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Proceedings Paper

Automatic description of the Gulf Stream from IR images using neural networks
Author(s): Matthew Lybanon; Eugene J. Molinelli; Michael Flanigan
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Paper Abstract

A system under development for automated interpretation of oceanographic satellite images includes a Gulf Stream description module which uses a neural network which produces coefficients of an empirical orthogonal function (EOF) series representation of the Gulf Stream directly from processed satellite imagery. The Gulf Stream module consists of the EOF software and the neural network with input from an innovative edge detector. The Gulf Stream is the swiftest and most energetic current in the north Atlantic and meanders with a broad spectrum of variability on several spatial and temporal scales. Satellite observations provide a means to observe the Gulf Stream''s shape although clouds in JR imagery and other types of " noise" complicate interpretation. The Gulf Stream shape at any time may be represented as a series of complex EOFs (CEOFs) i. e. principal components which can be truncated after a relatively small number of terms (10) and still describe Gulf Stream shapes well (to within 10 km). These modes can be optimized from initial values with as few as 21 fixes on the position of the Gulf Stream axis using leastsquares estimation. The CEOFs interpolate between spatially intermittent observations of portions of the Gulf Stream as might come from JR imagery with partial cloud cover. The study described here tested whether a credible Gulf Stream can be produced using a neural network (simulated in software) that has inputs derived from

Paper Details

Date Published: 1 August 1990
PDF: 13 pages
Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21173
Show Author Affiliations
Matthew Lybanon, Naval Oceanographic and Atmospheric Research Lab. (United States)
Eugene J. Molinelli, Planning Systems Inc. (United States)
Michael Flanigan, Planning Systems Inc. (United States)

Published in SPIE Proceedings Vol. 1294:
Applications of Artificial Neural Networks
Steven K. Rogers, Editor(s)

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