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Textural Features for Hyperspectral Pixel Classification

  • Olga Rajadell
  • Pedro García-Sevilla
  • Filiberto Pla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5524)

Abstract

Hyperspectral 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.

Keywords

Grey Level Textural Feature Hyperspectral Image Wavelet Packet Wavelet Decomposition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Olga Rajadell
    • 1
  • Pedro García-Sevilla
    • 1
  • Filiberto Pla
    • 1
  1. 1.Depto. Lenguajes y Sistemas InformáticosJaume I UniversityCastellónSpain

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