Proof of Concept for Sea Ice Stage of Development Classification Using Deep Learning

Ryan Kruk,M Christopher Fuller,Ian Jeffrey,Alexander S Komarov,Dustin Isleifson

doi:10.3390/rs12152486

Ryan Kruk, M Christopher Fuller + Show 3 more

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https://doi.org/10.3390/rs12152486

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Abstract

Accurate maps of ice concentration and ice type are needed to address increased interest in commercial marine transportation through the Arctic. RADARSAT-2 SAR imagery is the primary source of data used by expert ice analysts at the Canadian Ice Service (CIS) to produce sea ice maps over the Canadian territory. This study serves as a proof of concept that neural networks can be used to accurately predict ice type from SAR data. Datasets of SAR images served as inputs, and CIS ice charts served as labelled outputs to train a neural network to classify sea ice type. Our results show that DenseNet achieves the highest overall classification accuracy of 94.0% including water and the highest ice classification accuracy of 91.8% on a three class dataset using a fusion of HH and HV SAR polarizations for the input samples. The 91.8% ice classification accuracy validates the premise that a neural network can be used to effectively categorize different ice types based on SAR data.

Highlights

Sea ice is a central component of the Arctic cryosphere
While the analysis of the differences between numerical results was necessary to gain insights into the possible variables that caused them, a visual representation of the predictions can serve as a reminder that the goal of this work is to provide a proof of concept that this methodology could be sufficient to automate classification of ice types prior to the analysis of a sea ice expert
Three synthetic aperture radar (SAR) scenes were selected to showcase the predictive quality of the experiment that achieved the highest test set results, which was a DenseNet model trained on the DBDDCA experiment dataset

Summary

Introduction

Sea ice is a central component of the Arctic cryosphere. It covers the Arctic Ocean on an annual basis and persists throughout the summer months as multiyear sea ice. In a time of a rapidly changing climate, there is a demand for local-scale high-resolution information on Arctic marine conditions (e.g., environmental conditions, sea ice state and dynamics) to support logistical operations, transportation, and sea ice use. From an industrial and transportation perspective, knowledge of the ever-changing state of sea ice conditions is critical for operations planning (e.g., Environment and Climate Change Canada Regional Ice-Ocean Prediction System [1]), shipping routes, and sustainable development of the North. During the summer season, changing sea ice conditions have led to changes in transportation and usage of Arctic waterways [2]. Knowledge of the physical and thermodynamic state of sea ice is critically important to understanding how climate change is affecting our world

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