Methodology for hyperspectral band and classification model selection

2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data

Volumn , Issue , 2004, Pages 120-128

  Groves, P ^a   Bajcsy, P ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

REMOTE SENSING; SPACE RESEARCH; SPECTROSCOPY;

CLASSIFICATION ACCURACY; CLASSIFICATION METHODS; CLASSIFICATION MODELS; COMPUTATIONAL REQUIREMENTS; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL DATA ANALYSIS; INFORMATION CONTENTS; STATISTICAL MODELING;

COMPUTATIONAL EFFICIENCY;

EID: 84945243929     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WARSD.2003.1295183     Document Type: Conference Paper

References (16)

1. G. F. Hughes, "On the mean accuracy of statistical pattern recognizers," IEEE Trans. Inform. Theory, vol. IT-14, no. 1, January 1968.
2. J. C. Price, "Band selection procedure for multispectral scanners," Applied Optics, vol. 33, no. 15, pp. 3281-3288, 1994.
3. J. A. Benediktsson, J. R. Sveinsson, and K. Arnason, "Classification and feature extraction of AVIRIS data," IEEE Trans. Geosci. Remote Sensing, vol. 33, pp. 1194-1205, 1995.
4. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Recognition, Second Edition. New York: Wiley-Interscience, 2000.
5. R. Kohavi and G. H. John, "Wrappers for feature subset selection," Artificial Intelligence, vol. 97, no. 1-2, pp. 273-324, 1997. [Online]. Available: citeseer.nj.nec.com/article/kohavi97wrappers.html
6. J. C. Russ, The Image Processing Handbook, 3rd ed. CRC Press, 1999.
7. D. J. Wiersma and D. A. Landgrebe, "Analytical design of multispectral sensors," IEEE Trans. Geosci. Remote Sensing, vol. 18, no. 2, pp. 180-189, 1980.
8. J. B. Campbell, Introduction to Remote Sensing, 2nd ed. New York: The Guilford Press, 1996.
9. T. Warner, K. Steinmaus, and H. Foote, "An evaluation of spatial autocorrelation-based feature selection," International Journal of Remote Sensing, vol. 20, no. 8, pp. 1601-1616, 1999.
10. X. Jia and J. A. Richards, "Efficient maximum likelihood classification for imaging spectrometer data sets," IEEE Trans. Geosci. Remote Sensing, vol. 32, no. 2, pp. 274-281, March 1994.
11. L. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers, 2000.
12. J. Han and M. Kamber, Data Mining: Concepts and Techniques. San Francisco, California: Morgan Kaufmann Publishers, 2001.
13. J. R. Quinlan, C4.5: Programs for Machine Learning. San Francisco: Morgan Kaufmann Publishers, 1993.
14. G. Vane, "Airborne visible/infrared imaging spectrometer (AVIRIS): Description of the sensor, ground data processing facility, laboratory calibration, and first results," Presented at Imaging Spectroscopy II Conference, San Diego, California, August 1987, Jet Propulsion Lab, NASA, Pasedena, California, Tech. Rep. JPL Publication 87-38, November 1987. [Online]. Available: http://aviris.jpl.nasa.gov/
15. C. Wessman. (1999, October) Hyperspectral imagery with gramma labels. CU-CIRES-CSES, NASA Airborne Science Program, AVIRIS Project, Sevilleta LTER Database. [Online]. Available: http://sevilleta. unm.edu/data/archive/synopses/RS datasets update.html
16. D. J. Sheskin, Handbook of Parametric and Nonparametric Statistical Procedures, Second Edition. Boca Raton, FL: Champman & Hall/CRC, 2000.