An automated analysis tool for the classification of sea surface temperature imagery

Reggiannini M., Papini O., Pieri G.

Image processing  Remote sensing  Mesoscale patterns  Sea surface temperature  Machine learning  Climate change 

Sea observation through remote sensing technologies plays an essential role in understanding the health status of the marine coastal environment, its fauna species and their future behavior. Accurate knowledge of the marine habitat and the factors affecting faunal variations allows us to perform predictions and adopt proper decisions. This paper concerns the proposal of a classification system devoted to recognizing marine mesoscale events. These phenomena are studied and monitored by analyzing sea surface temperature imagery. Currently, the standard way to perform such analysis relies on experts manually visualizing, analyzing, and tagging large imagery datasets. Nowadays, the availability of remote sensing data has increased so much that it is desirable to replace the labor-intensive, time-consuming, and subjective manual interpretation with automated analysis tools. The results presented in this work have been obtained by applying the proposed approach to images captured over the southwestern region of the Iberian Peninsula.

Source: Pattern recognition and image analysis 32 (2022): 631–635. doi:10.1134/S1054661822030336

Publisher: Distributed by Allen Press,, Lawrence, KS , Stati Uniti d'America

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