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Proceedings Paper

Fields of nonlinear regression models for ocean color remote sensing from space
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Paper Abstract

The remote sensing of ocean color, a problem that consists in retrieving one or several oceanic variables from top-of-atmosphere spectral reflectance, is considered as a collection of similar inverse problems continuously indexed by the angular variables influencing the observation process. A general solution is proposed in the form of a field of non-linear regression models over the set T of permitted values for the angular variables, i.e., as a map from T to some function space. Each value of the field is a regression model that performs a direct mapping from the top-of-atmosphere reflectance to the geophysical variable(s) of interest. A methodology based on ridge functions is developed to approximate this solution to an arbitrary accuracy, and is applied to the retrieval of the marine reflectance. The developed models are evaluated on synthetic data as well as on actual data originating from the SeaWiFS instrument. The retrievals are achieved with a good performance in terms of accuracy, robustness, and generalization capabilities, suggesting that the methodology might improve the inversion quality over existing techniques.

Paper Details

Date Published: 20 January 2005
PDF: 12 pages
Proc. SPIE 5656, Active and Passive Remote Sensing of the Oceans, (20 January 2005); doi: 10.1117/12.576499
Show Author Affiliations
Bruno Pelletier, Univ. du Havre (France)
Robert J. Frouin, Scripps Institution of Oceanography (United States)

Published in SPIE Proceedings Vol. 5656:
Active and Passive Remote Sensing of the Oceans
Robert J. Frouin; Hiroshi Kawamura; Delu Pan, Editor(s)

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