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

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
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11568/191487
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact