Abstract

The basic concepts and some of the methods of pattern classification are reviewed. The problem of detecting and quantifying a mixture of gases is discussed as an application of spectral pattern recognition to atmospheric IR remote sensing.

© 1983 Optical Society of America

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References

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  1. T. W. Anderson, An Introduction to Multivariate Statistical Analysis (Wiley, New York, 1958).
  2. T. Y. Young, T. W. Calvert, Classification, Estimation and Pattern Recognition (American Embassy, New York, 1974).
  3. R. R. Legault, “IR Spectral Pattern Recognition,” Workshop on Optical and Laser Remote Sensing, Monterey, Calif. (1982).
  4. R. B. Crane et al., IEEE Trans. Geosci. Electron. GE-10, 159 (1972).
  5. R. O. Duda, P. E. Hart, Pattern Classification and Scene Analysis (Wiley, New York, 1973).
  6. D. J. Wilde, Optimum Seeking Methods (Prentice-Hall, Englewood Cliffs, N.J., 1964).
  7. E. N. Webb, H. A. Watter, D. Flanigan, “Spectral Classification Techniques for Remote Sensing Alarms,” U.S. Army Armament Research and Development Command, Report ARCSL-TR-77054 (1977).
  8. H. Harmon, Modern Factor Analysis (Van Nostrand, New York, 1967).
  9. P. Horst, Factor Analysis of Data Matrices (Holt, Rinehart & Winston, New York, 1965).
  10. G. Hangac, R. E. Wieboldt, R. B. Lam, T. L. Isenhour, Appl. Spectrosc. 36, 40 (1982).
    [CrossRef]

1982 (1)

1972 (1)

R. B. Crane et al., IEEE Trans. Geosci. Electron. GE-10, 159 (1972).

Anderson, T. W.

T. W. Anderson, An Introduction to Multivariate Statistical Analysis (Wiley, New York, 1958).

Calvert, T. W.

T. Y. Young, T. W. Calvert, Classification, Estimation and Pattern Recognition (American Embassy, New York, 1974).

Crane, R. B.

R. B. Crane et al., IEEE Trans. Geosci. Electron. GE-10, 159 (1972).

Duda, R. O.

R. O. Duda, P. E. Hart, Pattern Classification and Scene Analysis (Wiley, New York, 1973).

Flanigan, D.

E. N. Webb, H. A. Watter, D. Flanigan, “Spectral Classification Techniques for Remote Sensing Alarms,” U.S. Army Armament Research and Development Command, Report ARCSL-TR-77054 (1977).

Hangac, G.

Harmon, H.

H. Harmon, Modern Factor Analysis (Van Nostrand, New York, 1967).

Hart, P. E.

R. O. Duda, P. E. Hart, Pattern Classification and Scene Analysis (Wiley, New York, 1973).

Horst, P.

P. Horst, Factor Analysis of Data Matrices (Holt, Rinehart & Winston, New York, 1965).

Isenhour, T. L.

Lam, R. B.

Legault, R. R.

R. R. Legault, “IR Spectral Pattern Recognition,” Workshop on Optical and Laser Remote Sensing, Monterey, Calif. (1982).

Watter, H. A.

E. N. Webb, H. A. Watter, D. Flanigan, “Spectral Classification Techniques for Remote Sensing Alarms,” U.S. Army Armament Research and Development Command, Report ARCSL-TR-77054 (1977).

Webb, E. N.

E. N. Webb, H. A. Watter, D. Flanigan, “Spectral Classification Techniques for Remote Sensing Alarms,” U.S. Army Armament Research and Development Command, Report ARCSL-TR-77054 (1977).

Wieboldt, R. E.

Wilde, D. J.

D. J. Wilde, Optimum Seeking Methods (Prentice-Hall, Englewood Cliffs, N.J., 1964).

Young, T. Y.

T. Y. Young, T. W. Calvert, Classification, Estimation and Pattern Recognition (American Embassy, New York, 1974).

Appl. Spectrosc. (1)

IEEE Trans. Geosci. Electron. (1)

R. B. Crane et al., IEEE Trans. Geosci. Electron. GE-10, 159 (1972).

Other (8)

R. O. Duda, P. E. Hart, Pattern Classification and Scene Analysis (Wiley, New York, 1973).

D. J. Wilde, Optimum Seeking Methods (Prentice-Hall, Englewood Cliffs, N.J., 1964).

E. N. Webb, H. A. Watter, D. Flanigan, “Spectral Classification Techniques for Remote Sensing Alarms,” U.S. Army Armament Research and Development Command, Report ARCSL-TR-77054 (1977).

H. Harmon, Modern Factor Analysis (Van Nostrand, New York, 1967).

P. Horst, Factor Analysis of Data Matrices (Holt, Rinehart & Winston, New York, 1965).

T. W. Anderson, An Introduction to Multivariate Statistical Analysis (Wiley, New York, 1958).

T. Y. Young, T. W. Calvert, Classification, Estimation and Pattern Recognition (American Embassy, New York, 1974).

R. R. Legault, “IR Spectral Pattern Recognition,” Workshop on Optical and Laser Remote Sensing, Monterey, Calif. (1982).

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Figures (8)

Fig. 1
Fig. 1

Spectra and spectral classes displayed in color space.

Fig. 2
Fig. 2

Construction of optimum decision regions.

Fig. 3
Fig. 3

Optimization of the choice of colors for a two-color system by a gradient ascent (descent) method.

Fig. 4
Fig. 4

Categories of estimation and learning methods.

Fig. 5
Fig. 5

Geometric interpretation of the linear discriminant function.

Fig. 6
Fig. 6

Decision regions for a linear machine.

Fig. 7
Fig. 7

Transformation of clusters from (a) color space to (b) feature (concentration) space.

Fig. 8
Fig. 8

Effects of smaller N, low S/N, variable Wij, and correlated C j on the feature space clusters.

Equations (2)

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β = ln [ p ( S ¯ | A ) p ( S ¯ | B ) ] 1 / 2 d S ¯ ,
S i = const × L j = 1 M α ij C j ; i = 1 , 2 , , N ,

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