Grad Seminar: Sea ice classification with dual-polarized SAR imagery: a hierarchical pipeline
Abstract
Sea ice mapping on synthetic aperture radar (SAR) imagery is important for various purposes, including ship navigation and usage in environmental and climatological studies. Although a series of deep learning-based models have been proposed for automatic sea ice classification on SAR scenes, most of them are flat N-way classifiers that do not consider the uneven visual separability of different sea ice types. To further improve classification accuracy with limited training samples, a hierarchical deep learning-based pipeline is proposed for sea ice mapping from SAR.