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Classification of sea-ice images using a dual-polarized radar

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3 Author(s)
Orlando, J.R. ; Commun. Res. Lab., McMaster Univ., Hamilton, Ont., Canada ; Mann, R. ; Haykin, Simon

The classification of the returns of a ship-borne like- and cross-polarized radar system into one of four categories, first-year ice, multilayer ice, icebergs, and shadows cast by icebergs is described. The data sets are digitized images obtained from a dual-polarized noncoherent Ku-band (16.5-GHz) radar used on the northern tip of Baffin Island, Canada. By using both the like- and cross-polarized radar inputs, classifier accuracy is improved compared to previous classifiers using only a single input. In particular, the use of both polarizations significantly improves the discrimination between icebergs and multilayer ice. In order to combine the like- and cross-polarized inputs, four classifiers are used: a one-dimensional classifier using the composite image formed by fusing the two polarization inputs with principal components analysis; a two-dimensional Gaussian classifier; and two neural network classifiers (the multilayer perceptron and the Kohonen feature map classifier). The results are compared to the classification based on a single like- or cross-polarized input

Published in:

Oceanic Engineering, IEEE Journal of  (Volume:15 ,  Issue: 3 )