Segmentation of Upwelling Regions in Sea Surface Temperature Images via Unsupervised Fuzzy Clustering

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)


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.
Original languageUnknown
Title of host publicationLecture Notes in Computer Science
EditorsH Yin, E Corchado
Place of PublicationBurgos, Spain
ISBN (Print)3-642-04393-3 978-3-642-04393-2
Publication statusPublished - 1 Jan 2009
Event10th International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2009 -
Duration: 1 Jan 2009 → …


Conference10th International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2009
Period1/01/09 → …

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