Title :
Estimation of chlorophyll concentration from hyperspectral data: a radial basis functions neural networks approach
Author :
Carta, E. ; Corsini, G. ; Diani, M. ; Grasso, R.
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
Keywords :
geochemistry; geophysical signal processing; image processing; oceanographic techniques; radial basis function networks; remote sensing; MIVIS airborne hyperspectral sensor; Multispectral Infrared/Visible Imaging Spectrometer airborne hyperspectral sensor; OAPs; RBF-NNs; case II waters; chlorophyll concentration; hyperspectral data; radial basis functions neural networks approach; sea water optically active parameters; statistical distribution; synthetic data; Hyperspectral imaging; Hyperspectral sensors; Information retrieval; Infrared image sensors; Infrared spectra; Optical computing; Optical fiber networks; Optical sensors; Radial basis function networks; Reflectivity;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7803-7031-7
DOI :
10.1109/IGARSS.2001.978001
