Feature selection for classification of polar regions using a fuzzyexpert system

Remote Sensing of Environment

Volumn 58, Issue 1, 1996, Pages 81-100

  Penaloza, Manuel A ^a   Welch, Ronald M ^b  

^a South Dakota School of Mines and Technology   (United States)

^b SOUTH DAKOTA SCHOOL OF MINES AND TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CALCULATIONS; CLOUDS; EXPERT SYSTEMS; FEATURE EXTRACTION; FUZZY SETS; ICE; PATTERN RECOGNITION; RADIOMETERS; SNOW; WATER;

ADVANCED VERY HIGH RESOLUTION RADIOMETER; FEATURE SELECTION; ICE CLOUD; LOCAL AREA COVERAGE;

REMOTE SENSING;

AVHRR; CLASSIFICATION; FEATURE SELECTION; FUZZY EXPERT SYSTEM; IMAGE ACCURACY; LOCAL AREA COVERAGE;

ARCTIC;

EID: 0030251258     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/0034-4257(95)00260-X     Document Type: Article

References (38)

1. Allen, R. C., Jr., Durkee, P. A., and Wash, C. H. (1990), Snow / cloud discrimination with multispectral satellite measurement, J. Atmos. Sci. 29:994-1004.
2. Barry, R. G., Henderson-Sellers, A., and Shine, K. P. (1984), Climate sensitivity and the marginal cryosphere, in Climate Processes and Climate Sensitivity (J. E. Hansen and T. Takahoshi, Eds.) Geophysics Monograph 29, Am. Geophys. Union, 221 pp.
3. Berendes, T. A., and Welch, R. M. (1993), Interactive visual image classification system (IVICS), in Proceedings of 9th International Conference on Interactive Information and Processing Systems for Meteorology, Oceanography and Hydrology, Anaheim, CA, Am. Meteorol. Soc., pp. 76-80.
4. Bezdek, J. (1993), Fuzzy models - what are they, and why? IEEE Trans. Fuzzy Syst. 1:1-6
5. Brown, J. W., Brown, O. B., and Evans, R. H. (1993), Calibration of Advanced Very High Resolution Radiometer infrared channels: a new approach to nonlinear correction, J. Geophys. Res. 98:18257-18268.
6. Chen, D. W., Sengupta, S. K., and Welch, R. M. (1989), Cloud field classification based upon high spatial resolution textural features, Part 2, Simplified vector approaches, J. Geophys. Res. 94:14,749-14,765.
7. Coakley, J. A., and Bretherton, F. P. (1982), Cloud cover from high-resolution scanner data: Detecting and allowing for partially filled fields of view, J. Geophys. Res. 87:4917-4932.
8. Cox, E. (1992), Fuzzy fundamentals, IEEE Spectrum (Oct.): 58-61.
9. Ebert, E. (1987), A pattern recognition technique for distinguishing surface and cloud types in the polar regions, J. Clim. Appl. Meteorol. 26:1412-1427.
10. Ebert, E. (1989), Analysis of polar clouds from satellite imagery using pattern recognition and a statistical cloud analysis scheme, J. Appl. Meteorol. 28:382-399.
11. Giarratano, J., and Riley, G. (1989), Expert Systems: Principles and Programming, PWS-KENT, Boston, 632 pp.
12. Hand, D. J. (1981), Discrimination and Classification, Wiley Series in Probability and Mathematical Statistics, Wiley, New York, 218 pp.
13. Haralick, R. M., Shammugam, K. S., and Dinstein, I. (1973), Textural features for image classification, IEEE Trans. Syst. Man Cybernet. SMC-3:610-621.
14. Key, J. (1990), Cloud cover analysis with arctic AVHRR data, Part II: Classification with spectral and textural measures, J. Geophys. Res. 95:7661-7675.
15. Key, J., Maslanik, J. A., and Barry, R. G. (1989a), Cloud classification from satellite data using a fuzzy sets algorithm: a polar example, Int. J. Remote Sens. 10:1823-1842.
16. Key, J., Maslanik, J. A., and Schweiger, A. J. (1989b), Classification of merged AVHRR and SMMR arctic data with neural networks, Photogramm. Eng. Remote Sens. 55:1331-1338.
17. Kuo, K.-S., Welch, R. M., and Sengupta, S. K. (1988), Structural and textural characteristics of cirrus clouds observed using high spatial resolution LANDSAT imagery, J. Appl. Meteorol. 27:1242-1260.
18. McGuffie, K., Barry, R. G., Schweiger, A., Robinson, D. A., and Newell, J. (1988), Intercomparison of satellite derived cloud analyses for the arctic ocean in spring and summer, Int. J. Remote Sens. 9:447-467.
19. McKay, R. J. (1977), Simultaneous procedures for variable selection in multiple discriminant analysis, Biometrika 64: 283-290.
20. Moninger, W. R. (1988), ARCHER: a prototype expert system for identifying some meteorological phenomena, J. Atmos. Ocean. Technol. 5:144-148.
21. Peak, J. E., and Tag, P. M. (1989), An expert system approach for prediction of maritime visibility obscuration, Mon. Wea. Rev. 117:2641-2653.
22. Rabindra, P., Sengupta, S. K., and Welch, R. M. (1992), An interactive hybrid expert system for polar cloud and surface classification, Environmetrics 3:121-148.
23. Rao, C. R. N., and Chen, J. (1994), Post-launch calibration of the visible and near-infrared channels of the advanced very high resolution radiometer on NOAA-7-9 and -11 spacecraft, NOAA Technical Report NESDIS 78, U.S. Department of Commerce, Washington, DC.
24. Richards, J. A. (1993), Remote Sensing Digital Image Analysis, Springer-Verlag, New York, 340 pp.
25. Rossow, W. B., Gardner, L. C., and Lacis, A. A. (1988), Global, seasonal cloud variations from satellite radiance measurements, Part I: Sensitivity of analysis, J. Clim. 2: 419-458.
26. Self, K. (1990), Designing with fuzzy logic, IEEE Spectrum, (Nov.):42-44, 105.
27. Shine, K. P., and Crane, R. G. (1984), The sensitivity of a one-dimensional thermodynamic sea ice model to a change in cloudiness, J. Geophys. Res. C89:10,615-10,622.
28. Simpson, J. J., and Yhann, S. (1994), Noise reduction in AVHRR channel 3 using Wiener filtering methods, IEEE Trans. Geosci. Remote Sens. 32:315-328.
29. Stowe, L. L., Yeh, H. Y. M., Eck, T. F., Wellemeyer, C. G., Kyle, H. L., and the Nimbus-7 Cloud Data Processing Team (1989), Nimbus-7 global cloud climatology, Part I: First-year results, J. Clim. 2:671-709.
30. Tovinkere, V. R. (1992), Fuzzy logic expert system for classification of polar regions, M.S. thesis, Department of Mathematics and Computer Science, South Dakota School of Mines and Technology, Rapid city, 69 pp.
31. Tovinkere, V. R., Penaloza, M., Logar, A., et al. (1993), An intercomparison of artificial intelligence approaches for polar scene identification, J. Geophys, Res. 98:5001-5016.
32. Viot, G. (1993), Fuzzy logic in C, Dr. Dobb's J. (Feb).
33. Welch, R. M., Sengupta, S. K., and Chen, D. W. (1988), Cloud field classification based upon high spatial resolution textural features, Part I, Gray level concurrence matrix approach, J. Geophys. Res. 93:12,663-12,681.
34. Welch, R. M., Kuo, K. S., and Sengupta, S. K. (1990), Cloud and surface textural features in polar regions, IEEE Trans. Geosci. Remote Sens. 28:520-528.
35. Welch, R. M., Sengupta, S. K., Gorock, A. K., Rabindra, P., Rangaraj, N., and Navar, M. S. (1992), Polar cloud and surface classification using AVHRR imagery: an intercomparison of methods, J. Appl. Meteorol. 31:405-420.
36. Weszka, J. S., Dyer, C. R., and Rosenfeld, A. (1976), A comparative study of texture measures for terrain classification, IEEE Trans. Syst. Man Cybernet. SMC-6:2269-2285.
37. Yhann, S., and Simpson, J. J. (1995), Application of neural networks to AVHRR cloud segmentation, IEEE Trans. Geosci. Remote Sens. 33:590-604.
38. Zadeh, L. A. (1965), Fuzzy sets, Inf. Control: 338-353.