Textural Features for Hyperspectral Pixel Classification

Authors:
Olga Rajadell;Pedro García-Sevilla;Filiberto Pla
Affiliations:
Depto. Lenguajes y Sistemas Informáticos, Jaume I University, Castellón, Spain 12071;Depto. Lenguajes y Sistemas Informáticos, Jaume I University, Castellón, Spain 12071;Depto. Lenguajes y Sistemas Informáticos, Jaume I University, Castellón, Spain 12071
Venue:
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Year:
2009

Citing 3
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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.