Airborne Snow Radar Data Simulation With Deep Learning and Physics-Driven Methods
Airborne Snow Radar Data Simulation With Deep Learning and Physics-Driven Methods
复制标题
利用深度学习和物理驱动方法进行机载雪雷达数据模拟
DOI：
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发表时间：
2021
影响因子：
5.5
通讯作者：
M. Rahnemoonfar
中科院分区：
文献类型：
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作者：
M. Yari;O. Ibikunle;D. Varshney;Tashnim Chowdhury;Argho Sarkar;J. Paden;Jilu Li;M. Rahnemoonfar
监测极地冰盖的性质是冰川学的主要挑战之一。通过昂贵的任务，从极地地区收集了大量不同的雷达数据。然而，从如此大量的数据中检索有意义的信息仍然是一个巨大的挑战。随着近年来机器学习技术的进步，许多科学家都渴望利用这些算法和技术来探索和挖掘北极和南极的数据。然而，这些进步主要发生在监督学习领域，其中模型需要大量的数据，需要大量的注释数据。生成模拟数据可以是一种有效且廉价的方法，可以为训练机器学习模型提供大型标记数据集。在这项工作中，我们探索了两种方法来模拟北极雪雷达回波图图像，即雷达散射物理为基础的方法结合了一些统计措施和一个纯粹的数据驱动的方法，基于条件生成对抗网络。使用几个图像比较指标，我们分析了这两种方法的效用模拟回波图的目的。我们的研究结果表明，物理模拟器生成的图像具有良好的结构相似性，而纯粹的数据驱动的方法实现了更好的纹理相似性的模拟图像。最后，我们还表明，通过模拟回声图增强我们的真实的数据集，我们可以改进我们的深度学习模型，以跟踪雪的内部层。
Monitoring properties of ice sheets in polar regions is one of the main challenges in glaciology. There is a large amount of heterogeneous radar data from the polar regions that have been gathered through expensive missions. However, retrieving meaningful information from this large volume of data is still a great challenge. With the advancement of machine learning techniques in recent years, many scientists are eager to take advantage of these algorithms and techniques to explore and mine Arctic and Antarctic data. These advancements, however, have happened mainly in the area of supervised learning where the models are data hungry and require large amounts of annotated data. Generating simulated data can be an effective and inexpensive approach to provide large labeled datasets for training machine learning models. In this work, we explore two approaches to simulate arctic snow radar echogram images, namely a radar scattering physics based approach combined with some statistical measures and a purely data-driven approach based on a conditional generative adversarial network. Using several image comparison metrics, we analyze the utility of both methods for the purpose of simulating echograms. Our results show that the physics simulator generates images with good structural similarities, while the purely data-driven approach achieves better textural similarities for simulated image. Finally, we also show that by augmenting our real dataset by the simulated echograms, we can improve our deep learning model for tracking internal layers of snow.