A fast two-stage classification method for high-dimensional remote sensing data

IEEE Transactions on Geoscience and Remote Sensing

Volumn 36, Issue 1, 1998, Pages 182-191

  Tu, Te Ming ^a   Chen, Chin Hsing ^a   Wu, Jiunn Lin ^b   Chang, Chein I ^c  

^a NATIONAL DEFENSE UNIVERSITY   (Taiwan)

^b NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

^c University of Marylandw Baltimore County   (United States)

Author keywords

Band selection (bs); Canonical analysis (ca); Principal components analysis (pca); Recursive ml classifier (mlc); Winograd's identity

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); COMPUTATIONAL COMPLEXITY; FEATURE EXTRACTION; RECURSIVE FUNCTIONS;

BAND SELECTION; CANONICAL ANALYSIS; HIGH DIMENSIONAL REMOTELY SENSED DATA; HYPERSPECTRAL IMAGE DATA; MAXIMUM LIKELIHOOD CLASSIFIER (MLC); PRINCIPAL COMPONENTS ANALYSIS (PCA);

REMOTE SENSING;

EID: 0031646486     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/36.655328     Document Type: Article

References (26)

1. G. Vane and A. F. H. Goetz, Terrestrial imaging spectroscopy Remote Sens. Environ., vol. 24, pp. 1-29, 1988.
2. K. Fukanaga, Introduction to Statistical Pattern Recognition, 2nd ed. New York: Academic, 1990.
3. R. A. Schowengerdt, Techniques for Image Processing and Classifications in Remote Sensing. New York: Academic, 1983.
4. W. Eppler, Canonical analysis for increased classification speed and channel selection IEEE Trans. Geosci. Electron., vol. GE-14, pp. 26-33, 1976.
5. P. H. Swain and R. C. King, Two effective feature selection criteria for multispectral remote sensing in Proc. First Int. Joint Conf. Pattern Recognition, 1973, pp. 536-540.
6. A. Richards, Remote Sensing Digital Image Analysis, 2nd ed. New York: Springer-Verlag, 1993.
7. C. Lee and D. A. Landgrebe, Fast likelihood classifier IEEE Trans. Geosci. Remote Sensing, vol. 29, pp. 509-517, 1991.
8. X. Jia and J. A. Richards, Efficient maximum likelihood classification for imaging spectrometer data sets IEEE Trans. Geosci. Remote Sensing, vol. 32, pp. 274-281, 1994.
9. J. Settle and S. A. Briggs, Fast maximum likelihood classification of remotely-sensed imagery Int. J. Remote Sensing, vol. 8, pp. 723-734, 1987.
10. H. Foley and J. W. Sammon, An optimal set of discriminant vector IEEE Trans. Comput., vol. C-24, pp. 281-289, 1975.
11. A. Campbell and W. R. Atchley, The geometry of canonical variate analysis Syst. Zoology, vol. 30, pp. 268-280, 1981.
12. B. Kim and D. A. Landgrebe, Hierarchical classifier design in highdimensional, numerous class cases IEEE Trans. Geosci. Remote Sensing, vol. 29, pp. 518-528, 1991.
13. W. Mansel, W. J. Kramber, and J. K. Lee, Optimum band selection for supervised classification of multispectral data Photogramm. Eng. Remote Sensing, vol. 56, pp. 55-60, 1990.
14. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.
15. A. Basilevsky, Statistical Factor Analysis and Related Method: Theory and Application. New York: Wiley, 1994.
16. R. Harter, The optimality of Winograd's formula Commun. ACM, vol. 15, pp. 352-353, 1972.
17. L. Kronsjo, Algorithm: Their Complexity and Efficiency, 2nd ed. Singapore: Wiley, 1987.
18. B. Venkateswarlu and P. S. V. S. K. Raju, Winograd's method: A perspective for some pattern recognition problems Pattern Recognit. Lett., vol. 15, pp. 105-109, 1994.
19. H. Swain and S. M. Davis, Remote Sensing: The Quantitative Approach. New York: McGraw-Hill, 1978.
20. C. Conese and F. Maselli, Selection of optimum bands from TM scenes through mutual information analysis ISPRS J. Photogramm. Remote Sens., vol. 48, pp. 2-11, 1993.
21. Lee and D. A. Landgrebe, Feature extraction based on decision boundaries IEEE Trans. Pattern Anal. Machine Intel!., vol. 15, pp. 38800, 1993.
22. F. Hughes, On the accuracy of statistical pattern recognizers IEEE Trans. Inform. Theory, vol. IT-14, pp. 55-63, 1968.
23. B. Venkateswarlu and P. S. V. S. K. Raju, Three stage ML classifier Pattern Recognition, vol. 24, pp. 1113-1116, 1991.
24. E. Hodgson, Reducing the computational requirements of the minimum distance classifier Remote Sens. Environ., vol. 25, pp. 117-128, 1988.
25. P. M. Mather, Computationally-efficient maximum likelihood classifier employing prior probabilities for remotely-sensed data Int. J. Remote Sensing, vol. 6, pp. 369-376, 1985.
26. L. Biehl etal, A crops and soils data base for scene radiation research in Proc. Machine Processing Remotely Sensed Data Symp., 1982, pp. 169-177.