Sequential Self-tuning Clustering for Automatic Delimitation of Coastal Upwelling on SST Images
Nov 2020
Upwelling is of major environmental and economic impor-
tance for coastal regions. Sea Surface Temperature (SST) satellite im-
agery 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 cluster-
ing, able to precisely delineate coastal upwelling from SST images. The
stop condition is de ned 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 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.
21st International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2020
