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		<citationkey>MarettoKörCasFonSan:2016:SpAtSe</citationkey>
		<title>Spectral attributes selection based on data mining for remote sensing image classification</title>
		<year>2016</year>
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		<author>Maretto, Raian,</author>
		<author>Körting, Thales Sehn,</author>
		<author>Castejon, Emiliano Ferreira,</author>
		<author>Fonseca, Leila Maria Garcia,</author>
		<author>Santos, Rafael Duarte Coelho dos,</author>
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		<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>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
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		<electronicmailaddress>thales.korting@inpe.br</electronicmailaddress>
		<electronicmailaddress>emiliano.castejon@inpe.br</electronicmailaddress>
		<electronicmailaddress>leila.fonseca@inpe.br</electronicmailaddress>
		<electronicmailaddress>rafael.santos@inpe.br</electronicmailaddress>
		<conferencename>Workshop de Computação Aplicada, 16 (WORCAP)</conferencename>
		<conferencelocation>São José dos Campos, SP</conferencelocation>
		<date>25-26 out.</date>
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		<abstract>Remote sensing images are a rich source of information for studying large-scale geographic areas. The increased accessibility of the new generation high-spatial resolution multispectral sensors has improved the level of complexity required in the analysis techniques. In particular, many traditional per-pixel analysis may not be suitable to high-spatial resolution imagery, due to its high-frequency components and the horizontal layover caused by off-nadir look angles [Im et al. 2008]. Aiming to overcome this problem, in the last decades, several approaches and platforms have been developed with algorithms that consider contextual information and pixel region properties [Körting et al. 2013; Syed et al. 2005; Walter 2004]. Current software can extract several statistical, spatial, color, texture or topological attributes. However, most of them often do not help to distinguish between the classes of interest, due to its high correlation. Thus, the attributes selection phase often relies on ad hoc decisions about what of them can better describe the classes. The huge number of attributes available makes a detailed exploratory time-consuming and dependent on expertise [Körting et al. 2013]. Many works have proved that data mining techniques can be useful to this purpose [Dash and Liu 1997; Kohavi and Kohavi 1997; Laliberte et al. 2012]. In this context, the main objective of this work is to analyze the correlation of the spectral attributes between a set of classes of interest, in order to verify what of them best distinguish these classes. A case study is presented over a small region of the city of São José dos Campos, using a WorldView-2 image. It is important to emphasize that although this study is in a preliminary stage, the results are promising and reached improvements in the accuracy of the classification, even as a good reduction in the computational time.</abstract>
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