论文标题
使用机器学习的海冰浓度估计技术：用于估算SAR图像浓度图的端到端工作流程
Sea Ice Concentration Estimation Techniques Using Machine Learning: An End-To-End Workflow for Estimating Concentration Maps from SAR Images
论文作者
论文摘要
海冰浓度是用于表征极地海冰行为的重要指标。了解这种行为并准确地代表它对于气候科学研究至关重要，并且在海上导航的背景下也具有重要用途。此处介绍了从合成孔径雷达数据中生成学习浓度估计模型的端到端工作流，此处介绍了对现有的被动微波数据进行培训的工作流程。引入了一个新的目标函数，以说明被动微波测量值的不确定性，该测量可以扩展以说明训练数据中的任意误差源，并且使用了一组现场观察结果来评估所选的无源微波浓度估计模型的可靠性。 Google Colagorator被用作开发平台，所有笔记本，培训数据和训练有素的模型都可以在GitHub上找到。本章概述了该调查最有趣的方面，并且在Github上还提供了详细的报告。
Sea ice concentration is an important metric used to characterize polar sea ice behavior. Understanding this behavior and accurately representing it is of critical importance for climate science research, and also has important uses in the context of maritime navigation. An end-to-end workflow for generating learned concentration estimation models from synthetic aperture radar data, trained on existing passive microwave data, is presented here. A novel objective function was introduced to account for uncertainty in the passive microwave measurements, which can be extended to account for arbitrary sources of error in the training data, and a recent set of in-situ observations was used to evaluate the reliability of the chosen passive microwave concentration estimation model. Google Colaboratory was used as the development platform, and all notebooks, training data, and trained models are available on GitHub. This chapter is an overview of the most interesting aspects of this investigation, and a detailed report is also available on GitHub.
