OSA's Digital Library

Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Editor: Steven A. Burns
  • Vol. 24, Iss. 9 — Sep. 1, 2007
  • pp: 2864–2870

Data interpretation for spectral sensors with correlated bands

Zhipeng Wang, J. Scott Tyo, and Majeed M. Hayat  »View Author Affiliations


JOSA A, Vol. 24, Issue 9, pp. 2864-2870 (2007)
http://dx.doi.org/10.1364/JOSAA.24.002864


View Full Text Article

Enhanced HTML    Acrobat PDF (1447 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

New classes of spectral sensors are emerging that have significant overlap in the band spectral response functions. While conventional sensors such as the Multispectral Thermal Images (MTI) or Landsat may have responses with a few percent overlap between adjacent bands, some of the emerging sensors can have more than 50% correlation among all spectral bands. The traditional geometrical models used to describe spectral data fail when such high levels of correlation exist. In this paper we present a generalized geometrical model that relies on functional analysis. We define a sensor space and a scene space that can be used to characterize the suitability of a sensor for a particular spectral sensing task. We demonstrate that classifiers based on first-order distance and angle metrics fail for sensors with highly correlated bands unless appropriate preprocessing is carried out. We further show that second-order statistical classifiers are largely immune to many of the problems introduced by the correlated band responses.

© 2007 Optical Society of America

OCIS Codes
(100.2960) Image processing : Image analysis
(280.0280) Remote sensing and sensors : Remote sensing and sensors

ToC Category:
Remote sensing and sensors

History
Original Manuscript: November 21, 2006
Manuscript Accepted: May 1, 2007
Published: August 15, 2007

Citation
Zhipeng Wang, J. Scott Tyo, and Majeed M. Hayat, "Data interpretation for spectral sensors with correlated bands," J. Opt. Soc. Am. A 24, 2864-2870 (2007)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-24-9-2864


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. L. O. Jimenez and D. A. Landgrebe, "Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data," IEEE Trans. Syst. Man. Cybern., Part C Appl. Rev. 28, 39-54 (1998).
  2. J. A. Richards and X. Jia, Remote Sensing Digital Image Analysis (Springer, 1999).
  3. M. E. Leeser, P. Belanovic, M. Estlick, M. Gokhale, J. J. Szymanski, and J. P. Theiler, "Applying reconfigurable hardware to the analysis of multispectral and hyperspectral imagery," Proc. SPIE 4480, 100-107 (2002).
  4. D. Manolakis and G. A. Shaw, "Detection algorithms for hyperspectral imaging applications," IEEE Signal Process. Mag. 19, 29-43 (2002). [CrossRef]
  5. S. Krishna, S. Raghavan, G. von Winckel, P. Rotella, A. Stintz, C. P. Morath, D. Le, and S. W. Kennerly, "Two color InAs/InGaAs dots-in-a-well detector with background-limited performance at 91 K," Appl. Phys. Lett. 82, 2574-2576 (2003). [CrossRef]
  6. U. Sakoglu, J. S. Tyo, M. M. Hayat, S. Raghavan, and S. Krishna, "Spectrally adaptive infrared photodetectors using bias-tunable quantum dots," J. Opt. Soc. Am. B 21, 7-17 (2004). [CrossRef]
  7. U. Sakoglu, M. M. Hayat, J. S. Tyo, P. Dowd, S. Annamalai, K. T. Posani, and S. Krishna, "Statistical adaptive sensing using detectors with spectrally overlapping bands," Appl. Opt. 45, 7224-7234 (2006). [CrossRef]
  8. H. H. Barrett and K. J. Myers, Foundations of Image Science (Wiley-Interscience, 2003).
  9. J. W. Boardman, "Analysis, understanding, and visualization of hyperspectral data as convex sets in n-space," Proc. SPIE 2480, 14-22 (1995).
  10. J. S. Tyo, A. Konsolakis, D. I. Diersen, and R. C. Olsen, "Principal-components-based display strategy for spectral imagery," IEEE Trans. Geosci. Remote Sens. 41, 708-720 (2003).
  11. J. W. Boardman, "Automating spectral unmixing of AVIRIS data using convex geometry concepts," in Summaries of the 4th Annual JPL Airborne Geoscience Workshop, R.O.Green, ed. (Jet Propulsion Laboratory, 1993), JPL Pub 93-26, pp. 11-26.
  12. J. C. Harsanyi and C.-I. Chang, "Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach," IEEE Trans. Geosci. Remote Sens. 32, 779-785 (1994). [CrossRef]
  13. G. Healey and D. Slater, "Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions," IEEE Trans. Geosci. Remote Sens. 37, 2706-2717 (1999). [CrossRef]
  14. ASTER spectral library, http://speclib.jpl.nasa.gov/.
  15. Note that the 7-band data has nearly identical classification performance to the 50-band data for second-order classifiers. This artificially high rate is due to the fact that there is no additional noise added in the synthesis process.
  16. G. H. Golub and C. F. van Loan, Matrix Computations (Johns Hopkins U. Press, 1983).
  17. I. Daubecheis, Ten Lectures on Wavelets (SIAM, 1992), pp. 53-106.
  18. N. Keshava and J. F. Mustard, "Spectral unmixing," IEEE Signal Process. Mag. 19, 44-57 (2002).
  19. W. R. Bell, "MTI: overview," Proc. SPIE 4381, 173-183 (2001).
  20. G. Vane, R. Green, T. Chrien, H. Enmark, E. Hansen, and W. Porter, "The airborne visible infrared imaging spectrometer," Remote Sens. Environ. 44, 127-143 (1993). [CrossRef]
  21. J. S. Pearlman, P. S. Barry, C. C. Segal, J. Shepanski, D. Beiso, and S. L. Carman, "Hyperion, a space-based imaging spectrometer," IEEE Trans. Geosci. Remote Sens. 41, 1160-1173 (2003).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited