DocumentCode :
1061244
Title :
Determination of CDOM and NPPM absorption coefficient spectra from coastal water remote sensing reflectance
Author :
Alimonte, Davide D. ; Zibordi, Giuseppe ; Berthon, Jean-François
Author_Institution :
Neural Comput. Res. Group, Aston Univ., Birmingham, UK
Volume :
42
Issue :
8
fYear :
2004
Firstpage :
1770
Lastpage :
1777
Abstract :
Multilayer perceptron (MLP) neural network algorithms were developed to retrieve the absorption coefficient spectra of the colored dissolved organic matter (CDOM) and nonpigmented particulate matter (NPPM) from the remote sensing reflectance Rrs of optically complex waters. The two MLP algorithms, consisting of one hidden layer with ten neurons and requiring Rrs at 412, 490, and 665 nm as inputs, were trained with a comprehensive experimental dataset of the Northern Adriatic Sea coastal waters. The products of the proposed regional MLP algorithms showed higher accuracies than regional band-ratio algorithms, and exhibited average uncertainties of 20% and 25% in the determination of CDOM and NPPM absorption coefficients at 412 nm, respectively.
Keywords :
multilayer perceptrons; oceanographic regions; oceanographic techniques; remote sensing; seawater; 412 nm; 490 nm; 665 nm; CDOM absorption coefficient spectra; NPPM absorption coefficient spectra; Northern Adriatic Sea coastal waters; biooptical modeling; coastal water remote sensing reflectance; colored dissolved organic matter; hidden layer; multilayer perceptron; neural network algorithms; nonpigmented particulate matter; ocean color; optically complex waters; regional band-ratio algorithms; Absorption; Multi-layer neural network; Multilayer perceptrons; Neural networks; Optical computing; Optical fiber networks; Optical sensors; Reflectivity; Remote sensing; Sea measurements; Biooptical modeling; neural network; ocean color;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/TGRS.2004.831444
Filename :
1323133
Link To Document :
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