Deep learning techniques for enhanced sea-ice types classification in the Beaufort Sea via SAR imagery

Huang, Yan; Ren, Yibin; Li, Xiaofeng

doi:10.1016/j.rse.2024.114204

Cited by 6 publications

(1 citation statement)

References 62 publications

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“…This is the same dataset that has been extensively employed in previous research efforts [50,52], underscoring its proven reliability and relevance to the field. The Beaufort Sea has been one of the most significant areas of Arctic multi-year ice decline during the past few decades, which is a key area for conducting sea ice-related research [54]. The choice of this particular dataset was driven by its detailed ice chart annotations, which were carefully prepared by a retired ice analyst from CIS.…”

Section: Data Overviewmentioning

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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Section: Data Overviewmentioning

confidence: 99%