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Mesoscale Events Classification in Sea Surface Temperature Imagery

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Machine Learning, Optimization, and Data Science (LOD 2022)


Sea observation through remote sensing technologies plays an essential role in understanding the health status of marine fauna species and their future behaviour. Accurate knowledge of the marine habitat and the factors affecting faunal variations allows to perform predictions and adopt proper decisions. This is even more relevant nowadays, with policymakers needing increased environmental awareness, aiming to implement sustainable policies. There is a connection between the biogeochemical and physical processes taking place within a biological system and the variations observed in its faunal populations. Mesoscale phenomena, such as upwelling, countercurrents and filaments, are essential processes to analyse because their arousal entails, among other things, variations in the density of nutrient substances, in turn affecting the biological parameters of the habitat. This paper concerns the proposal of a classification system devoted to recognising marine mesoscale events. These phenomena are studied and monitored by analysing Sea Surface Temperature images captured by satellite missions, such as Metop and MODIS Terra/Aqua. Classification of such images is pursued through dedicated algorithms that extract temporal and spatial features from the data and apply a set of rules to the extracted features, in order to discriminate between different observed scenarios. The results presented in this work have been obtained by applying the proposed approach to images captured over the south-western region of the Iberian Peninsula.

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This paper is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101000825 (NAUTILOS\(^1\);

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Correspondence to Marco Reggiannini .

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Reggiannini, M., Janeiro, J., Martins, F., Papini, O., Pieri, G. (2023). Mesoscale Events Classification in Sea Surface Temperature Imagery. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13810. Springer, Cham.

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  • Print ISBN: 978-3-031-25598-4

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