Abstract
This work aims at automatically identify the upwelling areas in coastal ocean of Morocco using the Sea Surface Temperature (SST) satellite images. This has been done by using the fuzzy clustering technique. The proposed approach is started with the application of Gustafson-Kessel clustering algorithm in order to detect groups in each SST image with homogenous and non-overlapping temperature, resulting in a c-partitioned labeled image. Cluster validity indices are used to select the c-partition that best reproduces the shape of upwelling areas. An area opening technique is developed that is used to filter out the residuals noise and fine structures in offshore waters not belonging to the upwelling regions. The developed algorithm is applied and adjusted over a database of 70 SST images from years 2007 and 2008, covering the southern part of Moroccan atlantic coast. The system was evaluated by an oceanographer and provided acceptable results for a wide variety of oceanographic conditions.
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References
Atillah, A., Orbi, A., Hilmi, K., Mangin, A.: Produits opérationnels d’océanographie spatiale pour le suivi et l’analyse du phénomène d’upwelling marocain. Geo Observateur 14, 49–62 (2005)
Bakun, A.: Fronts and eddies as key structures in the habitat of marine fish larvae: Opportunity, adaptative response and competitive advantage. Scientia Marina 70, 105–122 (2006)
Cayula, J.F., Cornillon, P.: Edge detection algorithm for SST images. Journal of Atmospheric and Oceanic Techology 9, 67–80 (1992)
Holyer, R.J., Peckinpaugh, S.H.: Edge detection applied to satellite imagery of the oceans. IEEE Transactions on Geoscience and Remote Sensing 27, 46–56 (1989)
Nascimento, S., Franco, P., Sousa, F., Dias, J., Neves, F.: Automated computational delimitation of SST upwelling areas using fuzzy clustering. Computers and Geosciences 43, 207–216 (2012)
Tamim, A., Minaoui, K., Daoudi, K., Yahia, H., Atillah, A., Smiej, M.F., Aboutajdine, D.: A simple and efficient approach for coarse segmentation of Moroccan coastal upwelling. In: 2013 Proceedings of the 21st European Signal Processing Conference (EUSIPCO), pp. 1–5 (2013)
Chaudhari, S., Balasubramanian, R., Gangopadhyay, A.: Upwelling detection in AVHRR sea surface temperature (SST) images using neural-network framework. In: IEEE International Geoscience Remote Sensing Symposium II, pp. 926–929 (2008)
Sousa, F.M., Nascimento, S., Casimiro, H., Boutov, D.: Identification of upwelling areas on sea surface temperature images using fuzzy clustering. Remote Sensing of Environment 112, 2817–2823 (2008)
Marcello, J., Marques, F., Eugenio, F.: Automatic tool for the precise detection of upwelling and filaments in remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing 43, 1605–1616 (2005)
Stowe, L.L., Davis, P.A., McClain, E.P.: Scientific basis and initial evaluation of the clavr-1 global clear/cloud classification algorithm for the advanced very high resolution radiometer. Journal of Atmospheric and Oceanic Technology 16, 656 (1999)
Nieto, K., Demarcq, H., McClatchie, S.: Mesoscale frontal structures in the canary upwelling system: New front and filament detection algorithms applied to spatial and temporal patterns. Remote Sensing of Environment 123, 49–62 (2005)
Holben, B.N.: Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing 7, 1417–1434 (1986)
Gustafson, D., Kessel, W.: Fuzzy clustering with a fuzzy covariance matrix. In: 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes, pp. 761–766 (1978)
Wang, W., Zhang, Y.: On fuzzy cluster validity indices. Fuzzy Sets and Systems 158, 2095–2117 (2007)
Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 841–847 (1991)
Fukuyama, Y., Sugeno, M.: A new method of choosing the number of clusters for the fuzzy c-means method. In: Proceeding of Fifth Fuzzy System Symp., pp. 247–250 (1989)
Nascimento, S., Franco, P.: Segmentation of upwelling regions in sea surface temperature images via unsupervised fuzzy clustering. In: Corchado, E., Yin, H. (eds.) IDEAL 2009. LNCS, vol. 5788, pp. 543–553. Springer, Heidelberg (2009)
Vincent, L.: Morphological area openings and closings for grey-scale images. In: Shape in Picture, vol. 126, pp. 197–208. Springer, Heidelberg (1994)
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Tamim, A., Minaoui, K., Daoudi, K., Atillah, A., Aboutajdine, D. (2014). On Detectability of Moroccan Coastal Upwelling in Sea Surface Temperature Satellite Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_37
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DOI: https://doi.org/10.1007/978-3-319-14364-4_37
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