Some unmixing problems and algorithms in spectroscopy and hyperspectral imaging

Proceedings - Applied Imagery Pattern Recognition Workshop

Volumn , Issue , 2006, Pages 15-

  Berman, Mark ^a  

^a MACQUARIE UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS; PATTERN RECOGNITION; SPECTROSCOPY;

AUTOMATED IDENTIFICATION; CONSTITUENT MATERIALS; SPECTRAL LIBRARIES; UNMIXING;

HYPERSPECTRAL IMAGING;

EID: 48649083318     PISSN: 15505219     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AIPR.2006.37     Document Type: Conference Paper

References (16)

1. M. Berman, L. Bischof, and J. Huntington. Algorithms and software for the automated identification of minerals using field spectra or hyperspectral imagery. In Proceedings of the 13th International Conference on Applied Geologic Remote Sensing, Vancouver, volume 1, pages 222-232, 1999.
2. M. Berman, H. Kiiveri, R. Lagerstrom, A. Ernst, R. Dunne, and J. Huntington. ICE: A statistical approach to identifying endmembers. IEEE Transactions on Geoscience and Remote Sensing, 42:2085-2095, 2004.
3. M. Berman, A. Phatak, and R. Lagerstrom. ICE: A statistical approach to identifying endmembers in biomedical hyperspectral images. In SPIE Proceedings, volume 5694, pages 62-73, San Jose, U.S.A., 2005.
4. M. Craig. Minimum-volume transforms for remotely sensed data. IEEE Transactions on Geoscience and Remote Sensing, 32:542-552, 1994.
5. A.A. Green, M. Berman, P. Switzer, and M.D. Craig. A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geoscience and Remote Sensing, 26:65-74, 1988.
6. A.A. Green and M.D. Craig. Analysis of aircraft spectrometer data with logarithmic residuals. In G. Vane and A. Goetz, editors, Proceedings of the Airborne Imaging Spectrometer Data Analysis Workshop, pages 111-119. JPL Publ. 85-41, NASA, Pasadena, California, 1985.
7. T. Hastie, A. Buja, and R. Tibshirani. Penalized discriminant analysis. Annals of Statistics, 23:73-102, 1995.
8. J. Jaumot, R. Gargallo, A. de Juan, and R. Tauler. A graphical user-friendly interface for mcr-als: a new tool for multivariate curve resolution in MATLAB. Chemometrics and Intelligent Laboratory Systems, 76:101-110, 2005.
9. J.B. Lee, A.S. Woodyatt, and M. Berman. Enhancement of high spectral resolution remote sensing data by a noise-adjusted principal components transform. IEEE Transactions on Geoscience and Remote Sensing, 28:295-304, 1990.
10. G.J. McLachlan. Discriminant Analysis and Statistical Pattern Recognition. John Wiley & Sons, New York, 1992.
11. A.J. Miller. Subset Selection in Regression. Chapman and Hall, London, 1990.
12. T. Næs, T. Isaksson, T. Fearn, and T. Davies. A User-Friendly Guide to Multivariate Calibration and Classification. NIR Publications, Chichester, UK, 2002.
13. J. Nocedal and S. Wright. Numerical Optimization. Springer, New York, 1999.
14. D.A. Skoog and D.M. West. Principles of Instrumental Analysis. Saunders College, Philadelphia, 2nd edition, 1980.
15. G. Wahba. Spline Models for Observational Data. SIAM, Philadelphia, 1990.
16. M.E. Winter. Fast autonomous spectral endmember determination in hyperspectral data. In Proceedings of the 13th International Conference on Applied Geologic Remote Sensing, Vancouver, volume 2, pages 337-344, 1999.