Linearly constrained band selection for hyperspectral imagery

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 6233 II, Issue , 2006, Pages

  Wang, Su ^a   Chang, Chein I ^a  

^a Baltimore County   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAND CORRELATION CONSTRAINT (BCC); BAND CORRELATION MINIMIZATION (BCM); CONSTRAINED ENERGY MINIMIZATION (CEM); HYPERSPECTRAL IMAGERY;

ADAPTIVE SYSTEMS; ALGORITHMS; CONSTRAINT THEORY; IMAGE PROCESSING; OPTIMIZATION; RADAR TARGET RECOGNITION;

IMAGE SENSORS;

EID: 33748662021     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.665286     Document Type: Conference Paper

References (23)

1. P.W. Mausel, W.J. Kramber an d J.K. Lee, "Optimal band selection for supervised classification of multispectral data," Photogrammet. Eng. Remote Sens., vol. 56, no. 1, pp. 55-60, Jan. 1990.
2. C. Conese and F. Maselli, " Selection of optimum bands from TM scenes thorugh mutual information analysis," ISPRS Photogrammet. Eng. Remote Sens., vol. 48, no. 3, pp. 2-11, Jan. 1993.
3. S.D. Stearns, B.E. Wilson and J.R. Peterson, "Dimesnionality reduction by optimal band selection for pixel classification of hyperspectral imagery," Applications of Digital Image Processing, XVI, vol. 2028, pp. 118-127, 1993.
4. C.-I Chang, Q. Du, T.S. Sun and M.L.G. Althouse, "A joint band prioritization and band decorrelation approach to band selection for hyperspectral image classification," IEEE Trans. on Geoscience and Remote Sensing, vol. 37, no. 6, pp. 2631-2641, November 1999.
5. R. Huang and M. He, "Band selection based feature weighting for classification of hyperspectral data," IEEE Trans. on Geoscience and Remote Sensing Letters, vol. 2, no. 2, pp. 156-159, April 2005.
6. C.-I Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Kluwer Academic/Plenum Publishers, New York, N.Y., 2003.
7. B. Ramakrishna, J. Wang, A. Plaza and C.-I Chang, "Spectral/spatial hyperspectral image compression in conjunction with virtual dimensionality, " Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, SPIE Symposium on Defense and Security, SPIE Vol. 5806, Orlando, Florida, 28 March 28-1 April, 2005.
8. J. Wang and C.-I Chang, "Dimensionality reduction by independent component analysis for hyperspectral image analysis," IEEE International Geoscience and Remote Sensing Symposium, Seoul, Korea, July 25-29, 2005.
9. C.-I Chang and A. Plaza, "Fast iterative algorithm for implementation of pixel purity index," IEEE Trans. on Geoscience and Remote Sensing Letters, (to be published).
10. O.L. Frost III, "An algorithm for linearly constrained adaptive array processing," Proc. IEEE, vol. 60, pp. 926-935, 1972.
11. Joseph C. Harsanyi, Detection and Classification of Subpixel Spectral Signatures in Hyperspectral Image Sequences, Department of Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, August 1993.
12. C.-I Chang, "Target signature-constrained mixed pixel classification for hyperspectral imagery," IEEE Trans. on Geoscience and Remote Sensing, vol. 40, no. 5, pp. 1065-1081, May 2002.
13. T. Cover and J. Thomas, Elements of Information Theory, John Wiley & Sons, New York, 1991.
14. J.C. Harsanyi, W. Farrand and C.-I Chang, "Detection of subpixel spectral signatures in hyperspectral image sequences," Annual Meeting, Proceedings of American Society of Photogrammetry & Remote Sensing, Reno, pp. 236-247, 1994.
15. H.V. Poor, An Introduction to Detection and Estimation Theory, 2nd. ed., New York: Springer-Verlag, 1994.
16. D. Heinz and C.-I Chang, "Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imagery," IEEE Trans. on Geoscience and Remote Sensing, vol. 39, no. 3, pp. 529-545, March 2001.
17. H. Ren and C.-I Chang, "Automatic spectral target recognition in hyperspectral imagery," IEEE Trans. on Aerospace and Electronic Systems, vol. 39, no. 4, pp. 1232-1249, October 2003.
18. J. Harsanyi and C.-I Chang, "Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach," IEEE Trans. on Geoscience and Remote Sensing, vol. 32, no. 4, pp. 779-785, July, 1994
19. [19 http://speclab.cr.usgs.gov/cuprite.html.
20. J. Wang and C.-I Chang, "Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis," IEEE Trans. on Geoscience and Remote Sensing, (revised)
21. M.E. Winter, "N-finder: an algorithm for fast autonomous spectral endmember determination in hyperspectral data," Image Spectrometry V, Proc. SPIE 3753, pp. 266-277, 1999.
22. J.W. Boardman, F.A. Kruse and R.O. Green, "Mapping target signatures via partial unmixing of AVIRIS data," Summaries of JPL Airborne Earth Science Workshop, Pasadena, CA, 1995.
23. C.-I Chang, "Utility of virtual dimensionality in hyperspectral data exploitation," submitted to SPIE Defense and Security Symp., Algorithms and Techniques for Multspectral, Hyperspectral, and Ultraspectral Imagery XII (OR52), April 17-21, Orland, Florida, 2006.