Retrieval of sea water optically active parameters from hyperspectral data by means of generalized radial basis function neural networks

In this paper, we present a new methodology for estimating the concentration of sea water optically active constituents from remotely sensed hyperspectral data, based on generalized radial basis function neural networks (GRBF-NNs). This family of NNs is particularly suited to approximate relationships like those between hyperspectral reflectance data and the concentrations of optically active constituents of the water body, which are highly nonlinear, especially in case II waters. Three main water constituents are taken into account: phytoplankton, nonchlorophyllous particles, and yellow substance. Each parameter is estimated by means of a specific, multi-input single-output GRBF-NN. We adopt a recently proposed network learning strategy based on the combined use of the regression tree procedure and forward selection. The effectiveness of this approach, which is completely general and can be easily applied to any hyperspectral sensor, is proved using data simulated with an ocean color model over the channels of the medium resolution imaging spectrometer (MERIS), the new generation ESA sensor to be launched in 2001. We define the estimation algorithms over waters of cases I, II, and I+II and compare their performance with that of classical band-ratio, single-band, and multilinear algorithms. Generally, the GRBF-NN algorithms outperform the classical ones, except for the multilinear over case I waters. A particular improvement is over case II waters, where the mean square error (MSE) can be reduced by one or two orders of magnitude over the error of multilinear and band-ratio algorithms, respectively. We also discuss briefly, with an example, the noise filtering effects of the network and the effects of the size of the training set.