OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 41, Iss. 32 — Nov. 11, 2002
  • pp: 6786–6795

Wavelength Band Selection Method for Multispectral Target Detection

Jörgen Karlholm and Ingmar Renhorn  »View Author Affiliations


Applied Optics, Vol. 41, Issue 32, pp. 6786-6795 (2002)
http://dx.doi.org/10.1364/AO.41.006786


View Full Text Article

Acrobat PDF (244 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

A framework is proposed for the selection of wavelength bands for multispectral sensors by use of hyperspectral reference data. Using the results from the detection theory we derive a cost function that is minimized by a set of spectral bands optimal in terms of detection performance for discrimination between a class of small rare targets and clutter with known spectral distribution. The method may be used, e.g., in the design of multispectral infrared search and track and electro-optical missile warning sensors, where a low false-alarm rate and a high-detection probability for detection of small targets against a clutter background are of critical importance, but the required high frame rate prevents the use of hyperspectral sensors.

© 2002 Optical Society of America

OCIS Codes
(040.3060) Detectors : Infrared
(070.4790) Fourier optics and signal processing : Spectrum analysis
(070.5010) Fourier optics and signal processing : Pattern recognition
(150.5670) Machine vision : Range finding

Citation
Jörgen Karlholm and Ingmar Renhorn, "Wavelength Band Selection Method for Multispectral Target Detection," Appl. Opt. 41, 6786-6795 (2002)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-41-32-6786


Sort:  Author  |  Year  |  Journal  |  Reset

References

  1. J. C. Price, “Band selection procedure for multispectral scanners,” Appl. Opt. 33, 3281–3288 (1994).
  2. A. Kanodia, R. C. Hardie, and R. O. Johnson, “Band selection and performance analysis for multispectral target detectors using truthed Bomem spectrometer data,” in Proc. IEEE National Aerospace Electronics Conference (NAECON), B. Moore, ed., 1, 33–40(1996).
  3. R. C. Hardie, M. Vaidyanathan, and P. F. McManamon, “Spectral band selection and classifier design for a multispectral imaging laser radar,” Opt. Eng. 37, 752–762 (1998).
  4. K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed.(Academic, San Diego, Calif., 1990).
  5. S. Mallat, A Wavelet Tour of Signal Processing (Academic, San Diego, Calif., 1998).
  6. 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).
  7. B. S. Everitt, An Introduction to Latent Variable Models (Chapman and Hall, London, 1984), ISBN 0–412–25310–0.
  8. S. M. Kay, Fundamentals of Statistical Signal Processing: Detection Theory (Prentice-Hall, Englewood Cliffs, N.J., 1998), ISBN 0–13–504135-X.
  9. D. Manolakis, G. Shaw, and N. Keshava, “Comparative analysis of hyperspectral adaptive matched filter detectors,” in Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, S. S. Chen and M. R. Descour, eds., Proc. SPIE 4049, 2–17 (2000).
  10. D. Manolakis and G. Shaw, “Detection algorithms for hyperspectral imaging applications,” IEEE Signal Process. Mag. 19, 29–43 (2002).
  11. G. M. Davis, S. Mallat, and M. Avellaneda, “Greedy adaptive approximations,” J. Const. Appr. 13, 57–98 (1997).
  12. S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Comput. 20, 33–61 (1998).
  13. S. Mallat and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Trans. Signal Process. 41, 3397–3415 (1993).
  14. G. M. Davis, S. Mallat, and Z. Zhang, “Adaptive time-frequency approximations,” Opt. Eng. 33, 2183–2191 (1994).
  15. G. W. Stewart, On the Early History of the Singular Value Decomposition, Technical Report TR-92–31, Dept. of Computer Science, University of Maryland, College Park, 1992, ftp://thales.cs.umd.edu/pub/reports/ehsvd.ps.
  16. A. Berk, L. S. Bernstein, and D. C. Robertson, “modtran, a moderate resolution model for lowtran 7,” Technical Report GL-TR-89–0122, Air Force Geophysics Laboratory, Hanscom Air Force Base, Mass., (1989).
  17. S. F. Cotter, B. D. Rao, and K. Kreutz-Delgado, “Forward sequential algorithms for best basis selection,” IEE Proc. Vision Image Signal Process. 146, 235–244 (1999).
  18. B. K. Natarajan, “Sparse approximate solutions to linear systems,” SIAM J. Comput. 24, 227–234 (1995).
  19. D. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning (Addison-Wesley, Reading, Mass., 1989).

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