Collaborative sparse unmixing of hyperspectral data

International Geoscience and Remote Sensing Symposium (IGARSS)

Volumn , Issue , 2012, Pages 7488-7491

  Iordache, Marian Daniel ^a,c   Bioucas Dias, Jose M ^b   Plaza, Antonio ^c  

^a FLEMISH INSTITUTE FOR TECHNOLOGICAL RESEARCH VITO   (Belgium)

^b INSTITUTO SUPERIOR TÉCNICO   (Portugal)

^c UNIVERSITY OF EXTREMADURA   (Spain)

Author keywords

collaborative sparse coding; sparse regression; spectral library; Spectral unmixing

Indexed keywords

CONSTITUENT MATERIALS; DIMENSIONAL SUBSPACE; ENDMEMBERS; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL DATA; OBSERVED DATA; SPARSE CODING; SPARSE REGRESSION; SPECTRAL LIBRARIES; SPECTRAL SIGNATURE; SPECTRAL UNMIXING; UNMIXING;

GEOLOGY; REMOTE SENSING; SPECTROSCOPY;

PIXELS;

EID: 84873119738     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IGARSS.2012.6351900     Document Type: Conference Paper

References (14)

1. D. Iordache, J. Bioucas-Dias, and A. Plaza, "Sparse unmixing of hyperspectral data," IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 6, pp. 2014-2039, 2011.
2. R. Green, M. Eastwood, C. Sarture, T. Chrien, M. Aronsson, B. Chippendale, J. Faust, B. Pavri, C. Chovit, M. Solis, et al., "Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS)," Remote Sensing of Environment, vol. 65, no. 3, pp. 227- 248, 1998.
3. A. Bruckstein, D. Donoho, and M. Elad, "From sparse solutions of systems of equations to sparse modeling of signals and images," SIAM Review, vol. 51, pp. 34-81, 2009.
4. E. Candès, J. Romberg, and T. Tao, "Stable signal recovery from incomplete and inaccurate measurements," Communications on Pure and Applied Mathematics, vol. 59, no. 8, pp. 1207, 2006.
5. A. Bruckstein, M. Elad, and M. Zibulevsky, "On the uniqueness of nonnegative sparse solutions to underdetermined systems of equations," Information Theory, IEEE Transactions on, vol. 54, no. 11, pp. 4813- 4820, 2008.
6. J. Tropp, "Algorithms for simultaneous sparse approximation. part ii: Convex relaxation," Signal Processing, vol. 86, no. 3, pp. 589-602, 2006.
7. M. Mishali and Y. Eldar, "Reduce and boost: Recovering arbitrary sets of jointly sparse vectors," IEEE Transactions on Signal Processing, vol. 56, no. 10, pp. 4692-4702, October 2008.
8. Y. Eldar and H. Rauhut, "Average case analysis of multichannel sparse recovery using convex relaxation," IEEE Transactions on Information Theory, vol. 56, no. 1, pp. 505-519, 2010.
9. B. Turlach, W. Venables, and S. Wright, "Simultaneous variable selection," Technometrics, vol. 27, pp. 349-363, 2004.
10. J. Bioucas-Dias and M. Figueiredo, "An alternating direction algorithm for collaborative hierarchical sparse regularization," Technical Report, IST, 2011.
11. J. Bioucas-Dias and M. Figueiredo, "Alternating direction algorithms for constrained sparse regression: application to hyperspectral unmixing," 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2010.
12. M. Afonso, J. Bioucas-Dias, and M. Figueiredo, "An augmented Lagrangian approach to the constrained optimization formulation of imaging inverse problems," IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 681 -695, 2011.
13. J. Eckstein and D. Bertsekas, "On the DouglasRachford splitting method and the proximal point algorithm for maximal monotone operators," Mathematical Programming, vol. 5, pp. 293-318, 1992.
14. R. Clark, G. Swayze, K. Livo, R. Kokaly, S. Sutley, J. Dalton, R. Mc- Dougal, and C. Gent, "Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems," Journal of Geophysical Research, vol. 108, [E12], pp. 5131, 2003.