Abstract
Sea ice monitoring plays a vital role in secure navigation and offshore activities. Synthetic aperture radar (SAR) has been widely used as an effective tool for sea ice remote sensing (e.g., ice type classification, concentration and thickness retrieval) for decades because it can collect data by day and night and in almost all weather conditions. The RADARSAT Constellation Mission (RCM) is a new Canadian SAR mission providing several new services and data, with higher spatial coverage and temporal resolution than previous Radarsat missions. As a very deep convolutional neural network, Normalizer-Free ResNet (NFNet) was proposed by DeepMind in early 2021 and achieved a new state-of-the-art accuracy on the ImageNet dataset. In this paper, the RCM data are utilized for sea ice detection and classification using NFNet for the first time. HH, HV and the cross-polarization ratio are extracted from the dual-polarized RCM data with a medium resolution (50 m) for an NFNet-F0 model. Experimental results from Eastern Arctic show that destriping in the HV channel is necessary to improve the quality of sea ice classification. A two-level random forest (RF) classification model is also applied as a conventional technique for comparisons with NFNet. The sea ice concentration estimated based on the classification result from each region was validated with the corresponding polygon of the Canadian weekly regional ice chart. The overall classification accuracy confirms the superior capacity of the NFNet model over the RF model for sea ice monitoring and the sea ice sensing capacity of RCM.
Highlights
The training samples for the two-level random forest (RF) classification model and the Normalizer-Free residual neural network (ResNet) (NFNet) model were selected from this image
Only samples from region G1 were labeled as water, both classification results show that all the dark blue regions in the pseudo-color images were identified as water
This paper presents the first case study of a sea ice classification application using actual RADARSAT Constellation Mission (RCM) dual-polarized data with a state-of-the-art technique (NFNet)
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Global warming has become a great concern as the summer Arctic sea ice extent reached a historically unexpected minimum after 2007 [1]. Sea ice monitoring has become increasingly important because changes in sea ice in the northern hemisphere are speculated to strongly affect climate change. Sea ice floes cannot be ignored in polar navigation and offshore activities. Sea ice monitoring is conducive to management decisions to ensure the safety and efficiency of economic activities in the extreme Arctic environment [2]
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