Sparse demixing

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 7695, Issue , 2010, Pages

  Greer, John B ^a  

^a National Geospatial Intelligence Agency   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONSTRAINED LEAST SQUARES; DE-MIXING; DICTIONARY LEARNING; ENDMEMBERS; HIGH-DIMENSIONAL; HYPER-SPECTRAL IMAGES; IMAGE SPECTRA; LINEAR MIXTURE MODELS; MATCHING PURSUIT; SPECTRAL LIBRARIES; SPECTRAL SIGNATURE;

LEARNING ALGORITHMS; SIGNAL PROCESSING;

PIXELS;

EID: 77953753455     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.850346     Document Type: Conference Paper

References (14)

1. M. J. Abrams and R. P. Ashley. Alteration mapping using multispectral images - cuprite mining district, esmeralda county, nevada. USGS Open File Report 80-367, 1980.
2. M. Aharon, M. Elad, and A. Bruckstein. K-svd: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 54(11):553-572, November 2006.
3. J. W. Boardman. Analysis, understanding and visualization of hyperspectral data as convex sets in n-space. Proceedings of SPIE, 2480:14-22, 1995.
4. J. H. Bowles and D. B. Gillis. An optical real-time adaptive spectral identification system (orasis). In C-I Chang, editor, Hyperspectral Data Exploitation: Theory and Applications. John Wiley & Sons, Inc., 2007.
5. R. N. Clark, G. A. Swayze, and A. Gallagher. Mapping minerals with imaging spectroscopy. USGS Office of Mineral Resources Bulletin 2039, pages 141-150, 1993. http://speclab.cr.usgs.gov.
6. K. Engan, S. O. Aase, and J. H. Husøy. Method of optimal directions for frame design. IEEE International Conference on Acoustics, Speech, and Signal Processing, 5:2443-2446, 1999.
7. Z. Guo, T. Wittman, and S. Osher. L1 unmixing and its application to hyperspectral image enhancement. In Proceedings of SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, April 2009.
8. N. Keshava and J. F. Mustard. Spectral unmixing. IEEE Signal Processing Magazine, 19(1):44-57, 2002.
9. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Supervised dictionary learning, 2008.
10. S. Mallat and Z. Zhang. Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Process., 41(12):3397-3415, 1993.
11. B. A. Olshausen and D. J. Field. Emergence of simple-cell receptive-field properties by learning a sparse code for natural images. Nature, 381(6583):607-609, 1996.
12. R. G. Resmini, M. E. Kappus, W. S. Aldrich, J. C. Harsanyi, and M. Anderson. Mineral mapping with hyperspectral digital imagery collection experiment (hydice) sensor data at cuprite, nevada, u.s.a. Int. J. Remote Sensing, 18(7):1553-1570, 1997.
13. M. E. Winter. N-findr: An algorithm for fast autonomous spectral end-member determination in hyperspectral data. Proceedings of SPIE Conference on Imaging Spectromet ry V, 3753:266-275, 1999.
14. M. E. Winter. A proof of the n-findr algorithm for the automated detection of endmembers in a hyperspectral image. Proceedings of SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, 5425:31-41, 2004.