TY - GEN
T1 - Novel Cluster Modeling for the Spatiotemporal Analysis of Coastal Upwelling
AU - Nascimento, Susana
AU - Martins, Alexandre
AU - Relvas, Paulo
AU - Luís, Joaquim F.
AU - Mirkin, Boris
N1 - Funding Information:
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04326%2F2020/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04326%2F2020/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50019%2F2020/PT#
Acknowledgement. S.N. and A.M. acknowledge the support from NOVA LINCS (UID/CEC/04516/2020), P.R. acknowledges the support through project LA/P/0101/2020, all funded by FCT-Foundation for Science and Technology, through national funds. B.M. gratefully acknowledges support from the Basic Research Program of the National Research University Higher School of Economics. The authors are indebted to the reviewers for their helpful comments that allowed to improve the paper.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - This work proposes a spatiotemporal clustering approach for the analysis of coastal upwelling from Sea Surface Temperature (SST) grid maps derived from satellite images. The algorithm, Core-Shell clustering, models the upwelling as an evolving cluster whose core points are constant during a certain time window while the shell points move through an in-and-out binary sequence. The least squares minimization of clustering criterion allows to derive key parameters in an automated way. The algorithm is initialized with an extension of Seeded Region Growing offering self-tuning thresholding, the STSEC algorithm, that is able to precisely delineate the upwelling region at each SST instant map. Yet, the application of STSEC to the SST grid maps as temporal data puts the business of finding relatively stable “time windows”, here called “time ranges”, for obtaining the core clusters onto an automated footing. The experiments conducted with three yearly collections of SST data of the Portuguese coast shown that the core-shell clusters precisely recognize the upwelling regions taking as ground-truth the STSEC segmentations with Kulczynski similarity score values higher than 98%. Also, the extracted time series of upwelling features presented consistent regularities among the three independent upwelling seasons.
AB - This work proposes a spatiotemporal clustering approach for the analysis of coastal upwelling from Sea Surface Temperature (SST) grid maps derived from satellite images. The algorithm, Core-Shell clustering, models the upwelling as an evolving cluster whose core points are constant during a certain time window while the shell points move through an in-and-out binary sequence. The least squares minimization of clustering criterion allows to derive key parameters in an automated way. The algorithm is initialized with an extension of Seeded Region Growing offering self-tuning thresholding, the STSEC algorithm, that is able to precisely delineate the upwelling region at each SST instant map. Yet, the application of STSEC to the SST grid maps as temporal data puts the business of finding relatively stable “time windows”, here called “time ranges”, for obtaining the core clusters onto an automated footing. The experiments conducted with three yearly collections of SST data of the Portuguese coast shown that the core-shell clusters precisely recognize the upwelling regions taking as ground-truth the STSEC segmentations with Kulczynski similarity score values higher than 98%. Also, the extracted time series of upwelling features presented consistent regularities among the three independent upwelling seasons.
KW - Coastal upwelling
KW - Sequential clustering
KW - Spatiotemporal clustering
KW - Time window
UR - http://www.scopus.com/inward/record.url?scp=85138685200&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-16474-3_46
DO - 10.1007/978-3-031-16474-3_46
M3 - Conference contribution
AN - SCOPUS:85138685200
SN - 978-3-031-16473-6
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 563
EP - 574
BT - Progress in Artificial Intelligence - 21st EPIA Conference on Artificial Intelligence, EPIA 2022, Proceedings
A2 - Marreiros, Goreti
A2 - Martins, Bruno
A2 - Paiva, Ana
A2 - Sardinha, Alberto
A2 - Ribeiro, Bernardete
PB - Springer
CY - Cham
T2 - 21st EPIA Conference on Artificial Intelligence, EPIA 2022
Y2 - 31 August 2022 through 2 September 2022
ER -