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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)

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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

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

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)

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  1. A. Morel and L. Prieur, “Analysis of variations in ocean color,” Limnol. Oceanogr.22(4), 709–722 (1977). [CrossRef]
  2. R. P. Bukata, J. H. Jerome, K. Y. Kondratyev, and D. V. Pozdnyakov, Optical Properties and Remote Sensing of Inland and Coastal Waters (CRC Press, 1995).
  3. K. L. Carder, R. G. Steward, G. R. Harvey, and P. B. Ortner, “Marine humic and fulvic acids: their effects on remote sensing of ocean chlorophyll,” Limnol. Oceanogr.34(1), 68–81 (1989). [CrossRef]
  4. A. Gitelson, “The peak near 700 nm on radiance spectra of algae and water - relationships of its magnitude and position with chlorophyll concentration,” Int. J. Remote Sens.13(17), 3367–3373 (1992). [CrossRef]
  5. G. Dall’Olmo and A. A. Gitelson, “Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters: experimental results,” Appl. Opt.44(3), 412–422 (2005). [CrossRef] [PubMed]
  6. Y. Z. Yacobi, W. J. Moses, S. Kaganovsky, B. Sulimani, B. C. Leavitt, and A. A. Gitelson, “NIR-red reflectance-based algorithms for chlorophyll-a estimation in mesotrophic inland and coastal waters: Lake Kinneret case study,” Water Res.45(7), 2428–2436 (2011). [CrossRef] [PubMed]
  7. C. Le, Y. Li, Y. Zha, D. Sun, C. Huang, and H. Lu, “A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: the case of Taihu Lake, China,” Remote Sens. Environ.113(6), 1175–1182 (2009). [CrossRef]
  8. W. Yang, B. Matsushita, J. Chen, T. Fukushima, and R. Ma, “An enhanced three-band index for estimating chlorophyll-a in turbid case-II waters: case studies of Lake Kasumigaura, Japan, and Lake Dianchi, China,” IEEE Geosci. Remote Sens. Lett.7(4), 655–659 (2010). [CrossRef]
  9. C. D. Mobley, L. K. Sundman, C. O. Davis, J. H. Bowles, T. V. Downes, R. A. Leathers, M. J. Montes, W. P. Bissett, D. D. R. Kohler, R. P. Reid, E. M. Louchard, and A. Gleason, “Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables,” Appl. Opt.44(17), 3576–3592 (2005). [CrossRef] [PubMed]
  10. C. D. Mobley, “A numerical model for the computation of radiance distributions in natural waters with wind roughened surfaces,” Limnol. Oceanogr.34(8), 1473–1483 (1989). [CrossRef]
  11. C. D. Mobley and L. K. Sundman, Hydrolight 5 Ecolight 5 technical documentation, 1st Ed., (Sequoia Scientific Inc., 2008).
  12. R. L. Lucke, M. Corson, N. R. McGlothlin, S. D. Butcher, D. L. Wood, D. R. Korwan, R. R. Li, W. A. Snyder, C. O. Davis, and D. T. Chen, “Hyperspectral Imager for the Coastal Ocean: instrument description and first images,” Appl. Opt.50(11), 1501–1516 (2011). [CrossRef] [PubMed]
  13. W. J. Moses, J. H. Bowles, R. L. Lucke, and M. R. Corson, “Impact of signal-to-noise ratio in a hyperspectral sensor on the accuracy of biophysical parameter estimation in case II waters,” Opt. Express20(4), 4309–4330 (2012). [CrossRef] [PubMed]
  14. B. C. Gao, M. J. Montes, Z. Ahmad, and C. O. Davis, “Atmospheric correction algorithm for hyperspectral remote sensing of ocean color from space,” Appl. Opt.39(6), 887–896 (2000). [CrossRef] [PubMed]
  15. M. J. Montes, B. C. Gao, and C. O. Davis, “A new algorithm for atmospheric correction of hyperspectral remote sensing data,” Proc. SPIE, Geo-Spatial Image and Data Exploitation II, W. E. Roper (ed.), 4383: 23–30 (2001). [CrossRef]
  16. P. C. Mahalanobis, “On the generalized distance in statistics,” Proc. Natl. Inst. Sci. India2(1), 49–55 (1936).
  17. J. Hedley, C. Roelfsema, and S. Phinn, “Efficient radiative transfer model inversion for remote sensing applications,” Remote Sens. Environ.113(11), 2527–2532 (2009). [CrossRef]
  18. K. V. Mardia, J. T. Kent, and J. M. Bibby, Multivariate Analysis (Academic Press, 2003).
  19. P. Billingsley, Probability and Measure, Third Ed. (John Wiley & Sons, 1995).

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