Neural network algorithm for sea-ice edge classification

Jun-Dong Park; Sami M. Alhumaidi; W. Linwood Jones; Shannon Ferguson

doi:10.1117/12.271518

4 April 1997 Neural network algorithm for sea-ice edge classification

Jun-Dong Park, Sami M. Alhumaidi, W. Linwood Jones, Shannon Ferguson

Proceedings Volume 3077, Applications and Science of Artificial Neural Networks III; (1997) https://doi.org/10.1117/12.271518
Event: AeroSense '97, 1997, Orlando, FL, United States

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).

Citation Download Citation

Jun-Dong Park, Sami M. Alhumaidi, W. Linwood Jones, and Shannon Ferguson "Neural network algorithm for sea-ice edge classification", Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); https://doi.org/10.1117/12.271518

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