Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 947453, 11 pages
http://dx.doi.org/10.1155/2014/947453
Research Article

Approximate Sparse Regularized Hyperspectral Unmixing

1Department of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
2School of Art & Design, Zhejiang Sci-Tech University, Hangzhou 310018, China

Received 27 January 2014; Revised 18 June 2014; Accepted 19 June 2014; Published 17 August 2014

Academic Editor: Fazal M. Mahomed

Copyright © 2014 Chengzhi Deng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. P. Shippert, “Why use hyperspectral imagery?” Photogrammetric Engineering and Remote Sensing, vol. 70, no. 4, pp. 377–380, 2004. View at Google Scholar · View at Scopus
  2. J. M. Bioucas-Dias, A. Plaza, N. Dobigeon et al., “Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 5, no. 2, pp. 354–379, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. T. Chan, C. Chi, Y. Huang, and W. Ma, “A convex analysis-based minimum-volume enclosing simplex algorithm for hyperspectral unmixing,” IEEE Transactions on Signal Processing, vol. 57, no. 11, pp. 4418–4432, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  4. T. Chan, W. Ma, A. Ambikapathi, and C. Chi, “A simplex volume maximization framework for hyperspectral endmember extraction,” IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 11, pp. 4177–4193, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. F. Schmidt, A. Schmidt, E. Tréguier, M. Guiheneuf, S. Moussaoui, and N. Dobigeon, “Implementation strategies for hyperspectral unmixing using Bayesian source separation,” IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 11, pp. 4003–4013, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Arngren, M. N. Schmidt, and J. Larsen, “Unmixing of Hyperspectral images using bayesian non-negative matrix factorization with volume prior,” Journal of Signal Processing Systems, vol. 65, no. 3, pp. 479–496, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. M.-D. Iordache, J. M. 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. View at Publisher · View at Google Scholar · View at Scopus
  8. J. B. Greer, “Sparse demixing of hyperspectral images,” IEEE Transactions on Image Processing, vol. 21, no. 1, pp. 219–228, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  9. Y. F. Zhong, R. Y. Feng, and L. P. Zhang, “Non-local sparse unmixing for hyperspectral remote sensing imagery,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 6, pp. 1889–1909, 2014. View at Publisher · View at Google Scholar
  10. M. Iordache, J. M. Bioucas-Dias, and A. Plaza, “Total variation spatial regularization for sparse hyperspectral unmixing,” IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 11, pp. 4484–4502, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. M.-D. Iordache, J. M. Bioucas-Dias, and A. Plaza, “Collaborative sparse unmixing of hyperspectral data,” in Proceedings of the 32nd IEEE International Geoscience and Remote Sensing Symposium (IGARSS '12), pp. 7488–7491, July 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. M.-D. Iordache, J. M. Bioucas-Dias, and A. Plaza, “Collaborative sparse unmixing of hyperspectral data,” in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS '12), pp. 7488–7491.
  13. M. Iordache, A. J. Plaza, and J. M. Bioucas-Dias, “Recent developments in sparse hyperspectral unmixing,” in Proceedings of the 30th IEEE International Geoscience and Remote Sensing Symposium (IGARSS '10), pp. 1281–1284, July 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. E. J. Candès and T. Tao, “Decoding by linear programming,” IEEE Transactions on Information Theory, vol. 51, no. 12, pp. 4203–4215, 2005. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  15. E. J. Candes and T. Tao, “Near-optimal signal recovery from random projections: universal encoding strategies?” IEEE Transactions on Information Theory, vol. 52, no. 12, pp. 5406–5424, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  16. H. Mohimani, M. Babaie-Zadeh, and C. Jutten, “A fast approach for overcomplete sparse decomposition based on smoothed l0 norm,” IEEE Transactions on Signal Processing, vol. 57, no. 1, pp. 289–301, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  17. J. H. Wang, Z. Y. Huang, Y. Y. Zhou, and F. H. Wang, “Robust sparse recovery based on approximate l0 norm,” Acta Electronica Sinica, vol. 40, no. 6, pp. 1185–1189, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. A. M. Bruckstein, D. L. Donoho, and M. Elad, “From sparse solutions of systems of equations to sparse modeling of signals and images,” SIAM Review, vol. 51, no. 1, pp. 34–81, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  19. A. M. Bruckstein, M. Elad, and M. Zibulevsky, “On the uniqueness of nonnegative sparse solutions to underdetermined systems of equations,” IEEE Transactions on Information Theory, vol. 54, no. 11, pp. 4813–4820, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  20. S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM Review, vol. 43, no. 1, pp. 129–159, 2001. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  21. F. Chen and Y. Zhang, “Sparse hyperspectral unmixing based on constrained p-2 optimization,” IEEE Geoscience and Remote Sensing Letters, vol. 10, no. 5, pp. 1142–1146, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. L. Sun, Z. B. Wu, L. Xiao, J. J. Liu, Z. H. Wei, and F. X. Dang, “A novel sparse regression method for hyperspectral unmixing,” International Journal of Remote Sensing, vol. 34, no. 20, pp. 6983–7001, 2013. View at Publisher · View at Google Scholar
  23. J. M. Bioucas-Dias and M. A. T. Figueiredo, “Alternating direction algorithms for constrained sparse regression: application to hyperspectral unmixing,” in Proceedings of the IEEE 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS '10), pp. 1–4, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. M. V. Afonso, J. M. Bioucas-Dias, and M. A. T. 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. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  25. http://speclab.cr.usgs.gov/spectral.lib06.
  26. J. Nascimento and J. Bioucas-Dias, “Vertex component analysis: a fast algorithm to unmix hyperspectral data,” IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 4, pp. 898–910, 2005. View at Publisher · View at Google Scholar · View at Scopus