Wavelet techniques for band selection and material classification from hyperspectral data

Nikola S. Subotic; John D. Gorman; Brian J. Thelen

doi:10.1117/12.210896

12 June 1995 Wavelet techniques for band selection and material classification from hyperspectral data

Nikola S. Subotic, John D. Gorman, Brian J. Thelen

Proceedings Volume 2480, Imaging Spectrometry; (1995) https://doi.org/10.1117/12.210896
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

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.

Citation Download Citation

Nikola S. Subotic, John D. Gorman, and Brian J. Thelen "Wavelet techniques for band selection and material classification from hyperspectral data", Proc. SPIE 2480, Imaging Spectrometry, (12 June 1995); https://doi.org/10.1117/12.210896

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