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Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 21, Iss. 18 — Sep. 9, 2013
  • pp: 21306–21316

Improving the retrieval of water inherent optical properties in noisy hyperspectral data through statistical modeling

David B. Gillis, Jeffrey H. Bowles, and Wesley J. Moses  »View Author Affiliations


Optics Express, Vol. 21, Issue 18, pp. 21306-21316 (2013)
http://dx.doi.org/10.1364/OE.21.021306


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Abstract

The use of the Mahalanobis distance in a lookup table approach to retrieval of in-water Inherent Optical Properties (IOPs) led to significant improvements in the accuracy of the retrieved IOPs, as high as 50% in some cases, with an average improvement of 20% over a wide range of case II waters. Previous studies have shown that inherent noise in hyperspectral data can cause significant errors in the retrieved IOPs. For LUT-based retrievals that rely on spectrum matching, the particular metric used for spectral comparisons has a significant effect on the accuracy of the results, especially in the presence of noise in the data. In this study, we have compared the Euclidean distance and the Mahalanobis distance as metrics for spectral comparison. In addition to providing justification for the preference of the Mahalanobis Distance over the Euclidean Distance, we have also included a statistical description of noisy hyperspectral data.

© 2013 OSA

OCIS Codes
(000.5490) General : Probability theory, stochastic processes, and statistics
(010.4450) Atmospheric and oceanic optics : Oceanic optics
(110.4280) Imaging systems : Noise in imaging systems
(280.0280) Remote sensing and sensors : Remote sensing and sensors

ToC Category:
Atmospheric and Oceanic Optics

History
Original Manuscript: June 10, 2013
Revised Manuscript: August 1, 2013
Manuscript Accepted: August 7, 2013
Published: September 4, 2013

Virtual Issues
Vol. 8, Iss. 10 Virtual Journal for Biomedical Optics

Citation
David B. Gillis, Jeffrey H. Bowles, and Wesley J. Moses, "Improving the retrieval of water inherent optical properties in noisy hyperspectral data through statistical modeling," Opt. Express 21, 21306-21316 (2013)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-21-18-21306


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