中文摘要：

目的 SAR影像中像素光谱测度的空间相关性蕴含着海洋表面和海冰更加丰富的空间特性及其变化信息,因此合理建模这种相关性是高分辨率SAR影像海冰精准解译的关键.提出一种利用随机模型及空间统计学测度刻画海冰空间结构的方法.方法 本文首先,在空间统计学框架下,SAR影像被表示为多值Gamma模型和泊松线Mosaic模型线性加权构建的混合模型,其中多值Gamma模型用于描述海洋表面雷达信号背向散射变化的连续性,而泊松线Mosaic模型则用于表征不同类型海冰表面雷达信号背向散射变化的区域性.利用上述混合模型的一阶、二阶变异函数,建模蕴含在SAR影像中海冰空间结构的变化.结果 对RADARSAT-1影像海冰结构建模并反演其密度.实验区域真实海冰密度分别为20％,80％等,运用本文方法反演所得海冰密度与真实海冰密度误差正负不超过10％.结论 本文提出混合本征模型用以刻画SAR强度影像中海冰像素强度变化的空间关系,能够较好地反演Ungava湾海冰密度分布.为利用遥感影像检测空间机构提供一种全新的方法.

英文摘要：

Objective The spatial structures revealed in remote sensing imagery are essential pieces of information that char- acterize 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. Method A different geo-statistic metric is introduced, in which the spa- tial structures of sea ice are considered as a combination of two stochastic second-order stationary models. Under the station- arity assumption, a spatial structure model is proposed on the basis of second-order variograms to describe the sea ice densi- ty 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 com- bination 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 im-age spatial structures are characterized by the variance weight and the variogram range related to each model. Result 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%. Conclusion In this study, t