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Journal of Electronic Imaging

Land cover and benthic habitat classification using texture features from hyperspectral and multispectral images
Author(s): Vidya B. Manian; Luis O. Jiménez-Rodríguez
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Paper Abstract

A texture-based method for classification of land cover and benthic habitats from hyperspectral and multispectral images is presented. The features considered in this work are a set of statistical and multiresolution texture features, including a 2-level wavelet transform that uses an orthonormal Daubechies filter. These features are computed over spatial extents from each band of the image. A stepwise sequential feature selection process is applied that results in the selection of optimal features from the original feature set. A supervised classification is performed with a distance metric. Results with AVIRIS hyperspectral and IKONOS multispectral images show that texture features perform well under different land cover scenarios and are effective in characterizing the texture information at different wavelengths. Results over coastal regions show that wavelet texture features computed over the reflectance spectrum can accurately detect the benthic classes.

Paper Details

Date Published: 1 April 2007
PDF: 12 pages
J. Electron. Imag. 16(2) 023011 doi: 10.1117/1.2731810
Published in: Journal of Electronic Imaging Volume 16, Issue 2
Show Author Affiliations
Vidya B. Manian, Univ. de Puerto Rico Mayagüez (United States)
Luis O. Jiménez-Rodríguez, Univ. de Puerto Rico Mayagüez (United States)

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