用多分支图网络预测冰层厚度，更准更快。
Multi-branch Spatio-Temporal Graph Neural Network For Efficient Ice Layer Thickness Prediction
- 分枝设计让不同模块专注学习时空特征。
- 在准确率和效率上均优于现有融合模型。
- 适合极地冰层动态研究与遥感数据分析。
理解极地冰层的时空模式对追踪冰盖平衡变化和评估冰体动力学至关重要。尽管卷积神经网络常用于从机载雪雷达传感器获取的原始回波图像中学习冰层模式，但图像噪声导致结果质量不佳。本文转向几何深度学习，采用图神经网络，构建一种基于顶层冰层厚度信息预测深层冰层的时空图神经网络。提出一种新型多分支时空图神经网络，利用GraphSAGE框架学习空间特征，并通过时间卷积操作捕捉时间变化，使各分支更专业化、专注于单一任务。实验表明，该模型在准确率与效率上均持续优于当前融合型时空图神经网络。
原文摘要 · Abstract (English)
Understanding spatio-temporal patterns in polar ice layers is essential for tracking changes in ice sheet balance and assessing ice dynamics. While convolutional neural networks are widely used in learning ice layer patterns from raw echogram images captured by airborne snow radar sensors, noise in the echogram images prevents researchers from getting high-quality results. Instead, we focus on geometric deep learning using graph neural networks, aiming to build a spatio-temporal graph neural network that learns from thickness information of the top ice layers and predicts for deeper layers. In this paper, we developed a novel multi-branch spatio-temporal graph neural network that used the GraphSAGE framework for spatio features learning and a temporal convolution operation to capture temporal changes, enabling different branches of the network to be more specialized and focusing on a single learning task. We found that our proposed multi-branch network can consistently outperform the current fused spatio-temporal graph neural network in both accuracy and efficiency.
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