MeltPondNet: A Swin Transformer U-Net for Detection of Melt Ponds on Arctic Sea Ice
MeltPondNet: A Swin Transformer U-Net for Detection of Melt Ponds on Arctic Sea Ice
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DOI：
10.1109/jstars.2022.3213192
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发表时间：
2022
影响因子：
5.5
通讯作者：
I. Sudakow;V. Asari;Ruixu Liu;D. Demchev
中科院分区：
文献类型：
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作者：
I. Sudakow;V. Asari;Ruixu Liu;D. Demchev
北极地区的高分辨率航空照片是不同海冰特征识别的重要来源，这对于验证、调整和改进气候模型至关重要。北极海冰融化表面的融化池特别令人感兴趣，因为它们是敏感且有价值的指标，并且可以代表北极气候系统的过程。由于熔池形状复杂且边界不可预测，对遥感数据的手动分析极其困难且耗时，因此需要实现过程自动化。在本研究中，我们提出了一种稳健且高效的自动方法，用于从高分辨率航空照片中进行熔池区域分割和边界提取。所提出的算法基于 swin Transformer U-Net，其中我们在解码器设计中引入了新颖的跨通道注意机制。该框架使用光学数据进行操作，并允许将图像分为四类，即海冰/雪、开放水域、融水池和水下冰。我们使用 2005 年夏季希利-奥登跨北极考察期间在北极海冰上收集的航空照片作为测试数据。实验结果表明，该方法适用于熔体池几何形状的精确自动提取，并且还可以扩展到涉及熔体池的其他光学数据源。该方法具有广阔的潜力，可用于分析融化池多年来的相应变化。
High-resolution aerial photographs of Arctic region are a great source for different sea ice feature recognition, which are crucial to validate, tune, and improve climate models. Melt ponds on the surface of melting Arctic sea ice are of particular interest as they are sensitive and valuable indicators and are proxy to the processes in the Arctic climate system. Manual analysis of this remote sensing data is extremely difficult and time-consuming due to the complex shapes and unpredictable boundaries of the melt ponds, and that leads to the necessity for automatizing the processes. In this study, we propose a robust and efficient automatic method for melt pond region segmentation and boundary extraction from high-resolution aerial photographs. The proposed algorithm is based on a swin transformer U-Net in which we introduce novel cross-channel attention mechanisms into the decoder design. The framework operates with optical data and allows for classifying imagery into four classes, i.e., sea ice/snow, open water, melt pond, and submerged ice. We use aerial photographs collected during the Healy–Oden Trans Arctic Expedition over Arctic sea ice in the summer season of 2005 as test data. The experimental results show that the proposed method is suitable for precise automatic extraction of melt pond geometry, and it can also be extended for other optical data sources that involve melt ponds. The approach has a promising potential to be used to analyze melt ponds' corresponding changes between years.