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Optics Letters

Optics Letters


  • Editor: Alan E. Willner
  • Vol. 37, Iss. 15 — Aug. 1, 2012
  • pp: 3165–3167

Development of an explicit algorithm for remote sensing estimation of chlorophyll a using symbolic regression

Shilin Tang, Christine Michel, and Pierre Larouche  »View Author Affiliations

Optics Letters, Vol. 37, Issue 15, pp. 3165-3167 (2012)

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The primary mission of ocean color remote sensing is to provide accurate marine bio-optical properties from satellite data. We propose a new algorithm that uses symbolic regression to estimate chlorophyll a (chl a) concentrations from remote sensing reflectance. We compared the accuracy and computational efficiency of the new algorithm to that of the explicit empirical algorithms (OC4v4 and OC4v6), and implicit algorithms based on neural networks or support vector machines (SVM). Results show that the accuracy of the symbolic regression algorithm is higher than that of the OC4 algorithms and comparable to that of implicit algorithms. The improvement is particularly important for high biomass areas (chl a3mgm3) that are often found in optically complex waters. The computational efficiency of the explicit algorithm developed by symbolic regression is comparable to that of the two versions of OC4 algorithms and better than that of implicit algorithms based on SVM. With its good precision and fast processing, the symbolic regression algorithm is a powerful tool for remote sensing of chl a that could be used advantageously in the reprocessing of large datasets.

© 2012 Optical Society of America

OCIS Codes
(010.4450) Atmospheric and oceanic optics : Oceanic optics
(280.4788) Remote sensing and sensors : Optical sensing and sensors

ToC Category:
Remote Sensing and Sensors

Original Manuscript: May 22, 2012
Manuscript Accepted: June 17, 2012
Published: July 25, 2012

Virtual Issues
Vol. 7, Iss. 10 Virtual Journal for Biomedical Optics

Shilin Tang, Christine Michel, and Pierre Larouche, "Development of an explicit algorithm for remote sensing estimation of chlorophyll a using symbolic regression," Opt. Lett. 37, 3165-3167 (2012)

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