012through6 September 2012</notes>
		<notes>Code92323</notes>
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		<url>http://plutao.sid.inpe.br/rep-/dpi.inpe.br/plutao/2012/11.28.19.14.50</url>
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