With the development of remote sensing

SAR (Synthetic Aperture Radar) imagery is widely used in spatial data analysis.This paper uses stochastic models and geostatistic metrics to characterize the spatial structures of sea ice based on stochastic geometry and spatial statistics.We propose a geostatistic metric first-order variogram based on the second-order variogram

and prove its efficiency to describe the sea ice spatial structures.The sea ice spatial structures are characterized by the weighted linear combination of two stochastic models.One is a multi-Gamma model

which characterizes continuous variations corresponding to water or the background of sea ice.Another is a Poisson line mosaic model

which characterizes the regional variations of different types of sea ice.The linear combination of the two models defines the mixture model to represent spatial structures of sea ice within SAR intensity imagery.To estimate the parameters of the mixture model

experimental first-and second-order variograms are calculated from the SAR intensity imagery

and then fit them with the theoretical variograms for the purpose of estimating the mixture model parameters.The proposed approach is applied to Radarsat-1 images from April to June to identify the change of sea ice.The results of the experiments show that the proposed approach can estimate the sea ice density accurately and stably.