Evaluation Of A Marine Mesoscale Events Classifier

Marine mesoscale phenomena are relevant oceanographic processes that impact on fishery, biodiversity and climate variation. In previous literature, their analysis has been tackled by processing instantaneous remote sensing observations and returning a classification of the observed event. Indeed, these phenomena occur within an extended time range, thus an analysis including time dependence is desirable. Mesoscale Events Classifier (MEC) is an algorithm devoted to the classification of marine mesoscale events in sea surface temperature imagery. By processing time series of satellite temperature observations MEC recognizes the considered area of interest as the domain of one out of a given number of possible events and returns the corresponding label. Objective of this work is to discuss the performance of the MEC pipeline in terms of its capability of correctly capturing the nature of the observed mesoscale process. The evaluation process exploited satellite remote sensing data collected in front of the Portuguese coast.