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		<doi>10.1016/j.patcog.2013.04.001</doi>
		<issn>0031-3203</issn>
		<label>scopus</label>
		<citationkey>TorresSantFreiFrer:2014:SpRePo</citationkey>
		<title>Speckle reduction in polarimetric SAR imagery with stochastic distances and nonlocal means</title>
		<year>2014</year>
		<month>Jan.</month>
		<typeofwork>journal article</typeofwork>
		<secondarytype>PRE PI</secondarytype>
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		<author>Torres, Leonardo,</author>
		<author>Sant'Anna, Sidnei João de Siqueira,</author>
		<author>Freitas, Corina da Costa,</author>
		<author>Frery, Alejandro C.,</author>
		<group>SER-SRE-SPG-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>
		<affiliation>Universidade Federal de Alagoas - UFAL, CPMAT-LaCCAN, Av. Lourival Melo Mota, s/n, Tabuleiro dos Martins, 57072-900 Maceió - AL, Brazil</affiliation>
		<e-mailaddress>marcelo.pazos@inpe.br</e-mailaddress>
		<journal>Pattern Recognition</journal>
		<volume>47</volume>
		<number>1, SI</number>
		<pages>141-157</pages>
		<secondarymark>A1_ENGENHARIAS_III A1_INTERDISCIPLINAR A1_CIÊNCIA_DA_COMPUTAÇÃO A1_GEOCIÊNCIAS A1_ENGENHARIAS_IV A2_MEDICINA_II A2_MATEMÁTICA_/_PROBABILIDADE_E B1_ASTRONOMIA_/_FÍSICA B1_CIÊNCIAS_BIOLÓGICAS_I B2_CIÊNCIAS_BIOLÓGICAS_III C_BIODIVERSIDADE</secondarymark>
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		<versiontype>finaldraft</versiontype>
		<keywords>Hypothesis testing, Information theory, Multiplicative noise, PolSAR imagery, Speckle reduction, Stochastic distances, Synthetic aperture radar.</keywords>
		<abstract>This paper presents a technique for reducing speckle in Polarimetric Synthetic Aperture Radar (PolSAR) imagery using nonlocal means and a statistical test based on stochastic divergences. The main objective is to select homogeneous pixels in the filtering area through statistical tests between distributions. This proposal uses the complex Wishart model to describe PolSAR data, but the technique can be extended to other models. The weights of the location-variant linear filter are function of the p-values of tests which verify the hypothesis that two samples come from the same distribution and, therefore, can be used to compute a local mean. The test stems from the family of (h-Æsymbol) divergences which originated in Information Theory. This novel technique was compared with the Boxcar, Refined Lee and IDAN filters. Image quality assessment methods on simulated and real data are employed to validate the performance of this approach. We show that the proposed filter also enhances the polarimetric entropy and preserves the scattering information of the targets. © 2013 Elsevier Ltd. All rights reserved.</abstract>
		<area>SRE</area>
		<language>en</language>
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