卫星镶嵌图中极地低压检测的机器学习方法：主要问题及其解决方案
Machine learning methods for the detection of polar lows in satellite mosaics: major issues and their solutions
摘要：
极地中气旋（PMCs）及其强烈的亚类极地低压（PLs）是相对较小的大气涡旋，主要形成于高纬度的海洋上空。PL可以强烈影响深海水的形成，因为它们与强烈的表面风和热通量有关。探测和跟踪PL对了解PL的气候动力学和分析其对气候系统其他组成部分的影响至关重要。同时，PL的视觉跟踪是一个非常耗时的过程，需要专业知识和对源数据的广泛检查。存在涉及深度卷积神经网络（DCNN）的已知过程，其用于检测再分析数据中的大规模大气现象，其展示了高质量的检测。然而，不能直接将这些程序应用于卫星数据，因为与再分析不同，卫星产品记录了所有尺度的大气涡旋。众所周知，DCNN最初被设计为尺度不变的。这导致了对检测到的现象的尺度进行过滤的问题。还有其他问题需要解决，例如卫星数据的低信噪比和卫星数据集中的负类（没有PL）和正类（其中存在PL）的不平衡数量。在我们的研究中，我们提出了一种用于检测遥感数据中PL和PMC的深度学习方法，该方法解决了类别不平衡和尺度过滤问题。我们还概述了其他问题的潜在解决方案，以及有前途的改进所提出的方法。
英文摘要：
Polar mesocyclones (PMCs) and their intense subclass polar lows (PLs) are relatively small atmospheric vortices that form mostly over the ocean in high latitudes. PLs can strongly influence deep ocean water formation since they are associated with strong surface winds and heat fluxes. Detection and tracking of PLs are crucial for understanding the climatological dynamics of PLs and for the analysis of their impacts on other components of the climatic system. At the same time, visual tracking of PLs is a highly time-consuming procedure that requires expert knowledge and extensive examination of source data. There are known procedures involving deep convolutional neural networks (DCNNs) for the detection of large-scale atmospheric phenomena in reanalysis data that demonstrate a high quality of detection. However, one cannot apply these procedures to satellite data directly since, unlike reanalyses, satellite products register all the scales of atmospheric vortices. It is also known that DCNNs were originally designed to be scale-invariant. This leads to the problem of filtering the scale of detected phenomena. There are other problems to be solved, such as a low signal-to-noise ratio of satellite data and an unbalanced number of negative (without PLs) and positive (where a PL is presented) classes in a satellite dataset. In our study, we propose a deep learning approach for the detection of PLs and PMCs in remote sensing data, which addresses class imbalance and scale filtering problems. We also outline potential solutions for other problems, along with promising improvements to the presented approach.
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