Abstract
The present paper focuses on smoothing techniques for Sea Surface Temperature (SST) satellite images. Due to the non-uniformity of the noise in the image as well as their relatively low spatial resolution, automatic analysis on SST images usually gives poor results. This paper presents a new framework to smooth and enhance the information contained in the images. The gray levels in the image are filtered using a mesh smoothing technique called SOWA while a new technique for resolution enhancement, named grid smoothing, is introduced and applied to the SST images. Both techniques (SOWA and grid smoothing) represent an image using an oriented graph. In this framework, a quadratic criterion is defined according to the gray levels (SOWA) and the spatial coordinates of each pixel (grid smoothing) and minimised using non-linear programming. The two-steps enhancement method is tested on real SST images originated from Meteosat first generation satellite.
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References
Belkin, I.M., O’reilly, J.E.: An algorithm for oceanic front detection in chlorophyll and SST satellite imagery. Journal of Marine Systems 78(3), 319–326 (2009)
Huot, E., Herlin, I., Korotaev, G.: Assimilation of SST satellite images for estimation of ocean circulation velocity. In: Geoscience and Remote Sensing Symposium, pp. II847–II850 (2008)
Cayula, J.-F., Cornillon, P.: Cloud detection from a sequence of SST images, Remote Sens. Environ. 55, 80–88 (1996)
Hai, J., Xiaomei, Y., Jianming, G., Zhenyu, G.: Automatic eddy extraction from SST imagery using artificial neural network. In: Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, Beijing (2008)
Lim, Jae, S.: Two-Dimensional Signal and Image Processing, p. 548. Prentice Hall, Englewood Cliffs (1990) equations 9.44 – 9.46
Guindos-Rojas, F., Canton-Garbin, M., Torres-Arriaza, J.A., Peralta-Lopez, M., Piedra Fernandez, J.A., Molina-Martinez, A.: Automatic Recognition of Ocean Structures from Satellite Images by Means of Neural Nets and Expert Systems. In: Proceedings of ESA-EUSC 2004 - Theory and Applications of Knowledge-Driven Image Information Mining with Focus on Earth Observation (ESA SP-553)., Madrid, Spain, March 17-18 (2004)
Jiang, F., Shi, B.E.: The memristive grid outperforms the resistive grid for edge preserving smoothing, Circuit Theory and Design. In: ECCTD 2009, pp. 181–184 (2009)
Bu, S., Shiina, T., Yamakawa, M., Takizawa, H.: Adaptive dynamic grid interpolation: A robust, high-performance displacement smoothing filter for myocardial strain imaging. In: Ultrasonics Symposium, IUS 2008, November 2-5, pp. 753–756. IEEE, Los Alamitos (2008)
Huang, C.-L., Hsu, C.-Y.: A new motion compensation method for image sequence coding using hierarchical grid interpolation. IEEE Transactions on Circuits and Systems for Video Technology 4(1), 42–52 (1994)
Stals, L., Roberts, S.: Smoothing large data sets using discrete thin plate splines. Computing and Visualization in Science 9, 185–195 (2006)
Roberts, S., Stals, L.: Discrete thin plate spline smoothing in 3D. ANZIAM Journal 45 (2003)
Hamam, Y., Couprie, M.: An Optimisation-Based Approach to Mesh Smoothing: Reformulation and Extension. In: Torsello, A., Escolano, F., Brun, L. (eds.) GbRPR 2009. LNCS, vol. 5534, pp. 31–41. Springer, Heidelberg (2009)
Noel, G., Djouani, K., Hamam, Y.: Grid smoothing: A graph-based approach. In: Cesar Jr., R.M. (ed.) CIARP 2010. LNCS, vol. 6419, pp. 183–190. Springer, Heidelberg (2010)
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Noel, G., Djouani, K., Hamam, Y. (2010). Optimisation-Based Image Grid Smoothing for SST Images. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2010. Lecture Notes in Computer Science, vol 6475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17691-3_21
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DOI: https://doi.org/10.1007/978-3-642-17691-3_21
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