20 January 2005 Fields of nonlinear regression models for ocean color remote sensing from space
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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.
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Bruno Pelletier, Bruno Pelletier, Robert J. Frouin, Robert J. Frouin, } "Fields of nonlinear regression models for ocean color remote sensing from space", Proc. SPIE 5656, Active and Passive Remote Sensing of the Oceans, (20 January 2005); doi: 10.1117/12.576499; https://doi.org/10.1117/12.576499
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