2 September 1993 Neural networks for eddy detection in satellite imagery
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For several years the Naval Research Laboratory has worked toward the development of automated techniques for the analysis and interpretation of satellite oceanographic imagery. These techniques are combined to form the Semi-Automated Mesoscale Analysis System (SAMAS), which produces mesoscale charts of the Gulf Stream region. A key requirement of SAMAS is the ability to define location and size of mesoscale features known as eddies. A new method consists of a data reduction step using the Fourier power spectrum and a classification step using a neural network to define the presence or absence of eddies in satellite imagery. The original imagery is divided into chips, each of which overlaps the next by half the chip size. For each chip, a magnitude and direction of the maximum image 'energy' are computed from the local power spectrum. These magnitudes and directions are then used as the inputs into the neural network. The neural network has been successfully trained to distinguish 'warm eddy' and 'no-warm eddy' areas in the imagery. Accuracy of the method is shown to be high enough to produce useful results.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sarah H. Peckinpaugh, Juanita R. Chase, and Ronald J. Holyer "Neural networks for eddy detection in satellite imagery", Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); doi: 10.1117/12.152544; https://doi.org/10.1117/12.152544


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