In this study, our goal is to track internal ice layers on the Snow Radar data collected by NASA Operation IceBridge. We examine the application of deep learning methods on radar data gathered from polar regions. Artificial intelligence techniques have displayed impressive success in many practical fields. Deep neural networks owe their success to the availability of massive labeled data. However, in many real-world problems, even when a large dataset is available, deep learning methods have shown less success, due to causes such as lack of a large labeled dataset, presence of noise in the data or missing data. In our radar data, the presence of noise is one of the main obstacles in utilizing popular deep learning methods such as transfer learning. Our experiments show that if the neural network is trained to detect contours of objects in electro-optical imagery, it can only track a low percentage of contours in radar data. Fine-tuning and further training do not provide any better results. However, we show that selecting the right model and training it on the radar imagery from the start yields far better results.

中文翻译：

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用于自动跟踪雷达数据中内部冰层的深度多尺度学习

在这项研究中，我们的目标是跟踪美国宇航局冰桥行动收集的雪地雷达数据上的内部冰层。我们研究了深度学习方法在从极地地区收集的雷达数据上的应用。人工智能技术在许多实际领域都取得了令人瞩目的成功。深度神经网络的成功归功于大量标记数据的可用性。然而，在许多现实世界的问题中，即使有大数据集可用，深度学习方法的成功率也不高，原因包括缺乏大型标记数据集、数据中存在噪声或数据缺失。在我们的雷达数据中，噪声的存在是利用流行的深度学习方法（如迁移学习）的主要障碍之一。我们的实验表明，如果对神经网络进行训练以检测光电图像中物体的轮廓，它只能跟踪雷达数据中低百分比的轮廓。微调和进一步训练并没有提供任何更好的结果。但是，我们表明，从一开始就选择正确的模型并在雷达图像上对其进行训练会产生更好的结果。