用多时相信息提升冰川裂解前沿分割精度
Multi-temporal Calving Front Segmentation
- 并行处理多帧卫星影像，跨时间交换特征信息以稳定预测
- 在CaFFe数据集上实现184.4米均距误差和83.6%交并比
- 适合需要高精度冰川动态监测的研究者使用
海洋末端冰川的裂解前沿持续变化，显著影响冰川质量和动力学，需持续监测。现有深度学习模型虽可自动分割合成孔径雷达图像中的裂解前沿，但常因季节性因素（如冰碎屑或积雪覆盖）导致分类错误。为此，我们提出并行处理同一冰川的多时相卫星影像序列，通过在对应特征图间传递时间信息来稳定每帧预测。将该方法集成至当前最先进的Tyrion架构，在CaFFe基准数据集上达到新最优性能：平均距离误差为184.4米，平均交并比达83.6%。
原文摘要 · Abstract (English)
The calving fronts of marine-terminating glaciers undergo constant changes. These changes significantly affect the glacier's mass and dynamics, demanding continuous monitoring. To address this need, deep learning models were developed that can automatically delineate the calving front in Synthetic Aperture Radar imagery. However, these models often struggle to correctly classify areas affected by seasonal conditions such as ice melange or snow-covered surfaces. To address this issue, we propose to process multiple frames from a satellite image time series of the same glacier in parallel and exchange temporal information between the corresponding feature maps to stabilize each prediction. We integrate our approach into the current state-of-the-art architecture Tyrion and accomplish a new state-of-the-art performance on the CaFFe benchmark dataset. In particular, we achieve a Mean Distance Error of 184.4 m and a mean Intersection over Union of 83.6.
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