A neural network algorithm for sea ice edge classification

    Authors

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

    Comments

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    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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