This study proposes a dual-branch encoder U-Net (DBU-Net) deep learning model to classify sea ice types based on synthetic aperture radar (SAR) images in the Beaufort Sea. The DBU-Net can segment multi-year ice (MYI), first-year ice (FYI), open water (OW), and leads on SAR images. We design a dual-branch encoder to fuse the polarization and the grey-level co-occurrence matrix (GLCM) information of SAR images to improve the model's classification capability. The model is subsequently fine-tuned using lead samples to identify leads. 24 Sentinel-1 SAR images acquired in the Beaufort Sea are utilized for model training and testing. The accuracy (Acc), mean intersection over union (mIoU), and kappa coefficient (Kappa) are employed as evaluation metrics. Experiments show that DBU-Net achieves 91.83%/0.841/0.849 in Acc/mIoU/Kappa in classifying MYI, FYI, and OW, significantly outperforming three traditional models based on support vector machine, random forest, or convolutional neural network. Compared with the original U-Net, the dual-branch encoder and the GLCMs improve 1.45%/4.4%/2.8% in Acc/mIoU/Kappa in MYI, FYI, and OW. Acc/mIoU/Kappa metrics of leads detection is 99.49%/0.801/0.754. Besides, 454 Sentinel-1 SAR images are fed into the optimal DBU-Net to generate 80 m sea ice products in the Beaufort Sea for winters 2018–2022. As the MYI draws wide attention and the FYI and MYI are complementary in the area during the Winter, we discuss the variation of MYI based on the generated sea ice products and explore the relationship between MYI's variation and the Beaufort High. We found that the MYI export in the 2018/19 winter was due to large summer sea ice remains and the abnormal sea ice motion caused by the southeast shifting Beaufort Atmospheric Pressure High (Beaufort High). The MYI import in the 2020/21 winter was due to a strong northward MYI import caused by the powerful Beaufort High.

中文翻译：

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通过 SAR 图像增强波弗特海海冰类型分类的深度学习技术


本研究提出了一种双分支编码器 U-Net (DBU-Net) 深度学习模型，用于根据波弗特海的合成孔径雷达 (SAR) 图像对海冰类型进行分类。 DBU-Net 可以分割多年冰 (MYI)、一年冰 (FYI)、开放水域 (OW) 和 SAR 图像上的线索。我们设计了一种双分支编码器来融合SAR图像的偏振和灰度共生矩阵（GLCM）信息，以提高模型的分类能力。随后使用先导样本对该模型进行微调以识别先导。在波弗特海采集的 24 张 Sentinel-1 SAR 图像用于模型训练和测试。采用准确度 (Acc)、并集平均交集 (mIoU) 和 kappa 系数 (Kappa) 作为评价指标。实验表明，DBU-Net 在 MYI、FYI 和 OW 分类方面的 Acc/mIoU/Kappa 成绩达到 91.83%/0.841/0.849，明显优于基于支持向量机、随机森林或卷积神经网络的三种传统模型。与原始 U-Net 相比，双分支编码器和 GLCM 在 MYI、FYI 和 OW 的 Acc/mIoU/Kappa 方面提高了 1.45%/4.4%/2.8%。线索检测的 Acc/mIoU/Kappa 指标为 99.49%/0.801/0.754。此外，454 张 Sentinel-1 SAR 图像被输入最佳 DBU-Net，以生成 2018-2022 年冬季波弗特海 80 m 的海冰产品。由于MYI受到广泛关注，并且冬季FYI和MYI在该地区具有互补性，我们根据生成的海冰产品讨论MYI的变化，并探讨MYI的变化与波弗特高压之间的关系。 我们发现，2018/19冬季MYI输出是由于大量夏季海冰残留以及东南移动的波弗特高压高压（Beaufort High）引起的异常海冰运动造成的。 2020/21冬季的MYI输入是由于强大的波弗特高压导致MYI向北强劲输入。