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The spatial structures revealed in SAR intensity imagery provide essential information characterizing the natural variation processesof sea ice. This paper proposes a new method to extract the spatial structures of sea ice based on two spatial stochastic models. Oneis a multi-Gamma model, which characterizes continuous variations corresponding to ice-free area or the background. The other is aPoisson line mosaic model, which characterizes the regional variations of sea ice with different types. The linear combination of thetwo models builds the mixture model to represent spatial structures of sea ice within SAR intensity imagery. To estimate differentsea ice parameters, such as its concentration, scale etc. We define two kinds of geostatistic metrics, theoretical first- and second-ordervariograms. Their experimental alternatives can be calculated from the SAR intensity imagery directly, then the parameters ofthe mixture model are estimated through fitting the theoretical variograms to the experimental ones, and by comparing the estimatedparameters to the egg code, it is verified that the estimated parameters can indicate sea ice structure information showing in the eggcode. The proposed method is applied to simulated images and Radarsat-1 images. The results of the experiments show that theproposed method can estimate the sea ice concentration and floe size accurately and stably within SAR testing images

ESTIMATING SEA ICE PARAMETERS FROM MULTI-LOOK SAR IMAGES USING FIRST- AND SECOND-ORDER VARIOGRAMS