A neural network algorithm for sea ice edge classification
- Florida Inst. of Tech., Melbourne, FL (United States). Florida Technical Remote Sensing Research Group
- Univ. of Central Florida, Orlando, FL (United States)
The NASA Scatterometer (NSCAT), launched in August 1996, is designed to measure wind vectors over ice-free oceans. To prevent contamination f 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).
- Sponsoring Organization:
- National Aeronautics and Space Administration, Washington, DC (United States)
- OSTI ID:
- 524720
- Journal Information:
- IEEE Transactions on Geoscience and Remote Sensing, Journal Name: IEEE Transactions on Geoscience and Remote Sensing Journal Issue: 4 Vol. 35; ISSN 0196-2892; ISSN IGRSD2
- Country of Publication:
- United States
- Language:
- English
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