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- W3094951678 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." @default.
- W3094951678 created "2020-11-09" @default.
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- W3094951678 date "2020-01-01" @default.
- W3094951678 modified "2023-10-01" @default.
- W3094951678 title "Sequential Self-tuning Clustering for Automatic Delimitation of Coastal Upwelling on SST Images" @default.
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- W3094951678 doi "https://doi.org/10.1007/978-3-030-62365-4_41" @default.
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