A neural network algorithm for sea ice edge classification

• Format: Text
• Language: English
• Published: STARS 1997
• Subjects: Geochemistry & Geophysics; Engineering; Electrical & Electronic; Remote; Sensing; Imaging Science & Photographic Technology; Antarc*; Antarctica; Sea ice
• Online Access: https://stars.library.ucf.edu/facultybib1990/1828

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).