Articolo in rivista, 2022, ENG, 10.1134/S1054661822030336
Reggiannini M.; Papini O.; Pieri G.
CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy
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.
Pattern recognition and image analysis 32 (3), pp. 631–635
Image processing, Remote sensing, Mesoscale patterns, Sea surface temperature, Machine learning, Climate change
Papini Oscar, Pieri Gabriele, Reggiannini Marco
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 471436
Year: 2022
Type: Articolo in rivista
Creation: 2022-09-29 11:52:16.000
Last update: 2022-11-02 10:00:00.000
CNR authors
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1134/S1054661822030336
URL: https://link.springer.com/article/10.1134/S1054661822030336
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:471436
DOI: 10.1134/S1054661822030336
Scopus: 2-s2.0-85140260896
ISI Web of Science (WOS): 000869886400029