This paper deals with the use of radial basis functions neural networks (RBF-NNs) to retrieve sea water optically active parameters (OAPs) from hyperspectral reflectance data. We consider the Multispectral Infrared/Visible Imaging Spectrometer (MIVIS) airborne hyperspectral sensor and we test the capabilities of RBF-NNs on a series of synthetic data representing a typical OAPs statistical distribution of case II waters
Estimation of clorophyll concentration from hyperspectral data: a radial basis functions neural network approach
CORSINI, GIOVANNI;DIANI, MARCO;
2001-01-01
Abstract
This paper deals with the use of radial basis functions neural networks (RBF-NNs) to retrieve sea water optically active parameters (OAPs) from hyperspectral reflectance data. We consider the Multispectral Infrared/Visible Imaging Spectrometer (MIVIS) airborne hyperspectral sensor and we test the capabilities of RBF-NNs on a series of synthetic data representing a typical OAPs statistical distribution of case II watersFile in questo prodotto:
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