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Neural network training: using non-logarithmic or logarithmic training data for the inversion of ocean colour spectra?

Record type: Article

A bio?optical model coupled with the radiative transfer model Hydrolight was used to create 18,000 synthetic ocean colour spectra corresponding to open ocean and coastal waters. The bio?optical model took into account the optical properties of the three oceanic constituents, chlorophyll?a, suspended non?chlorophyllous particles and coloured dissolved organic matter (CDOM) as well as of normal seawater. The resulting spectra were input into multilayer perceptron neural network algorithms with the aim of computing the original concentrations of chlorophyll?a, non?chlorophyllous particles and CDOM initially input into the bio?optical model. The process of training the neural networks is essential for the accuracy of the inversion the neural net performs on the coupled bio?optical and radiative transfer models. The objective of this paper is to investigate the performance difference of a neural network trained with untransformed as opposed to logarithmically transformed data

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Citation

Dransfeld, S., Tatnall, A.R., Robinson, I. and Mobley, C.D. (2006) Neural network training: using non-logarithmic or logarithmic training data for the inversion of ocean colour spectra? International Journal of Remote Sensing, 27, (10), pp. 2011-2016. (doi:10.1080/01431160500245658).

More information

Submitted date: 16 March 2005
Published date: 20 May 2006

Identifiers

Local EPrints ID: 35371
URI: http://eprints.soton.ac.uk/id/eprint/35371
ISSN: 0143-1161
PURE UUID: 650c9f13-c173-4c73-85c0-a36315afd862

Catalogue record

Date deposited: 05 Jun 2006
Last modified: 17 Jul 2017 15:48

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