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		<citationkey>BuemiMejaJacoGamb:2007:ImSAIm</citationkey>
		<author>Buemi, María Elena,</author>
		<author>Mejail, Marta,</author>
		<author>Jacobo, Julio,</author>
		<author>Gambini, Juliana,</author>
		<affiliation>Universidad de Buenos Aires-Facultad de Ciencias Exactas y Naturales-Departamento de Computacion</affiliation>
		<affiliation>Universidad de Buenos Aires-Facultad de Ciencias Exactas y Naturales-Departamento de Computacion</affiliation>
		<affiliation>Universidad de Buenos Aires-Facultad de Ciencias Exactas y Naturales-Departamento de Computacion</affiliation>
		<affiliation>Universidad de Buenos Aires-Facultad de Ciencias Exactas y Naturales-Departamento de Computacion</affiliation>
		<title>Improvement in SAR Image Classification using Adaptive Stack Filters</title>
		<conferencename>Brazilian Symposium on Computer Graphics and Image Processing, 20 (SIBGRAPI)</conferencename>
		<year>2007</year>
		<editor>Falcão, Alexandre Xavier,</editor>
		<editor>Lopes, Hélio Côrtes Vieira,</editor>
		<booktitle>Proceedings</booktitle>
		<date>Oct. 7-10, 2007</date>
		<publisheraddress>Los Alamitos</publisheraddress>
		<publisher>IEEE Computer Society</publisher>
		<conferencelocation>Belo Horizonte</conferencelocation>
		<keywords>Stack Filter - SAR - Speckle -Classification.</keywords>
		<abstract>Stack filters are a special case of non-linear filters. They have a good performance for filtering images with different types of noise while preserving edges and details. A stack filter decomposes an input image into several binary images according to a set of thresholds. Each binary image is filtered by a Boolean function. The Boolean function that characterizes an adaptive stack filter is optimal and is computed from a pair of images consisting of an ideal noiseless image and its noisy version. In this work the behavior of adaptive stack filters is evaluated for the classification of Synthetic Aperture Radar (SAR) images, which are affected by speckle noise. With this aim it was carried out experiment in which simulated and real images are generated and then filtered with a stack filter trained with one of them. The results of their Maximum Likelihood classification are evaluated and then are compared with the results of classifying the images without previous filtering.</abstract>
		<language>en</language>
		<tertiarytype>Full Paper</tertiarytype>
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