ESTIMATING SEA ICE PARAMETERS FROM MULTI-LOOK SAR IMAGES USING FIRST- AND SECOND-ORDER VARIOGRAMS

• Published in: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Main Authors: Wang, Xiaojian, Li, Yu, Zhao, Quanhua
• Format: Text
• Language: English
• Published: 2018
• Subjects: Sea ice
• Online Access: https://doi.org/10.5194/isprs-annals-III-2-99-2016; https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-2/99/2016/

The spatial structures revealed in SAR intensity imagery provide essential information characterizing the natural variation processes of sea ice. This paper proposes a new method to extract the spatial structures of sea ice based on two spatial stochastic models. One is a multi-Gamma model, which characterizes continuous variations corresponding to ice-free area or the background. The other is a Poisson line mosaic model, which characterizes the regional variations of sea ice with different types. The linear combination of the two models builds the mixture model to represent spatial structures of sea ice within SAR intensity imagery. To estimate different sea ice parameters, such as its concentration, scale etc. We define two kinds of geostatistic metrics, theoretical first- and second-order variograms. Their experimental alternatives can be calculated from the SAR intensity imagery directly, then the parameters of the mixture model are estimated through fitting the theoretical variograms to the experimental ones, and by comparing the estimated parameters to the egg code, it is verified that the estimated parameters can indicate sea ice structure information showing in the egg code. The proposed method is applied to simulated images and Radarsat-1 images. The results of the experiments show that the proposed method can estimate the sea ice concentration and floe size accurately and stably within SAR testing images.