随着遥感技术的不断发展，SAR（Synthetic Aperture Radar，合成孔径雷达）影像开始广泛用于空间数据分析。本文在随机几何和空间统计学的基础上，利用随机模型和空间统计学测度解译SAR影像海冰空间结构。在传统二阶变异函数的基础上，创新性地提出一阶变异函数，并以此刻画SAR影像海冰空间结构，从而更加全面、准确地辨识各种类型海冰结构。该方法将SAR影像海冰空间结构建模成两随机函数的线性加权和混合随机模型，其中，多值Gamma随机函数表征海水与海冰的连续性变化，Poisson Mosaic随机函数表征海水与海冰之间的局域性变化。并定义该混合随机模型的理论一阶、二阶变异函数以刻画海冰空间结构变化。对给定SAR影像计算其实际变异函数值，利用最小二乘拟合理论与实际变异函数，得到理论模型参数，并以此反演海冰空间结构信息。本文对加拿大Ungava湾的RADARSAT-1影像进行实验，时间为4月到6月的海冰融化期，海冰结构变化明显。实验结果表明提出的方法可以准确描述不同时期各种类型海冰空间结构。%With the development of remote sensing,SAR (Synthetic Aperture Radar)imagery is widely used in spa-tial 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 sec-ond-order variogram,and prove its efficiency to describe the sea ice spatial structures.The sea ice spatial structures are char-acterized 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 mix-ture 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.
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