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International audience; This research deals with the problem of identifying and extracting effectively the main Moroccan upwelling front. The proposed methodology, based on the image-fusion concept, comes to benefit from the information available in both sea-surface temperature (SST) and chlorophyll-a satellite images. Moreover, a new validation index is proposed by computing a simple gradient along the extracted upwelling limit. The developed procedure is applied over a database of 366 SST and 366 chlorophyll-a images from 2007 to 2014, covering the Moroccan Atlantic coast. The final results are validated qualitatively by an oceanographer and quantitatively by our innovative index. The findings of validation demonstrate the performance of our fusion approach.

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.

The current paper presents a novel methodology with the goal of detecting the Moroccan coastal upwelling area. Realistically, our region of interest is characterized by lower temperature degree and higher chlorophyll concentration. The distribution of this two indicators in the ocean is observed by remote sensing from sea surface chlorophyll (SSC) and sea surface temperature (SST) images. In this context, we process 46 images of the year 2014 for each kind set to detect efficiently the desired zone by applying Principal Component Analysis fusion algorithm. The validation made by the oceanographer indicates that the results of our approach is promising in term of Moroccan coastal upwelling delimitation.

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.