MSG SEVIRI Image Segmentation Using a Method Based on Spectral, Temporal and Textural Features

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A new scheme for the classification of rainfall areas in convective and stratiform rain using MSG/SEVIRI (Spinning Enhanced Visible and Infrared) data is developed in this paper. The technique is based on spectral, temporal and textural properties of clouds. The introduced classification method uses as spectral parameters: brightness temperature BT(TIR10.8), brightness temperature differences BTDs (TIR10.8-TIR12.1, TIR8.7 -TIR10.8,TWV6.2-TIR10.8) during daytime and nighttime, temperature differences (TIR3.9 -TIR10.8 and TIR3.9-TWV7.3) during nighttime, and reflectances( RVIS0.6, RNIR1.6) during daytime. The textural information is based on the grey level rank approach where each pixel of the brightness temperature in the 10.8µm channel image is represented by a code which takes into account the relations between the spatial positions and the grey level ranks of the neighborhood pixels.
The textural parameter of each pixel correspond to the occurrence frequency vector of a 24 pixel code possibilities computed within a window analysis of 31 x 31 pixels. The temporal parameter (RCT10.8) is the rate of change of brightness temperature over two consecutive images. The developed daytime and nighttime rain area classification technique (RACT-DN) is based on two multilayer perceptron neural networks (MLP-D for daytime and MLP-N for nighttime) which relies on the correlation of satellite data with convective and stratiform rain.
The two algorithms (MLP-D and MLP-N) are trained using as reference convective and stratiform classification data from ground meteorological radar over northern Algeria. It was found that the introduction of temporal and textural parameters improved the results of discrimination between convective and stratiform areas
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Classification; MSG Image; Radar; Artificial Neural Network; Convective and Stratiform Clouds

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E.E. Ebert, M.J. Manton, Performance of satellite rainfall estimation algorithms during TOGA COARE. J Atmos Sci 55, pp.1538–1557, 1998.

B. Thies, T. Nauss, J. Bendix, Discriminating raining from non-raining cloud areas at mid-latitudes using Meteosat Second Generation SEVIRI nighttime data. Meteorological Applications, 15, pp. 219–230, 2008.

M. Lazri, S. Ameur, Y. Mohia, Instantaneous rainfall estimation using neural network from multispectral observations of SEVIRI radiometer and its application in estimation of daily and monthly rainfall. Advances in Space Research, 53(1), pp. 138-155, 2014.

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 SEVIRIMSG, .Advances in Space Research, 52, pp. 1450-1466, 2013,

H. Feidas, A. Giannakos, Classifying convective and stratiform rain using multispectral infrared Meteosat Second Generation satellite data. Theor Appl Climatol 108(3):613–630, 2012.

R. Kaur, A. Ganju, Cloud classification in NOAA AVHRR imageries using spectral and textural features. J Indian Soc Remote Sens 36, pp. 167–174, 2008.

Z. Ameur, S. Ameur, A. Adane, H. Sauvageot, K. Bara, Cloud classification using the textural features of Meteosat images. Int J Remote Sens 25, pp. 4491–4503, 2004.

M.JJ Uddstrom, W. Gray, Satellite cloud classification and rain rate estimation using multispectral radiances and measures of spatial texture. J Appl Meteor ,35, pp. 839–858, 1996.

A. Giannakos, H. Feidas, Classification of convective and stratiform rain based on the spectral and textural features of Meteosat Second Generation infrared data. Theor Appl Climatol 113, pp. 495–510, 2013.

Y. Shou, S. Li, S.Shou, Z. Zhao, Application of a cloud-texture analysis scheme to the cloud cluster structure recognition and rainfall estimation in a mesoscale rainstorm process. Adv Atmos Sc 23(5), pp. 767–774, 2006.

Z. Ameur, A. Adane, S. Ameur, Determination of the Grey Level Ranks for the Segmentation of Textured Images. IEEE ISIE 2006, Montreal, Quebec, Canada, July 9-12, pp. 435-440, 2006.

T. Bellerby, M. Todd, D. Kniveton, C. Kidd, Rainfall estimation from a combination of TRMM precipitation radar and GOES multispectral satellite imagery through the use of an artificial neural network. J. Appl. Meteor., 39, pp. 2115–2128, 2000.

D.I.F. Grimes, E. Coppola, M. Verdecchia, G. Visconti, A neural network approach to real-time rainfall estimation for Africa using satellite data. J. Hydrometeor, 4, pp. 1119–1133, 2003.

F.J. Tapiador, C. Kidd, V. Levizzani, F.S. Marzano, A neural networks–based fusion technique to estimate halfhourly rainfall estimates at 0.18 resolution from satellite passive microwave and infrared data. J. Appl. Meteor., 43, pp. 576–594, 2004.

Y. Hong, K. Hsu, S. Sorooshian, X. Gao, Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. J Appl Meteor 43, pp.1834–1852, 2004.

Lionello P, Malanotte-Rizzoli P, Boscolo R ,Alpert P, Artale V, Li L, Luterbacher J, May W, Trigo R, Tsimplis M, Ulbrich U, Xoplaki E, The Mediterranean climate: an overview of the main characteristics and issues, in: Mediterranean Climate Variability. Elsevier B.V, pp. 1–26, 2006.

R.M. Trigo, E. Xoplaki, J. Lüterbacher, S.O. Krichak, P. Alpert, J. Jacobeit, J. Sàenz, J. Fernàndez, J.F. Gonzàlez-Rouco, Relations between variability in the Mediterranean region and mid-latitude variability, in: Lionello P, Malanotte-Rizzoli P, Boscolo R (Eds.), Mediterranean Climate Variability. Elsevier, Amsterdam, pp. 179–226, 2006.

P. Alpert, M. Baldi, R. Ilani, S. Krichak, C. Price, X. Rodό, H. Saaroni, B. Ziv, P. Kishcha, J. Barkan, A. Mariotti, E. Xoplaki, Relations between climate variability in the Mediterranean region and the tropics: ENSO, South Asian and African monsoons, hurricanes and Saharan dust, in: Mediterranean Climate Variability. Elsevier B.V, pp. 149–177, 2006.

EUMETSAT, Applications of Meteosat Second Generation - Conversion from Counts to Radiances and from Radiances to Brightness Temperatures and Reflectance, 2004,

G.A. Vicente, J.C. Davenport, R.A. Scofield, The role of orographic and parallax corrections on real time high resolution satellite rainfall rate distribution. Int. J. Rem. Sens. 23(2, pp. 221–230), 2002.

H. Feidas, Study of a mesoscale convective complex over the eastern Mediterranean basin with Meteosat data. Eumetsat Meteorological Satellite Conference, Oslo, Norway, 5-9 September, 2011.

H. Feidas, A. Giannakos, Classifying convective and stratiform rain using multispectral infrared Meteosat Second Generation satellite data, 2011.Theor.Appl.Climatol.doi: 10.1007/s00704-011-0557-y.

H. Feidas, A. Giannakos, Identifying precipitating clouds in Greece using multispectral infrared Meteosat Second Generation satellite data, 2010.Theor.Appl.Climatol, doi:10.1007/s00704-010- 0316-5.

H. Feidas, G. Kokolatos, A. Negri, M. Manyin, N. Chrysoulakis, Y. Kamarianakis, Validation of an infrared-based satellite algorithm to estimate accumulated rainfall over the Mediterranean basin. Theor. Appl. Climatol.,2008,

B. Thies, T. Nauss, J. Bendix, Discriminating raining from non-raining cloud areas at mid-latitudes using Meteosat Second Generation SEVIRI daytime data.Atmospheric Chemistry and Physics, 8, pp. 2341–2349, 2008,

T. Inoue, On the temperature and effective emissivity determination of semi-transparent cirrus clouds by bi-spectral measurements in the 10-mm window region. J. Meteorol. Soc. Japan 63, pp. 88–99, 1985.

T. Inoue, A cloud type classification with NOAA-7 split-window measurements. J. Geophys. Res. 92, pp. 3991–4000, 1987.

T.Inoue, X. Wu, K. Bessho, Life cycle of convective activity in terms of cloud type observed by split window. In: 11th Conference on Satellite Meteorology and Oceanography, Madison, WI, USA, 2001.

T. Inoue, An instantaneous delineation of convective rainfall areas using split window data of NOAA-7 AVHRR. J. Meteorol. Soc. Japan 65, pp. 469–481, 1987.

T. Inoue, Day-to-night cloudiness change of cloud types inferred from split window measurements aboard NOAA polar-orbiting satellites. J Meteor Soc Japan 75, pp. 59–66, 1997.

H.J. Lutz, T. Inoue, J. Schmetz, NOTES AND CORRESPONDENCE Comparison of a split-window and a multi-spectral cloud classification for MODIS observations. J Meteor Society of Japan 81(3), pp. 623–631, 2003.

S. Fritz, I. Laszlo, Detection of water vapor in the stratosphere over very high clouds in the tropics. J Geophys Res 98(D12), pp. 22959–22967, 1993.

J. Schmetz, S.A. Tjemkes, M. Gube, L. van de Berg, Monitoring deep convection and convective overshooting with Meteosat. Advances in Space Research, 19(3), pp. 433–441, 1997.

S.A. Ackerman, K.I. Strabala, W.P. Menzel, R.A. Frey, C.C. Moeller, L.E. Gumley, Discriminating clear sky from clouds with MODIS. Journal of Geophysical Research, 103(D24), 1998,pp.32141–3215, .

K.I. Strabala, S.A. Ackerman, W.P. Menzel, Cloud properties inferred from 8–12 μm data. Journal of Applied Meteorology, 33, pp. 212–229, 1994.

B.A. Baum, P.F. Soulen, K.I. Strabala, M.D. King, S.A. Ackerman, W.P. Menzel, P. Yang, Remote sensing of cloud properties using MODIS airborne simulator imagery during SUCCESS: 2. Cloud thermodynamic phase. J Geophys Res 105(11), 2000, pp. 781- 792.

B.A. Baum, S. Platnick, Introduction to MODIS cloud products. Earth Science Satellite Remote Sensing,, pp. 87–108, 2006.

M.J. Pavolonis, A.K. Heidinger, T.Uttal, Daytime global cloud typing from AVHRR and VIIRS: Algorithm description, validation, and comparisons. J. Appl. Meteor., Vol. 44, Issue 6, pp. 804–826, 2005.

M. Lazri, S. Ameur, J.M. Brucker, J. Testud, B. Hamadache, S. Hameg, F. Ouallouche, Y. Mohia, Identification of raining clouds using a method based on optical and microphysical cloud properties from Meteosat second generation daytime and nighttime data, Appl Water Sci, 2013. DOI 10.1007/s13201-013-0079-0.

G.A. Vicente, R.S. Scofield, W.P. Menzel, The operational GOES infrared rainfall estimation technique. Bull. Am. Meteorol. Soc. 79, pp. 1883–1898, 1998.

Nawi, N.M., Rehman, M.Z., Ghazali, M.I., Noise-induced hearing loss prediction in malaysian industrial workers using gradient descent with adaptive momentum Algorithm, (2011) International Review on Computers and Software (IRECOS), 6 (5), pp. 740-748.

B. Krose, P. van der Smagt, An introduction to neural networks. University of Amsterdam, 1996, pp. 44–45.

C. Reudenbach, G. Heinemann, E. Heuel, J. Bendix, M. Winiger, Investigation of summertime convective rainfall in Western Europe based on a synergy of remote sensing data and numerical models. Meteorol.Atmos.Phys, 76, pp. 23–41, 2001.

R. Adler, A.J. Negri, A satellite infrared technique to estimate tropical convective and stratiform rainfall. Journal of Applied Meteorology, 27, pp. 30–51, 1988.

S.A. Tjemkes, L. van de Berg, J. Schmetz, Warm water vapour pixels over high clouds as observed by Meteosat. Beiträge zur Physik der Atmosphäre, 70(1), pp.15–21, 1997.

M. Satyanarayana, G. S. N. Raju, Modern Radars and the Generation of Specified Antenna Radiation Patterns, (2011) International Journal on Communications Antenna and Propagation (IRECAP), 1 (2), pp. 165-169.

Sharareh Kiani, Amir Mansour Pezeshk, Hossein Pourghassem, Design and Simulation of Monopulse Patch Linear Array for Passive SAR Satellite Tracking, (2012) International Journal on Communications Antenna and Propagation (IRECAP), 2 (1), pp. 45-50.

Yılmaz Kalkan, On the Advantages of Frequency-Only MIMO Radar, (2013) International Journal on Communications Antenna and Propagation (IRECAP), 3 (3), pp. 163-168.


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