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dc.contributor.authorRajadell Rojas, Olga
dc.contributor.authorGarcía Sevilla, Pedro
dc.contributor.authorPla, Filiberto
dc.identifier.citationRAJADELL, O., et al. Textural Features for Hyperspectral Pixel Classification. En: Pattern Recognition and Image Analysis: 4th Iberian Conference, IbPRIA 2009 Póvoa de Varzim, Portugal, June 10-12, 2009 Proceedings , p. 208-216. Springer Berlin Heidelberg, 2009. (Lecture Notes in Computer Science; 5524) ISBN 978-3-642-02172-5ca_CA
dc.description.abstractHyperspectral remote sensing provides data in large amounts from a wide range of wavelengths in the spectrum and the possibility of distinguish subtle differences in the image. For this reason, the process of band selection to reduce redundant information is highly recommended to deal with them. Band selection methods pursue the reduction of the dimension of the data resulting in a subset of bands that preserves the most of information. The accuracy is given by the classification performance of the selected set of bands. Usually, pixel classification tasks using grey level values are used to validate the selection of bands. We prove that by using textural features, instead of grey level information, the number of hyperspectral bands can be significantly reduced and the accuracy for pixel classification tasks is improved. Several characterizations based on the frequency domain are presented which outperform grey level classification rates using a very small number of hyperspectral bands.ca_CA
dc.format.extent8 p.ca_CA
dc.publisherSpringer Berlin Heidelbergca_CA
dc.relation.isPartOfSeriesLecture Notes in Computer Science;5524
dc.rightsCopyright Springer Berlin Heidelberg 2009ca_CA
dc.titleTextural Features for Hyperspectral Pixel Classificationca_CA

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  • LSI_Capítols de llibres [17]
    Capítols de llibres escrits per professors del Departament de Llenguatges i Sistemes Informàtics

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