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Optics Letters

Optics Letters


  • Editor: Alan E. Willner
  • Vol. 35, Iss. 21 — Nov. 1, 2010
  • pp: 3601–3603

Enhanced material classification using turbulence-degraded polarimetric imagery

Milo W. Hyde, IV, Jason D. Schmidt, Michael J. Havrilla, and Stephen C. Cain  »View Author Affiliations

Optics Letters, Vol. 35, Issue 21, pp. 3601-3603 (2010)

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An enhanced material-classification algorithm using turbulence-degraded polarimetric imagery is presented. The proposed technique improves upon an existing dielectric/metal material-classification algorithm by providing a more detailed object classification. This is accomplished by redesigning the degree-of-linear-polarization priors in the blind-deconvolution algorithm to include two subclasses of metals—an aluminum group classification (includes aluminum, copper, gold, and silver) and an iron group classification (includes iron, titanium, nickel, and chromium). This new classification provides functional information about the object that is not provided by existing dielectric/metal material classifiers. A discussion of the design of these new degree-of-linear-polarization priors is provided. Experimental results of two painted metal samples are also provided to verify the algorithm’s accuracy.

© 2010 Optical Society of America

OCIS Codes
(010.1330) Atmospheric and oceanic optics : Atmospheric turbulence
(100.1455) Image processing : Blind deconvolution
(280.4991) Remote sensing and sensors : Passive remote sensing
(110.5405) Imaging systems : Polarimetric imaging

ToC Category:
Remote Sensing and Sensors

Original Manuscript: June 15, 2010
Revised Manuscript: September 27, 2010
Manuscript Accepted: October 2, 2010
Published: October 21, 2010

Milo W. Hyde, IV, Jason D. Schmidt, Michael J. Havrilla, and Stephen C. Cain, "Enhanced material classification using turbulence-degraded polarimetric imagery," Opt. Lett. 35, 3601-3603 (2010)

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