Wavelength band selection method for multispectral target detection

Applied Optics

Volumn 41, Issue 32, 2002, Pages 6786-6795

  Karlholm, Jörgen ^a   Renhorn, Ingmar ^a  

^a SWEDISH DEFENCE RESEARCH AGENCY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

CLUTTER (INFORMATION THEORY); COSTS; FUNCTIONS; PROBABILITY; RADAR TARGET RECOGNITION; SENSORS; SPECTRUM ANALYSIS;

MULTISPECTRAL SENSORS;

MULTISPECTRAL SCANNERS;

EID: 0037058442     PISSN: 1559128X     EISSN: 21553165     Source Type: Journal    
DOI: 10.1364/AO.41.006786     Document Type: Article

References (19)

1. J. C. Price, “Band selection procedure for multispectral scanners,” Appl. Opt. 33, 3281-3288 (1994).
2. A. Kanodia, R. C. Hardie, and R. O. Johnson, “Band selection and performance analysis for multispectral target detectors using truthed Bomem spectrometer data,” in Proc. IEEE National Aerospace Electronics Conference (NAECON), B. Moore, ed., 1, 33-40 (1996).
3. R. C. Hardie, M. Vaidyanathan, and P. F. McManamon, “Spectral band selection and classifier design for a multispectral imaging laser radar,” Opt. Eng. 37, 752-762 (1998).
4. K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed. (Academic, San Diego, Calif., 1990).
5. S. Mallat, A Wavelet Tour of Signal Processing (Academic, San Diego, Calif., 1998).
6. J. C. Harsanyi and C.-I. Chang, “Hyperspectral image classification and dimensionality reduction: an orthogonal sub space projection approach,” IEEE Trans. Geosci. Remote Sens. 32, 779-785 (1994).
7. B. S. Everitt, An Introduction to Latent Variable Models (Chapman and Hall, London, 1984), ISBN 0-412-25310-0.
8. S. M. Kay, Fundamentals of Statistical Signal Processing: Detection Theory (Prentice-Hall, Englewood Cliffs, N.J., 1998), ISBN 0-13-504135-X.
9. D. Manolakis, G. Shaw, and N. Keshava, “Comparative analysis of hyperspectral adaptive matched filter detectors,” in Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, S. S. Chen and M. R. Descour, eds., Proc. SPIE 4049, 2-17 (2000).
10. D. Manolakis and G. Shaw, “Detection algorithms for hyperspectral imaging applications,” IEEE Signal Process. Mag. 19, 29-43 (2002).
11. G. M. Davis, S. Mallat, and M. Avellaneda, “Greedy adaptive approximations,” J. Const. Appr. 13, 57-98 (1997).
12. S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Comput. 20, 33-61 (1998).
13. S. Mallat and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Trans. Signal Process. 41, 3397-3415 (1993).
14. G. M. Davis, S. Mallat, and Z. Zhang, “Adaptive time-frequency approximations,” Opt. Eng. 33, 2183-2191 (1994).
15. G. W. Stewart, On the Early History of the Singular Value Decomposition, Technical Report TR-92-31, Dept. of Computer Science, University of Maryland, College Park, 1992, ftp://thales.cs.umd.edu/pub/reports/ehsvd.ps.
16. A. Berk, L. S. Bernstein, and D. C. Robertson, “modtran, a moderate resolution model for LOWTRAN 7,” Technical Report GL-TR-89-0122, Air Force Geophysics Laboratory, Hanscom Air Force Base, Mass., (1989).
17. S. F. Cotter, B. D. Rao, and K. Kreutz-Delgado, “Forward sequential algorithms for best basis selection,” IEE Proc. Vision Image Signal Process. 146, 235-244 (1999).
18. B. K. Natarajan, “Sparse approximate solutions to linear systems,” SIAM J. Comput. 24, 227-234 (1995).
19. D. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning (Addison-Wesley, Reading, Mass., 1989).