Large Spectral Library Problem

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Hyperspectral imaging produces a spectrum or vector at each image pixel. These spectra can be used to identify materials present in the image. In some cases, spectral libraries representing atmospheric chemicals or ground materials are available. The challenge is to determine if any of the library chemicals or materials exist in the hyperspectral image. The number of spectra in these libraries can be very large, far exceeding the number of spectral channels collected in the ¯eld. Suppose an image pixel contains a mixture of p spectra from the library. Is it possible to uniquely identify these p spectra? We address ... continued below

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Chilton, Lawrence K. & Walsh, Stephen J. October 3, 2008.

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Hyperspectral imaging produces a spectrum or vector at each image pixel. These spectra can be used to identify materials present in the image. In some cases, spectral libraries representing atmospheric chemicals or ground materials are available. The challenge is to determine if any of the library chemicals or materials exist in the hyperspectral image. The number of spectra in these libraries can be very large, far exceeding the number of spectral channels collected in the ¯eld. Suppose an image pixel contains a mixture of p spectra from the library. Is it possible to uniquely identify these p spectra? We address this question in this paper and refer to it as the Large Spectral Library (LSL) problem. We show how to determine if unique identi¯cation is possible for any given library. We also show that if p is small compared to the number of spectral channels, it is very likely that unique identi¯cation is possible. We show that unique identi¯cation becomes less likely as p increases.

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  • Report No.: PNNL-17810
  • Grant Number: AC05-76RL01830
  • DOI: 10.2172/963240 | External Link
  • Office of Scientific & Technical Information Report Number: 963240
  • Archival Resource Key: ark:/67531/metadc931180

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  • October 3, 2008

Added to The UNT Digital Library

  • Nov. 13, 2016, 7:26 p.m.

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  • Dec. 9, 2016, 8:13 p.m.

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Chilton, Lawrence K. & Walsh, Stephen J. Large Spectral Library Problem, report, October 3, 2008; Richland, Washington. (digital.library.unt.edu/ark:/67531/metadc931180/: accessed September 21, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.