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 waters
2001
0780370317
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/191487
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