SAR ocean image inversion using neural network
Forgia, V.L. ; Nirchio, F. ; Pasquariello, G. ; et al.
Jan - 1995
DOI: 10.1109/IGARSS.1995.521104
ISBN : 0-7803-2567-2

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type: Conference Proceedings

Abstract
The objective of this paper is to present a methodological approach devoted to the sea state parameters extraction from ERS-1 SAR data. Wave parameters (direction and wavelength) retrieval is not a straightforward task, due to the nonlinearity of mapping the sea surface into the detected image. To overcome these difficulties a neural network approach has been tested. A SAR ocean image simulator has been used to create a set of image windows for the learning of a multilayers network and the trained network has been applied to a set of independent examples corresponding to various wave directions and wavelengths. The results, obtained on simulated data, seems to be encouraging and independent of linearity or nonlinearity of the wave data

keywords : ERS-1,Extraterrestrial measurements,Image retrieval,Linearity,Multi-layer neural network,Neural networks,Oceans,Parameter extraction,Remote monitoring,SAR ocean image inversion,Sea measurements,Sea surface,Surface waves,Testing,Weather forecasting,direction,feedforward neural net,feedforward neural nets,geophysical signal processing,geophysics computing,measurement technique,multilayer network,neural network,nonlinearity,ocean wave,ocean waves,oceanographic techniques,parameters extraction,radar applications,radar imaging,radar remote sensing,remote sensing,remote sensing by radar,retrieval method,sea state,sea surface,spaceborne radar,synthetic aperture radar,trained network,wavelength