On The Spectral Correlation Structure of Hyperspectral Imaging Data

@article{Manolakis2008OnTS,
  title={On The Spectral Correlation Structure of Hyperspectral Imaging Data},
  author={Dimitris Manolakis and Ronald B. Lockwood and Thomas W. Cooley},
  journal={IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium},
  year={2008},
  volume={2},
  pages={II-581-II-584}
}
Spectral correlation, as quantified by the elements of the covariance matrix, plays a prominent role in the development of optimum statistical algorithms for hyperspectral data exploitation. Indeed, the most useful statistical models for hyperspectral image modeling, namely the multivariate normal distribution and the multivariate t-distribution, are parameterized by the spectral covariance matrix. The inverse of the covariance matrix, however, also has important interpretations. In this paper… CONTINUE READING

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Showing 1-5 of 5 references

Modeling hyperspectral imaging data using elliptically contoured distributions

  • D. Marden, D. Manolakis
  • 0 50 100 150 200 0 100 200 300 400 500 S…
  • 2003
1 Excerpt

Target detection algorithms for hyperspectral imaging application

  • D.Manolakis, D.Marden, G. Shaw
  • Lincoln Laboratory Journal, vol. 14, no. 1, pp…
  • 2003
1 Excerpt

Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering and Array

  • D. G. Manolakis, V. K. Ingle, S. M. Kogon
  • 2000
2 Excerpts

Remote Sensing: Models and Methods for Image Processing

  • R. A. Schowengerdt
  • 1997
2 Excerpts

Multivariate Analysis, Marcel Dekker, Inc., NewYork

  • A. Kshirsagar
  • 1972
1 Excerpt

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