通过拓扑机器学习分析海洋表面温度图

Francesco Conti, O. Papini, D. Moroni, G. Pieri, M. Reggiannini, M. A. Pascali
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引用次数: 0

摘要

利用信号和图像中的拓扑特征的计算方法是目前应用数学中最具创新性的趋势之一。本文将拓扑机器学习的管道应用于从遥感数据中分类四种特定的海洋中尺度模式的挑战性任务,即伊比利亚半岛西南部地区的海表温度图。我们的初步研究在4标签分类中达到了56%的准确率。这样的结果是令人鼓舞的,特别是考虑到数据受到噪声的影响,并且存在低质量/缺失数据。此外,本文还设计了未来改进的方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of sea surface temperature maps via topological machine learning
Computational methods to leverage topological features occurring in signals and images are currently one of the most innovative trends in applied mathematics. In this paper a pipeline of topological machine learning is applied to the challenging task of classifying four specific marine mesoscale patterns from remote sensing data, i.e., Sea Surface Temperature maps of the southwestern region of the Iberian Peninsula. Our preliminary study achieves an accuracy of 56% in the 4-label classification. Such results are encouraging, especially considering that the data are affected by noise and that there are low-quality/missing data. Also, the paper devises directions for future improvements.
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