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An AM-FM Based Image Segmentation: Detection of Clouds in MSG Images of Algeria

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In this paper, amplitude and frequency modulation (AM-FM) data are extracted from satellite images and processed to facilitate meteorological observations. The approach used is mainly based on 2D-DESA (Discrete Energy Separation Algorithm) where the Teager Kaiser Energy Operator (TKEO) acts as data discriminator. Dominant energy parameters selected by this operator, are employed to get the AM-FM components and segment the images under study via K-means classifier. The AM-FM method so described has been tested on a set of multispectral satellite images collected by Meteosat 9 (MSG-2) over North Africa in November 2011. Applied to both visible and infrared Meteosat Second Generation (MSG) images, it yields a compact and coherent segmentation map of Northern Algeria, Mediterranean Sea and South Western Europe, where the object contours are satisfactorily reproduced and typical clouds, detected.
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Image Segmentation; Brightness; Texture; Weather Satellite; Cloud Detection

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A.Arking, The Radiative Effects of clouds and their impact on climate. Bulletin of the American Meteorological Society, 72, 795-813, 1991.;2

H. R. Pruppacher & J. D. Klett, Microphysics of clouds and precipitation. Kluwer Academic Publishers, Dordrecht, 954 pp, 1997.

J. Schmetz, P. Pili, S. Tjemkes, D. Kerkmann, J. Just, S. Rota & A. Ratier, An Introduction to Meteosat Second Generation (MSG). Bulletin of the American Meteorological Society, 83, 977-992, 2002.;2

H. M. Deneke, A. J. Feijt & R. A. Roebeling, Estimating surface solar irradiance from METEOSAT SEVIRI-derived cloud properties. Remote Sensing of Environment, 112, 3131-3141, 2008.

P. A. Arkin, T. M. Smith, M. R. P. Sapiano & J. Janowiak, The observed sensitivity of the global hydrological cycle to changes in surface temperature. Environmental Research Letters. 5, 35201-35206, 2010.

J. Kaňák, Overview of the IR channels and their applications. Resource document. Slovak Hydro meteorological Institute, 2011. On line:

TD 16. Meteosat Data Collection and Distribution Service. Eumetsat, Darmstadt, 2012.

Hanson, C. G., Mueller, J. , Pili, P., Aminou, D. M. A., Jacquet, B., Bianchi, S., Coste, P. & Faure, F. Meteosat Second Generation: SEVIRI Imaging Performance Results from the MSG-1 Commissioning Phase, The 2003 EUMETSAT Meteorological Satellite Conference, Weimar, Germany: 29 September – 30 October 2003.

J. A. Parikh & A. Rosenfeld, Automatic Segmentation and Classification of Infrared Meteorological Satellite Data. IEEE Transactions on Systems, Man, and Cybernetics, SMC-8, (10), 736-743, 1978.

D. Rosenfeld & I. M. Lensky, Satellite-based insights into precipitation formation processes in continental and maritime convective clouds. Bulletin of the American Meteorological Society, 79, 2457- 2476, 1998,;2

C. Kidd, Satellite rainfall climatology: a review. International Journal of Climatology., 21, 1041-1066, 2001, doi:10.1002/joc.365.

C. Papin, P. Bouthemy & G. Rochard, Unsupervised Segmentation of low Clouds from Infrared Meteosat Images Based on a Contextual Spatio–Temporal Labeling Approach. IEEE Transactions on Geoscience and Remote Sensing, 40(1), 104-114, 2002.

Roebeling, R. A., Schutgens, N. A. J. & Feijt, A. J. Analysis of uncertainties in SEVIRI cloud property retrievals for climate monitoring. AMS Cloud Phys. Rad. Conf., American Meteorology Society, Madison, WI, CD–ROM, P4.51, 2006.

R. Kaur & A. Ganju, Cloud classification in NOAA AVHRR imageries using spectral and textural features, Journal of the Indian Society of Remote Sensing, 36(2), 167-174, 2008.

H. Feidas & A. Giannakos, Classifying convective and stratiform rain using multispectral infrared Meteosat Second Generation satellite data. Theoretical and Applied Climatology, 108 (3), 613–630, 2012.

M. Lazri, Z. Ameur, S. Ameur, Y. Mohia, J. M. Brucker & J. Testud, Rainfall estimation over a Mediterranean region using a method based on various spectral parameters of SEVIRI MSG, Advances in Space Research, 52, 1450-1466, 2013.

C.Agurto & V. Murray, Multiscale AM-FM Methods for Diabetic Retinopathy Lesion Detection. IEEE Transactions on Medical Imaging, 29(2), 502-512, 2010,

J. F. Kaiser, J. F. Some useful properties of Teager’s energy operator. Proc.ICASSP, 3,149-152, 1993.

Ray, N., Havlicek, J., Acton, S. & Pattichis, M. Active contour segmentation guided by AM-FM dominant component analysis. In proceeding IEEE International Conference on Image Processing. Thessaloniki, Greece, Oct. 7-10, 2001, 1, 78-81, 2001.

A.O. Boudraa, F. Salzenstein & J. C. Cexus, 2D Continuous higher energy operators, Optical Engineering, 44(11), 7001-7010, 2005.

Cheikh, F. A., Hamila, R., Gabbouj, M. & Astola, J. Impulsive noise emoval in highly corrupted color images. In Proceeding IEEE International Conference on Image Processing. 1, 997-1000, 1996.

Maragos, P. & Bovik, A. C. Demodulation of images modeled by amplitude-frequency modulation using multidimensional energy separation, In proceeding IEEE International Conference on Image Processing, Austin, TX, November 1994,3, 421-425, 1994.

J. P. Havlicek, P. C. Tay & A. C. Bovik, AM-FM image models: Fundamental techniques and emerging trends. In Handbook of Image and Video Processing, A. C. Bovik, ed., pp. 377–395, Elsevier Academic Press, Burlington, MA, 2nd ed, 2005.

M. S. Pattichis & A. C. Bovik, Analyzing image structure by multidimensional frequency modulation. IEEE Transactions on Pattern Analysis & Machine Intelligence, 5, 753-766, 2007.

I. Kokkinos, G. Evangelopoulos & P. Maragos, Texture analysis and segmentation using modulation features, generative models and weighed curve evolution. IEEE Transactions on Pattern Analysis & Machine Intelligence, 31(1), 142-157, 2009.

Z. Ameur, S. Ameur, A. Adane, H. Sauvageot & K. Bara, Cloud classification using the textural features of Meteosat images, International Journal of Remote Sensing, 25(21), 4491-4503, 2004.

G. Evangelopoulos & P. Maragos, Multiband Modulation Energy Tracking for Noisy Speech Detection. IEEE Transactions on Audio, Speech, and Language Processing, 14(6), 2024-2038, 2006.


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