12 June 1995 Wavelet techniques for band selection and material classification from hyperspectral data
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We describe a band selection process based on wavelet analysis of hyperspectral data which naturally decomposes the data into sub-bands. Wavelet analysis allows the control of the position, resolution, and envelope of the specific spectral sub-bands which will be selected. The sub-band sets are selected to maximize the Kullback-Liebler distance between specific classes of materials for a specific dimensionality contraint or discrimination performance goal. A sequential construction of the sub-band sets is used as an approximation to the global maximization operation over all possible sub-band sets. A max/min strategy is also introduced to provide a robust framework for sub-band selection when faced with multiple materials. We show band selection and material classification results of this technique applied to Fourier transform spectrometer data.
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Nikola S. Subotic, Nikola S. Subotic, John D. Gorman, John D. Gorman, Brian J. Thelen, Brian J. Thelen, } "Wavelet techniques for band selection and material classification from hyperspectral data", Proc. SPIE 2480, Imaging Spectrometry, (12 June 1995); doi: 10.1117/12.210896; https://doi.org/10.1117/12.210896

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