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Proceedings Paper

Neural network algorithm for sea-ice edge classification
Author(s): Jun-Dong Park; Sami M. Alhumaidi; W. Linwood Jones; Shannon Ferguson
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Paper Abstract

The NASA Scatterometer, launched in August 1996, is designed to measure wind vectors over ice-free oceans. To prevent contamination of the wind measurements, by the presence of sea ice, algorithms based on neural network technology have been developed to classify ice-free ocean surfaces. Neural networks trained using polarized alone and polarized plus multi-azimuth `look' Ku-band backscatter are described. Algorithm skill in locating the sea ice edge around Antarctica is experimentally evaluated using backscatter data from the Seasat-A Satellite Scatterometer that operated in 1978. Comparisons between the algorithms demonstrate a slight advantage of combined polarization and multi-look over using co-polarized backscatter alone. Classification skill is evaluated by comparisons with surface truth (sea ice maps), subjective ice classification, and independent over lapping scatterometer measurements (consecutive revolutions).

Paper Details

Date Published: 4 April 1997
PDF: 10 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271518
Show Author Affiliations
Jun-Dong Park, Univ. of Central Florida (United States)
Sami M. Alhumaidi, Florida Institute of Technology (United States)
W. Linwood Jones, Univ. of Central Florida (United States)
Shannon Ferguson, Florida Institute of Technology (United States)

Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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