The spatial structures revealed in remote sensing imagery are essential pieces of information that characterize the nature and scale of the spatial variation of sea ice processes. The freezing and melting of sea ice lead to changes in sea environment conditions

which in turn cause lockout

channel blocking

ship damage

and other issues. This study evaluates the potential capability of using the variogram of the intrinsic regionalization model to estimate sea ice density from synthetic-aperture radar (SAR) intensity images. A different geo-statistic metric is introduced

in which the spatial structures of sea ice are considered as a combination of two stochastic second-order stationary models. Under the stationarity assumption

a spatial structure model is proposed on the basis of second-order variograms to describe the sea ice density in multi-look SAR images. First

the multi-gamma model is used to characterize continuous variations that correspond to water or the background of sea ice. Second

a Poisson tessellation-based mosaic model is used

in which the image domain is randomly separated into non-overlapping cells. In each cell

a random value is independently assigned. The linear combination of these two stochastic models defines the mixture model to represent the spatial structures of sea ice presented in the SAR intensity imagery. Finally

the least squares method is used for the fitting method to estimate parameters. The image spatial structures are characterized by the variance weight and the variogram range related to each model. The proposed algorithm is applied to Radarsat-1 images acquired in different days to identify the change in sea ice. Experimental results show that the proposed method can accurately and stably estimate sea ice density. The real sea ice densities of the experimental areas are 20% and 80%. The errors between the real sea ice densities and the results from the proposed method are no more than plus or minus 10%. In this study

the spatial structures of sea ice shown in a SAR intensity image are characterized by the intrinsic regionalization model. The algorithm is applied to a Radarsat-1 SAR intensity image to detect the sea ice change during two months in 2008. Results demonstrate that the algorithm is useful in detecting sea ice change in terms of intensity and size. However

these findings are limited by the number

types

and small size of the sea ice. Several aspects of the proposed algorithm can be improved in further studies. With the availability of the high-resolution polarization SAR image

the problem caused by high resolution and full polarization should be considered. For example

for the full-polarization SAR image

the spectral and spatial correlation of the same sea ice type becomes complicated. High resolution makes both the details and the noise in the homogeneous sea ice area prominent and enables the SAR images to show massive data characteristics. All of these issues result in unexpected difficulties in the design of high-resolution polarization SAR image-orientated algorithms for sea ice detection. In our future work

the suitability of the proposed algorithm to a high-resolution polarization SAR image will be improved through development of a spatial statistic model in spectral space.