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- Piece‐wise constant cluster modelling of dynamics of upwelling patternsPublication . Nascimento, Susana; Martins, Alexandre; Relvas, Paulo; Luis, Joaquim; Mirkin, BorisA comprehensive approach is presented to analyse season's coastal upwelling represented by weekly sea surface temperature (SST) image grids. Our three-stage data recovery clustering method assumes that the season's upwelling can be divided into shorter periods of stability, ranges, each to be represented by a constant core and variable shell parts. Corresponding clustering algorithms parameters are automatically derived by using the least-squares clustering criterion. The approach has been successfully applied to real-world SST data covering two distinct regions: Portuguese coast and Morocco coast, for 16 years each.
- Novel cluster modeling for the spatiotemporal analysis of coastal upwellingPublication . Nascimento, Susana; Martins, Alexandre; Relvas, Paulo; Luis, Joaquim; Mirkin, BorisThis 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.