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Land cover and benthic habitat classification using texture features from hyperspectral and multispectral images

[+] Author Affiliations
Vidya Manian

University of Puerto Rico, Department of Electrical and Computer Engineering, Mayagüez, Puerto Rico 00681-9042

Luis O. Jimenez

University of Puerto Rico, Department of Electrical and Computer Engineering, Mayagüez, Puerto Rico 00681-9042

J. Electron. Imaging. 16(2), 023011 (April 24, 2007). doi:10.1117/1.2731810
History: Received February 17, 2006; Revised January 02, 2007; Accepted January 02, 2007; Published April 24, 2007
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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.

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© 2007 SPIE and IS&T

Citation

Vidya Manian and Luis O. Jimenez
"Land cover and benthic habitat classification using texture features from hyperspectral and multispectral images", J. Electron. Imaging. 16(2), 023011 (April 24, 2007). ; http://dx.doi.org/10.1117/1.2731810


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