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International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2020: Intelligent Data Engineering and Automated Learning – IDEAL 2020 pp 434–443Cite as

Sequential Self-tuning Clustering for Automatic Delimitation of Coastal Upwelling on SST Images

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12490))

Abstract

Upwelling is of major environmental and economic importance for coastal regions. Sea Surface Temperature (SST) satellite imagery provide an expedited method of monitoring its variability.

This work proposes a one-by-one extracting version of a spatial clustering algorithm with self-tuning thresholding derived from anomalous clustering, able to precisely delineate coastal upwelling from SST images. The stop condition is defined based on properties of the phenomenon and allows to model the appropriate number of upwelling regions.

The algorithm, Sequential Self-Tuning Seed Expanding Cluster (S-STSEC), shows to outperform the homologous sequential version of Seeded Region Growing (SRG) on the automatic delimitation of coastal upwelling from a collection of 207 SST images comprising two distinct upwelling systems: from the Portuguese coast and from Canary upwelling system. Four popular internal clustering validity indices were combined to measure the quality of the results.

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Acknowledgments

S.N. acknowledges the support from NOVA LINCS (UIDB /04516/2020) and P.R. acknowledges the support from CCMAR (UIDB/04516/ 2020) both funded by FCT-Fundação para a Ciência e a Tecnologia, through national funds. The authors acknowledge Dr. Joaquim Luís for the preprocessing of the satellite imagery and the support to this research. Colleagues from CO and DEGGE, Faculdade de Ciências, Universidade de Lisboa, are thanked for providing the collection of SST images of the Portuguese coast examined in this study. The authors also wish to thank the anonymous reviewers for their insightful and constructive comments that allowed to improve the paper.

Author information

Authors and Affiliations

  1. CS Department, Lisbon, Portugal

    Susana Nascimento & Sayed Mateen

  2. NOVA Laboratory for Computer Science and Informatics (NOVA LINCS) Faculdade de Ciências E Tecnologia, Universidade Nova de Lisboa, Lisbon, Portugal

    Susana Nascimento

  3. Campus de Gambelas, Centro de Ciências do Mar (CCMAR), Universidade do Algarve, 8005 -139, Faro, Portugal

    Paulo Relvas

Authors
  1. Susana Nascimento
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  2. Sayed Mateen
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  3. Paulo Relvas
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Corresponding author

Correspondence to Susana Nascimento .

Editor information

Editors and Affiliations

  1. University of Minho, Braga, Portugal

    Cesar Analide

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. Technical University of Madrid, Madrid, Spain

    David Camacho

  4. University of Manchester, Manchester, UK

    Hujun Yin

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Nascimento, S., Mateen, S., Relvas, P. (2020). Sequential Self-tuning Clustering for Automatic Delimitation of Coastal Upwelling on SST Images. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12490. Springer, Cham. https://doi.org/10.1007/978-3-030-62365-4_41

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  • DOI: https://doi.org/10.1007/978-3-030-62365-4_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62364-7

  • Online ISBN: 978-3-030-62365-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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