Automatic classification of VIS-IR meteosat images

Computers and Geosciences

Volumn 22, Issue 10, 1996, Pages 1137-1146

  Massons, J ^a   Domingo, D ^a   Grau, J ^b  

^a UNIVERSITAT ROVIRA I VIRGILI   (Spain)

^b UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

Author keywords

Cloud analysis; Unsupervised image classification

Indexed keywords

ALGORITHM; GEOSTATIONARY SATELLITE; IMAGE CLASSIFICATION; METEOSAT; UNSUPERSIVED CLASSIFICATION; VISIBLE INFRARED;

EID: 0030440827     PISSN: 00983004     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0098-3004(96)00058-1     Document Type: Article

References (26)

1. Anderberg, M. R., 1973, Cluster analysis for applications: Academic Press, London, 359 p.
2. Atkinson, P., Cushnie, J. L., Townshend, J. R. G., and Wilson, A., 1985, Improving Thematic Mapper land cover classification using filtered data: Intern. Jour. Remote Sensing, v. 6, no. 6, p. 955-961.
3. Buchheim, M. P., and Lillesand, T. M., 1989, Semiautomated training field extraction and analysis for efficient digital image classification: Photogrammetric Engineering and Remote Sensing, v. 55, no. 9, p. 1347-1355.
4. Desbois, M., Sezé, G., and Szejwach, G., 1982, Automatic classification of clouds on METEOSAT imagery: application to high-level clouds: Jour. Applied Meteorology, v. 21, no. 3, p. 401-412.
5. Devi, B. B., and Sarma, V. V. S., 1986, Binary tree design using fuzzy isodata: Pattern Recognition Letters, v. 4, no. 1, p. 13-18.
6. Dioszeghy, M., and Fejes, E., 1995, Cloud classification derived from Meteosat data involving the standard deviation fields of the brightness values: Adv. Space Res., v. 16, no. 10, p. 33-36.
7. England, C. F., and Hunt, G. E., 1985, A biespectral method for automatic determination of parameters for use in imaging satellite retrievals: Intern. Jour. Remote Sensing, v. 6, no. 9, p. 1545-1553.
8. Fukunaga, K., 1990, Introduction to statistical pattern recognition: Academic Press, London, 591 p.
9. Gnanadesikan, R., 1977, Methods for statistical data analysis of multivariate observations: John Wiley & Sons, New York, 311 p.
10. Gordon, S. I., and Philipson, R., 1986, A texture-enhancement procedure for separating orchard from forest in Thematic Mapper data: Intern. Jour. Remote Sensing, v. 7, no. 2, p. 301-304.
11. Hand, D. J., 1992, Discrimination and classification: John Wiley & Sons, New York, 218 p.
12. Liljas, E., 1987, Multispectral classification of cloud, fog and haze, in Vaughan, R. A., ed., Remote Sensing Applications in Meteorology and Hydrology: D. Reidel Publ. Co., Dordrecht, p. 301-319.
13. Liu, G. S., Curry, J. A., and Shen, R. S., 1995, Classification of clouds over the western equatorial Pacific using combined infrared and microwave satellite data: Jour. Geophys. Res., v. 100, no. F7, p. 13811-13826.
14. Massons, J., Grau, J., and Llorens, M., 1996, Time variability of the radiative characteristics of soil and sea surfaces observed from Meteosat satellites: Theoretical and Applied Climatology, (in press).
15. McCaffrey, T. M., and Franklin, S. E., 1993, Automated training site selection for large-area remote-sensing image analysis: Computers & Geosciences, v. 19, no. 10, p. 1413-1428.
16. Minnis, P., and Harrison, E. F., 1984, Diurnal variability of regional cloud and clear sky radiative parameters derived from GOES data, Part I, analysis method: Jour. Climatology and Applied Meteorology, v. 23, no. 7, p. 993-1011.
17. Pairman, D., and Kitter, J., 1986, Clustering algorithms for use with images of clouds: Intern. Jour. Remote Sensing, v. 7, no. 7, p. 865-866.
18. Platt, C. M. R., 1983, On the bispectral method for cloud parameter determination from satellite VIS-IR data: separating broken clouds and semitransparent clouds: Jour. Climatology and Applied Meteorology, v. 22, no. 3, p. 429-439.
19. Porcú, F., and Levizzani, V., 1992, Cloud classification using METEOSAT VIS-IR imagery: Intern. Jour. Remote Sensing, v. 13, no. 5, p. 893-909.
20. Ritter, N. D., and Hepner, G. F., 1990, Application of an artificial neural network to land-cover classification of Thematic Mapper imagery: Computers & Geosciences, v. 16, no. 6, p. 873-880.
21. Rossow, W. B., and Garder, L. C., 1993, Cloud detection using satellite measurements of infrared and visible radiances for ISCCP: Jour. Climate, v. 6, no. 12 , p. 2341-2369.
22. Rossow, R., and Schiffer, R. A., 1991, ISCCP cloud data products: Bull. Am. Meteorological Soc., v. 72, no. 1, p. 2-20.
23. Rossow, W. B., Mosher, F., Kinsella, E., Arking, A., Desbois, M., Harrison, E., Minnis, P., Ruprecht, E., Sèze, G., Simmer, C., and Smith, E., 1985, ISCCP cloud algorithm intercomparation: Jour. Climatology and Applied Meteorology, v. 24, no. 9, p. 877-903.
24. Sèze, G., and Desbois, M., 1987, Cloud cover analysis from satellite imagery using spatial and temporal characteristics of the data: Jour. Climatology and Applied Meteorology, v. 26, no. 2, p. 287-303.
25. Tsonis, A. A., 1984, On the separability of various classes from GOES visible and infrared data: Jour. Climatology and Applied Meteorology, v. 23, no. 10, p. 1393-1410.
26. Welsh, R. W., Kuo, K. S., Wielicki, B. A., Sengupta, S. K., and Parker, L., 1988, Marine stratocumulus cloud fields off the coast of southern California observed using LANDSAT imagery. Part 1: structural characteristics: Jour. Applied Meteorology, v. 27, no. 4, p. 341-362.