论文标题
深层图像分割，用于检测雷达图像中的内部冰层
Deep Tiered Image Segmentation For Detecting Internal Ice Layers in Radar Imagery
论文作者
论文摘要
了解地球极性冰盖的结构对于建模全球变暖将如何影响极地冰和地球的气候很重要。地面穿透雷达能够收集雪和冰的内部结构的观察，但是手动标记这些观察结果的过程缓慢而费力。最近的工作已经开发了自动技术来找到冰和基岩之间的界限，但是找到内部层 - 指示一年冰的积累结束的微妙界限 - 下一个开始的地方 - 更具挑战性，因为层的数量变化，边界经常合并和分裂。在本文中，我们提出了一个新型的深神经网络，用于解决一般的分层分割问题。然后，我们将其应用于极地冰中的内部层，并在具有人体标记的注释作为地面真理的极性冰雷达数据的大规模数据集上进行评估。
Understanding the structure of Earth's polar ice sheets is important for modeling how global warming will impact polar ice and, in turn, the Earth's climate. Ground-penetrating radar is able to collect observations of the internal structure of snow and ice, but the process of manually labeling these observations is slow and laborious. Recent work has developed automatic techniques for finding the boundaries between the ice and the bedrock, but finding internal layers - the subtle boundaries that indicate where one year's ice accumulation ended and the next began - is much more challenging because the number of layers varies and the boundaries often merge and split. In this paper, we propose a novel deep neural network for solving a general class of tiered segmentation problems. We then apply it to detecting internal layers in polar ice, evaluating on a large-scale dataset of polar ice radar data with human-labeled annotations as ground truth.
