@article {Tamim:2019:0143-1161:2648,
title = "Automatic detection of Moroccan coastal upwelling zones using sea surface temperature images",
journal = "International Journal of Remote Sensing",
parent_itemid = "infobike://tandf/tres",
publishercode ="tandf",
year = "2019",
volume = "40",
number = "7",
publication date ="2019-04-03T00:00:00",
pages = "2648-2666",
itemtype = "ARTICLE",
issn = "0143-1161",
eissn = "1366-5901",
url = "https://www.ingentaconnect.com/content/tandf/tres/2019/00000040/00000007/art00010",
doi = "doi:10.1080/01431161.2018.1528513",
author = "Tamim and Minaoui and Daoudi and Yahia and Atillah and El Fellah and Aboutajdine and El Ansari",
abstract = "An efficient unsupervised method is developed for automatic segmentation of the area covered by upwelling waters in the coastal ocean of Morocco using the Sea Surface Temperature (SST) satellite images. The proposed approach first uses the two popular unsupervised clustering techniques,
k-means and fuzzy c-means (FCM), to provide different possible classifications to each SST image. Then several cluster validity indices are combined in order to determine the optimal number of clusters, followed by a cluster fusion scheme, which merges consecutive clusters to
produce a first segmentation of upwelling area. The region-growing algorithm is then used to filter noisy residuals and to extract the final upwelling region. The performance of our algorithm is compared to a popular algorithm used to detect upwelling regions and is validated by an oceanographer
over a database of 92 SST images covering each week of the years 2006 and 2007. The results show that our proposed method outperforms the latter algorithm, in terms of segmentation accuracy and computational efficiency.",
}