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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 53, Iss. 13 — May. 1, 2014
  • pp: C25–C31

Continuum fusion solutions for replacement target models in electro-optic detection

Alan Schaum  »View Author Affiliations


Applied Optics, Vol. 53, Issue 13, pp. C25-C31 (2014)
http://dx.doi.org/10.1364/AO.53.000C25


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Abstract

The additive target model is used routinely in the statistical detection of opaque targets, despite its phenomenological inaccuracy. The more appropriate replacement target model is seldom used, because the standard method for producing a detection algorithm from it proves to be intractable, unless narrow restrictions are imposed. Now, the recently developed continuum fusion (CF) methodology allows an expanded solution set to the general replacement target problem. It also provides a mechanism for producing approximate solutions for the standard approach. We illustrate the principles of CF by using them to generate both types of answers for the correct detection model.

© 2014 Optical Society of America

OCIS Codes
(100.5010) Image processing : Pattern recognition
(330.6180) Vision, color, and visual optics : Spectral discrimination
(280.4991) Remote sensing and sensors : Passive remote sensing

History
Original Manuscript: December 3, 2013
Revised Manuscript: March 21, 2014
Manuscript Accepted: March 26, 2014
Published: April 17, 2014

Citation
Alan Schaum, "Continuum fusion solutions for replacement target models in electro-optic detection," Appl. Opt. 53, C25-C31 (2014)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-53-13-C25


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References

  1. D. Manolakis and G. Shaw, “Detection algorithms for hyperspectral imaging applications,” IEEE Signal Process. Mag. 19(1), 29–43 (2002). [CrossRef]
  2. S. Kraut, L. L. Scharf, and R. W. Butler, “The adaptive coherence estimator,” IEEE Trans. Signal Process. 53, 427–438 (2005). [CrossRef]
  3. C. M. Stellman, G. G. Hazel, F. Bucholtz, J. V. Michalowicz, A. Stocker, and W. Schaaf, “Real-time hyperspectral detection and cuing,” Opt. Eng. 39, 1928–1935 (2000). [CrossRef]
  4. D. Manolakis, R. Lockwood, T. Cooley, and J. Jacobson, “Is there a best hyperspectral detection algorithm?” Proc. SPIE 7334, 733402 (2009). [CrossRef]
  5. D. Manolakis, E. Truslow, M. Pieper, T. Cooley, and M. Brueggeman, “Detection algorithms in hyperspectral imaging systems,” IEEE Signal Process. Mag. 31(1), 24–33 (2014). [CrossRef]
  6. A. Stocker and A. Schaum, “Spectrally-selective target detection,” in Proceedings of International Symposium on Spectral Sensing Research, International Society for Photogrammetry and Remote Sensing, B. A. Mandel, ed. (1997), p. 23.
  7. J. W. Boardman, “Automating spectral unmixing of AVIRIS data using convex geometry concepts,” in Fourth Annual JPL Airborne Geoscience Workshop (Jet Propulsion Lab, 1993), Vol. 1, pp. 11–14.
  8. D. Manolakis, C. Siracusa, and G. Shaw, “Hyperspectral subpixel target detection using the linear mixing model,” IEEE Trans. Geosci. Remote Sens. 39, 1392–1409 (2001). [CrossRef]
  9. A. Schaum and A. D. Stocker, “Spectral detection methods: spectral unmixing, correlation processing, and when they are appropriate,” in Proceedings of the Second Annual Symposium on Spectral Sensing Research, July, 1994.
  10. H. Kwon and N. M. Nasrabadi, “Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 43, 388–397 (2005). [CrossRef]
  11. L. Zhang, L. Zhang, D. Tao, and X. Huang, “Sparse transfer manifold embedding for hyperspectral target detection,” IEEE Trans. Geosci. Remote Sens. 52, 1030–1043 (2014). [CrossRef]
  12. L. Scharf, Statistical Signal Processing (Wesley, 1991).
  13. A. P. Schaum and B. J. Daniel, “Continuum fusion methods of spectral detection,” Opt. Eng. 51, 111718 (2012). [CrossRef]
  14. A. Schaum, “The continuum fusion theory of signal detection applied to a bi-modal fusion problem,” Proc. SPIE 8064, 806403 (2011). [CrossRef]
  15. B. J. Daniel and A. Schaum, “Linear log-likelihood ratio (L3R) algorithm for spectral detection,” Proc. SPIE 8048, 804804 (2011). [CrossRef]
  16. S. Kay, “Fundamentals of statistical signal processing,” in Detection Theory (Prentice-Hall, 1998), Vol. 2.

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