Analysis of sea surface temperature maps via topological machine learning
Résumé
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.
Mots clés
topological data analysis machine learning image classification temperature map remote sensing digital image 3D point cloud computational topology upwelling classification
topological data analysis
machine learning
image classification
temperature map
remote sensing
digital image
3D point cloud
computational topology
upwelling classification
Domaines
Informatique [cs]Origine | Fichiers produits par l'(les) auteur(s) |
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