Sea Ice in the Arctic Paleoenvironments

Bobylev, Leonid; Miles, Martin W.

doi:10.1007/978-3-030-21301-5_2

Cited by 7 publications

(5 citation statements)

References 94 publications

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“…The net surface heat budget of the Arctic Ocean is dominated by radiative fluxes, and the exchange of heat between the Arctic Ocean and atmosphere is strongly moderated by the thickness of sea ice (Bobylev and Miles, 2020;Untersteiner, 1964).…”

Section: Thermodynamic Forcing and Thermodynamic Feedbackmentioning

confidence: 99%

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Yang

2021

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Section: Thermodynamic Forcing and Thermodynamic Feedbackmentioning

confidence: 99%

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Yang

2021

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“…Local communities and marine mammals rely on sea ice for hunting, traveling, and other daily activities [2]. Due to global warming, the hlArctic sea ice extent has declined rapidly, at a rate of 13% per decade [3]. The 15 lowest sea ice extent yearly minima on record have all occurred in the past 15 years [4,5].…”

Section: Introductionmentioning

confidence: 99%

IceGCN: An Interactive Sea Ice Classification Pipeline for SAR Imagery Based on Graph Convolutional Network

Jiang,

Chen,

et al. 2024

Remote Sensing

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Monitoring sea ice in the Arctic region is crucial for polar maritime activities. The Canadian Ice Service (CIS) wants to augment its manual interpretation with machine learning-based approaches due to the increasing data volume received from newly launched synthetic aperture radar (SAR) satellites. However, fully supervised machine learning models require large training datasets, which are usually limited in the sea ice classification field. To address this issue, we propose a semi-supervised interactive system to classify sea ice in dual-pol RADARSAT-2 imagery using limited training samples. First, the SAR image is oversegmented into homogeneous regions. Then, a graph is constructed based on the segmentation results, and the feature set of each node is characterized by a convolutional neural network. Finally, a graph convolutional network (GCN) is employed to classify the whole graph using limited labeled nodes automatically. The proposed method is evaluated on a published dataset. Compared with referenced algorithms, this new method outperforms in both qualitative and quantitative aspects.

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“…Sea ice covers about 12% of the oceans on Earth [1]. In high latitude and polar regions, sea ice reduces the heat exchange between the sea and the atmosphere, regulating the global climate [2]. As the global temperature has been rising in the past decades, sea ice thickness has reduced dramatically [3].…”

Section: Introductionmentioning

confidence: 99%

Sea Ice–Water Classification of RADARSAT-2 Imagery Based on Residual Neural Networks (ResNet) with Regional Pooling

Jiang

Clausi

2022

Remote Sensing

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Sea ice mapping plays an integral role in ship navigation and meteorological modeling in the polar regions. Numerous published studies in sea ice classification using synthetic aperture radar (SAR) have reported high classification rates. However, many of these focus on numerical results based on sample points and ignore the quality of the inferred sea ice maps. We have designed and implemented a novel SAR sea ice classification algorithm where the spatial context, obtained by the unsupervised IRGS segmentation algorithm, is integrated with texture features extracted by a residual neural network (ResNet) and, using regional pooling, classifies ice and water. This algorithm is trained and tested on a published dataset and cross-validated using leave-one-out (LOO) strategy, obtaining an overall accuracy of 99.67% and outperforming several existing algorithms. In addition, visual results show that this new method produces sea ice maps with natural ice–water boundaries and fewer ice and water errors.

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Sea Ice in the Arctic Paleoenvironments

Cited by 7 publications

References 94 publications

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IceGCN: An Interactive Sea Ice Classification Pipeline for SAR Imagery Based on Graph Convolutional Network

Sea Ice–Water Classification of RADARSAT-2 Imagery Based on Residual Neural Networks (ResNet) with Regional Pooling

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