Change Detection of Sea Ice Distribution in SAR Imagery Using Semi-variogram of Intrinsic Regionalization Model

Abstract

The spatial structures revealed in remotely sensed imagery are essential information characterizing the nature and the scale of spatial variation of sea ice processes. This study evaluates the potential capability of using semi-variogram of intrinsic regionalization model for change detection of sea ice. Up to now, the second-order variogram has been widely used to describe the spatial variations within an image, but it demonstrates the limitation to discriminate distinct image spatial structures. This study introduces a different geo-statistic metric, in which spatial structures of sea ice are considered a combination of two stochastic second-order stationary models. Firstly, the multi-gamma model is used to characterize continuous variations corresponding to water or the background of sea ice. The second model is a tessellation model, in which the image domain is randomly separated into non-overlapping cells. In each cell, a random value is independently assigned. It is called the mosaic model. In this paper, the mosaic model is constructed by a Poisson tessellation. The linear combination of these two stochastic models defines the mixture model to represent spatial structures of sea ice presented in SAR intensity imagery. This algorithm is applied to Radarsat-1 images acquired different days to identify the change of sea ice.