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SAR Ice Classification using Fuzzy Screening Method

Rashpal S. Gill (1)

(1) Danish Meteorological Institute (DMI), Lyngbyvej 100, DK - 2100 Copenhagen , Denmark


A semi-automatic SAR sea ice classification algorithm is described. It is based on combining the information in the original SAR data with those in the three "image" products derived from it, namely Power-to-Mean Ratio (PMR), the Gamma distribution and the second order texture parameter entropy, respectively. The technique used to fuse the information in these products is the fuzzy screening method called Multi Experts-Multi Criteria Decision Making (ME-MCDK). The Multiple Experts in this case are the above four "image" products. The two criteria used currently for making decisions are the Kolgomorov-Smirnov distribution matching and the mean of float values. The algorithm classifies an image into any number of predefined classes of sea ice and open water. The representative classes of these surface types are manually identified by the user. Further, as SAR signals from sea ice covered regions and open water are ambiguous, it was found that for the ice infested water around Greenland 4 pre-identified surface classes (2 of sea ice and 2 of open water) in the near range and a similar number in the far range are needed to reliably classify an image. Initial results illustrating the potential of this ice classification algorithm using the RADARSAT ScanSAR data will be presented and its possible extension to fuse the information in these data with the ENVISAT ASAR image products will be discussed. Keywords: SAR, KS Distribution Matching, Ice Cover Classification, Fuzzy Rule, ME-MCDK, RADARSAT, ENVISAT ASAR, Greenland.


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