Abstract
In this work a novel system for bubbles detection on sea surface images is presented. This application is basic to verify radiometer satellite systems which are used to the study of the floor humidity and the sea salinity. 160 images of 8 kinds of salinity have been processed, 20 per class. Two main steps have been implemented: image pre-processing and enhancing in order to improve the bubbles features, and segmentation and bubbles detection. A combination system has been performed with Support Vector Machines (SVM) in order to detect the sea salinity, showing a recognition rate of 95.43%.
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Camps, A., Vall-llosera, M., Villarin, R., Reul, N., Chapron, B., Corbella, I., Duffo, N., Torres, F., Miranda, J.J., Sabia, R., Monerris, A., Rodríguez, R.: The Emissivity of Foam-Covered Water Surface at L-Band: Theorical Modeling and Experimental Results From the Frog 2003 Field Experiment. IEEE Transactions on Geoscience and Remote Sensing 43(5), 925–937 (2003)
Villarino, R., Camps, A., Vall-llosera, M., Miranda, J., Arenas, J.: Sea Foam Effects on the Brightness Temperature at L-Band. In: IEEE International Geoscience and Remote Sensing Symposium IGARSS 2003, vol. 5, pp. 3076–3078 (2003)
Villarino, R.M.: Empirical Determination of the Sea Surface Emissivity at L-Band: A contribution to ESA’s SMOS Earth Explorer Mission. PhD Document, Polytechnic University of Catalonia, Spain (2004)
Corchado, E., Abraham, A., Carvalho, A.C.P.L.F.D.: Hybrid intelligent algorithms and applications. Inf. Sci., 2633-2634 (2010)
Abraham, A., Corchado, E., Corchado, J.M.: Hybrid learning machines. Neurocomputing 72, 2729–2730 (2009)
Gonzalez, R.C., Wood, R.E.: Digital Image Processing. Addison- Wesley, London (2002)
Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis and Machine Vision. Thomson Engineering (2007)
Mokhtarian, F., Abbasi, S.: Matching shapes with self-intersections: application to leaf classification. IEEE Transactions on Image Processing 13(5), 653–661 (2004)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Steinwart, I., Christmann, A.: Support Vector Machines. Springer, New York (2008)
Wang, L.P.: Support Vector Machines: Theory and Application. Springer, Berlin (2005)
Kecman, V.: Learning and Soft Computing, Support Vector Machines, Neural Networks and Fuzzy Logic Models. The MIT Press, Cambridge (2001)
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Fuertes, J.J., Travieso, C.M., Alonso, J.B. (2011). Automatic Identification Approach for Sea Surface Bubbles Detection. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21219-2_11
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DOI: https://doi.org/10.1007/978-3-642-21219-2_11
Publisher Name: Springer, Berlin, Heidelberg
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