Title

A neural network algorithm for sea ice edge classification

Authors

Authors

S. M. Alhumaidi; W. L. Jones; J. D. Park;S. M. Ferguson

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

IEEE Trans. Geosci. Remote Sensing

Keywords

Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote; Sensing; Imaging Science & Photographic Technology

Abstract

The NASA Scatterometer (NSCAT), 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).

Journal Title

Ieee Transactions on Geoscience and Remote Sensing

Volume

35

Issue/Number

4

Publication Date

1-1-1997

Document Type

Article; Proceedings Paper

Language

English

First Page

817

Last Page

826

WOS Identifier

WOS:A1997YF41100037

ISSN

0196-2892

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