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Detection of Moroccan coastal upwelling in SST images using the Expectation-Maximization

Authors :
Driss Aboutajdine
Khalid Daoudi
Khalid Minaoui
Ayoub Tamim
Abderrahman Atillah
LRIT
Laboratoire de Recherche Informatique et Télécommunications (LRIT)
Université Mohammed V de Rabat [Agdal] (UM5)-Centre National de la Recherche Scientifique et Technologique (CNRST)-Université Mohammed V de Rabat [Agdal] (UM5)-Centre National de la Recherche Scientifique et Technologique (CNRST)
Université Mohammed V de Rabat [Agdal] (UM5)-Centre National de la Recherche Scientifique et Technologique (CNRST)
Geometry and Statistics in acquisition data (GeoStat)
Inria Bordeaux - Sud-Ouest
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Centre Royal de Télédétection Spatiale (CRTS)
This work is funded by the French-Moroccan research pro- gram Volubilis (MA/11/256) and the project n◦MPI 12/2010.
Source :
ISVC'14: 10th International Symposium on Visual Computing
Publication Year :
2015
Publisher :
IEEE, 2015.

Abstract

International audience; This paper proposes an unsupervised algorithm for automatic detection and segmentation of upwelling region in Moroccan Atlantic coast using the Sea Surface Temperature (SST) satellite images. This has been done by exploring the Expectation-Maximization algorithm. The good number of clus- ters that best reproduces the shape of upwelling areas is selected by using the two popular Davies-Bouldin and Dunn indices. Area opening technique is developed that is used to remove and discarded the residuals noise in offshore waters not belonging to the upwelling region. The complete system has been validated by an oceanographer using a database of 30 SST images of the year 2007, demonstrating its capability and robustness for precise detection of Moroccan coastal upwelling.

Details

Database :
OpenAIRE
Journal :
2015 Intelligent Systems and Computer Vision (ISCV)
Accession number :
edsair.doi.dedup.....7518aa47cf63d2897dcc097a3736eb83
Full Text :
https://doi.org/10.1109/isacv.2015.7106195