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		<doi>10.1080/01431161.2016.1165883</doi>
		<issn>0143-1161</issn>
		<citationkey>NegriDutrSantLu:2016:ExReMe</citationkey>
		<title>Examining region-based methods for land cover classification using stochastic distances</title>
		<year>2016</year>
		<month>Apr.</month>
		<typeofwork>journal article</typeofwork>
		<secondarytype>PRE PI</secondarytype>
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		<author>Negri, Rogério G.,</author>
		<author>Dutra, Luciano Vieira,</author>
		<author>Sant'Anna, Sidnei João Siqueira,</author>
		<author>Lu, D.,</author>
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		<group>DPI-OBT-INPE-MCTI-GOV-BR</group>
		<affiliation>Universidade Estadual Paulista (UNESP)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Michigan State University</affiliation>
		<electronicmailaddress>rogerio.negri@ict.unesp.br</electronicmailaddress>
		<electronicmailaddress>luciano.dutra@inpe.br</electronicmailaddress>
		<electronicmailaddress>sidnei.santanna@inpe.br</electronicmailaddress>
		<journal>International Journal of Remote Sensing</journal>
		<volume>37</volume>
		<number>8</number>
		<pages>1902-1921</pages>
		<secondarymark>A1_PLANEJAMENTO_URBANO_E_REGIONAL_/_DEMOGRAFIA A2_INTERDISCIPLINAR A2_GEOGRAFIA A2_ENGENHARIAS_IV A2_ENGENHARIAS_III A2_ENGENHARIAS_I A2_CIÊNCIAS_AMBIENTAIS A2_CIÊNCIA_DA_COMPUTAÇÃO B1_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA B1_GEOCIÊNCIAS B1_ENGENHARIAS_II B1_CIÊNCIAS_AGRÁRIAS_I B1_BIODIVERSIDADE B2_SAÚDE_COLETIVA B2_ODONTOLOGIA B3_CIÊNCIAS_BIOLÓGICAS_I B3_BIOTECNOLOGIA B5_ASTRONOMIA_/_FÍSICA</secondarymark>
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		<keywords>Graph theory, Pixels, Radar imaging, Remote sensing, Stochastic systems, Support vector machines, Synthetic aperture radar.</keywords>
		<abstract>A recent alternative to standard pixel-based classification of remote-sensing data is region-based classification, which has proved to be particularly useful when analysing high-resolution imagery of complex environments, such as urban areas, or when addressing noisy data, such as synthetic aperture radar (SAR) images. First, following certain criteria, the imagery is decomposed into homogeneous regions, and then each region is classified into a class of interest. The usual method for region-based classification involves using stochastic distances, which measure the distances between the pixel distributions inside an unknown region and the representative distributions of each class. The class, which is at the minimum distance from the unknown region distribution, is assigned to the region and this procedure is termed stochastic minimum distance classification (SMDC). This study reports the use of methods derived from the original SMDC, Support Vector Machine (SVM), and graph theory, with the objective of identifying the most robust and accurate classification methods. The equivalent pixel-based versions of region-based analysed methods were included for comparison. A case study near the Tapajós National Forest, in Pará state, Brazil, was investigated using ALOS PALSAR data. This study showed that methods based on the nearest neighbour, derived from SMDC, and SVM, with a specific kernel function, are more accurate and robust than the other analysed methods for region-based classification. Furthermore, pixel-based methods are not indicated to perform the classification of images with a strong presence of noise, such as SAR images.</abstract>
		<area>SRE</area>
		<language>en</language>
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