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- W1566651270 abstract "In this work the Anomalous Pattern algorithm is explored as an initialization strategy to the Fuzzy K-Means (FCM), with the sequential extraction of clusters, one by one, that simultaneously allows to determine the number of clusters. The composed algorithm, Anomalous Pattern Fuzzy Clustering (AP-FCM), is applied in the segmentation of Sea Surface Temperature (SST) images for the identification of Coastal Upwelling.Two independent data samples of two upwelling seasons, in a total of 61 SST images covering large diversity of upwelling situations, are analysed. Results show that by tuning the AP-FCM stop conditions it fits a good number of clusters providing an effective segmentation of the SST images whose spatial visualization of fuzzy membership closely reproduces the original images. Comparing the AP-FCM with the FCM using several validation indices to determine the number of clusters shows the advantage of the AP-FCM since FCM typically leads to under or over-segmented images. Quantitative assessment of the segmentations is accomplished through ROC analysis with ground-truth maps constructed from the Oceanographers' annotations. Compared to FCM, the number of iterations of the AP-FCM is significantly decreased." @default.
- W1566651270 created "2016-06-24" @default.
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- W1566651270 date "2009-01-01" @default.
- W1566651270 modified "2023-09-28" @default.
- W1566651270 title "Segmentation of Upwelling Regions in Sea Surface Temperature Images via Unsupervised Fuzzy Clustering" @default.
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- W1566651270 doi "https://doi.org/10.1007/978-3-642-04394-9_66" @default.
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