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. First, a semantic segmentation model with encoder-decoder structure is implemented to accurately separate ice and open water on each SAR scene. To classify different ice types, a two-level category hierarchical convolutional neural network (CNN)-based model is then trained using limited numbers of labeled image patches. Experimental results on dual-polarized SAR scenes collected from C-band satellite RADARSAT-2 show that ice-water mapping results are in very good accordance with pixel-based labels under different combinations of encoders and decoders. Also, compared to a flat N-way CNN, the hierarchical CNNs further boosts the classification accuracy among all the ice types.

Presenter

Xinwei Chen, PhD candidate in Systems Design Engineering 

Attending this seminar will count towards the graduate student seminar attendance milestone!