Semiautomated Segmentation of Sentinel-1 SAR Imagery for Mapping Sea Ice in Labrador Coast

This study aims at proposing a semiautomated sea ice segmentation workflow utilizing Sentinel-1 synthetic aperture radar imagery. The workflow consists of two main steps. First, preferable features in sea ice interpretation were determined with a random forest feature selection method. Second, an unsupervised graph-cut image segmentation was performed. The workflow was tested on 13 Sentinel-1A images from January to June 2016, and the results were evaluated on open water segmentation per ice charts provided by Canada Ice Service. The results showed that the proposed workflow was able to segment Sentinel-1 images in to appropriate number of classes, and the potential water identification rate reached 95%.