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International audience; 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.

17 pages, 16 figures, 3 tables, A new methodology to derive an SST-based upwelling index was based on a rigorous spatial analysis of satellite SST fields and their variability, by referring to previous works, from Wooster et al. (1976) to Santos et al. (2011). The data was precautiously processed by considering data quality aspects (including cloud cover) and the best way to derive accurate coastal SST and its offshore reference. The relevance of the developed index was evaluated by comparing its spatial and seasonal consistency against two wind-based indices as well as with the previous SST-based indices, largely superseding these later ones in term of overall quality and spatio-temporal dynamic. Our index adequately describes the spatio-temporal variability of the coastal upwelling intensity in the Canary Current upwelling system and has the advantage of describing complementary aspects of the coastal dynamics of the region that were not covered by Ekman-based indices.The proposed methodology is generic and can be easily applicable to various coastal upwelling systems, especially the four major eastern boundary upwelling ecosystems. © 2014 Elsevier Ltd., We thank the 50th Anniversary Young African fellowship programme of IOC (Intergovernmental Oceanographic Commission) as well as the French Institute of Research for Development (IRD) for partially supporting this work

Algorithm for detection of thermal upwelling fronts in SST images is presented.The algorithm makes use of the singularity exponents.The singularity exponents are computed in a microcanonical framework.Performance of the algorithm is compared to an automatic algorithm for satellite edge detection.The algorithm is applied and validated by an oceanographer over 92 SST images. Nonlinear signal processing using the Microcanonical Multiscale Formalism (MMF) is used to the problem of detecting and extracting the upwelling fronts in coastal region of Morocco using Sea Surface Temperature (SST) satellite images. The algorithm makes use of the Singularity Exponents (SE), computed in a microcanonical framework, to detect and analyze the critical transitions in oceanographic satellite data. The objective of the proposed study is to develop a helpful preprocessor that transforms SST images into clean and simple line drawing of upwelling fronts as an input to a subsequent step in the analysis of SST images of the ocean. The method is validated by an oceanographer and it is shown to be superior to that of an automatic algorithm commonly used to locate edges in satellite oceanographic images. The proposed approach is applied over a collection of 92 SST images, covering the southern Moroccan Atlantic coast of the years 2006 and 2007. The results indicate that the approach is promising and reliable for a wide variety of oceanographic conditions.

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.

International audience; An unsupervised classification method is developed for the coarse segmentation of Moroccan coastal upwelling using the Sea Surface Temperature (SST) satellite images. The algorithm is started with the generation of c-partitioned labeled image using Otsu's method for the purpose of finding regions of homogenous temperatures. Then two well-known validity indices are used to select the c-partition which best reproduce the shape of upwelling area. A region-growing algorithm is developed that is used to remove the noisy structures in the offshore waters not belonging to the upwelling area. The algorithm is used to provide a seasonal variability of upwelling activity in the southern Moroccan Atlantic coast using 70 SST images of the years 2007 and 2008. The performance of the proposed methodology has been validated by an oceanographer, showing its effectiveness for automatic delimitation of Moroccan upwelling region.