Skip to main content
SpringerLink
Log in

Efficient sparse unmixing analysis for hyperspectral imagery based on random projection

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Hyperspectral imagery including rich spectral information could be applied to detect and identify objects at a distance. In this paper, we concentrate on the surface material identification of interested objects within the domain of space object identification (SOI) and geological survey. One of the approaches is the unmixing analysis that identifies the components (called endmembers) in each pixel and estimates their corresponding fractional abundances, and then, we could obtain the space distributions of substances. To solve this problem, we present an approach in a semi-supervised fashion, by assuming that the measured spectrum is expressed in the form of linear combination of a number of pure spectral signatures in a spectral library and the fractional abundances are their weights. Thus, the abundances are sparse and we propose a sparse regression model to realize the sparse unmixing analysis. We apply random projection technique to accelerate the sparse unmixing process and use split Bregman iteration to optimize the objective function. Our algorithm is tested and compared with other classic algorithms by using simulated hyperspectral images and a real-world image.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Adams JB, Smith MO, Johnson PE (1986) Spectral mixture modeling: a new analysis of rock and soil types at the Viking Lander 1 site. J Geophys Res 91: 8098–8112

    Article  Google Scholar 

  2. Bioucas-Dias JM, Plaza A (2010) Hyperspectral unmixing: geometrical, statistical, and sparse regression-based approaches. In: Proceedings of SPIE international society for optical engineering V, 7830

  3. Boardman JW, Kruse FA, Green RO (1995) Mapping target signatures via partial unmixing of AVIRIS data. In: Proceedings of JPL airborne earth science workshop, pp 23–26

  4. Winter ME (2003) N-FINDR: an algorithm for fast autonomous spectral endmember determination in hyperspectral data. In: Proceedings of SPIE image spectrometry V, 3753: 266–277

  5. Ren H, Chang C-I (2003) Automatic spectral target recognition in hyperspectral imagery. IEEE Trans Aerosp Electron Syst 9(4): 1232–1249

    Google Scholar 

  6. Nascimento JMP, Bioucas-Dias JM (2005) Vertex component analysis: a fast algorithm to unmix hyperspectral data. IEEE Trans Geosci Remote Sens 43(4): 898–910

    Article  Google Scholar 

  7. Iordache M-D, Bioucas-Dias JM, Plaza A (2011) Sparse unmixing of hyperspectral data. IEEE Trans Geosci Remote Sens 49(6): 2014–2039

    Article  Google Scholar 

  8. Chan TH, Chi CY, Huang YM, Ma WK (2009) A convex analysis-based minimum-volume enclosing simplex algorithm for hyperspectral unmixing. IEEE Trans Geosci Remote Sens 47(11): 4418–4432

    MathSciNet  Google Scholar 

  9. Chen J, Jia X, Yang W, Matsushita B (2009) Generalization of subpixel analysis for hyperspectral data with flexibility in spectral similarity measures. IEEE Trans Geosci Remote Sens 47(7): 2165–2171

    Article  Google Scholar 

  10. Vempala S (2004) The random projection method. American Mathematical Society, Providence

    MATH  Google Scholar 

  11. Achlioptas D (2003) Database-friendly random projections: Johnson–Lindenstrauss with binary coins. J Comput Syst Sci 66(4): 671–687

    Article  MathSciNet  MATH  Google Scholar 

  12. Newman MEJ (2005) Power laws, pareto distributions and zipf’s law. Contemp Phys 46(5): 232–351

    Article  Google Scholar 

  13. Achlioptas D, McSherry F, Scholkopf B (2001) Sampling techniques for kernel methods. In: Proceedings of NIPS, pp 335–342

  14. Arriaga R, Vempala S (1999) An algorithmic theory of learning: robust concepts and random projection. In: Proceedings of FOCS (also to appear in machine learning), pp 616–623

  15. Goldstein T, Osher S (2009) The split Bregman method for l1 regularized problems. SIAM J Imaging Sci 2(2):323–343

    Article  MathSciNet  MATH  Google Scholar 

  16. Pati YC, Rezahfar R, Krishnaprasad P (2003) Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: Proceedings of the 27th annual asilomar conference on signals, systems and computers, Los Alamitos

  17. Chen S, Donoho D, Saunders M (2001) Atomic decomposition by basis pursuit. SIAM Rev 43(1): 129C159

    Article  MathSciNet  Google Scholar 

  18. Zhang Q, Wang H, Plemmons R, Pauca P (2008) Tensor methods for hyperspectral data analysis: a space object material identification study. J Opt Soc Am A 25(12): 3001–3012

    Article  Google Scholar 

  19. http://speclab.cr.usgs.gov/spectral-lib.html

  20. Swayze GA, Clark RL, Sutley S, Gallagher AJ (1992) Ground-truthing AVIRIS mineral mapping at cuprite, nevada. In: Summaries of the 3rd annual JPL airborne geoscience workshop, vol 1, pp 47–49

  21. Miao LD, Qi HR (2007) Endmember extraction from highly matrix data using minimum volume constrained nonnegative matrix factorization. IEEE Trans Geosci Remote Sens 45(3):765–777

    Article  Google Scholar 

Download references

Acknowledgments

The work was supported by the National Natural Science Foundation of China under the Grants 61273245, 60975003 and 91120301, the 973 Program under the Grant 2010CB327904, the open funding project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (Grant No. BUAA-VR-12KF-07), the Beijing Natural Science Foundation (Non-negative Component Analysis for Hyperspectral Imagery Unmixing) under the Grant 4112036, and Program for New Century Excellent Talents in University of Ministry of Education of China under the Grant NCET-11-0775.

Author information

Authors and Affiliations

  1. Image Processing Center, School of Astronautics, Beihang University, Beijing, 100191, People’s Republic of China

    Zhenwei Shi, Liu Liu, Xinya Zhai & Zhiguo Jiang

  2. Beijing Key Laboratory of Digital Media, Beihang University, Beijing, 100191, People’s Republic of China

    Zhenwei Shi

  3. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, People’s Republic of China

    Zhenwei Shi

Authors
  1. Zhenwei Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xinya Zhai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhiguo Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhenwei Shi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shi, Z., Liu, L., Zhai, X. et al. Efficient sparse unmixing analysis for hyperspectral imagery based on random projection. Neural Comput & Applic 23, 2281–2293 (2013). https://doi.org/10.1007/s00521-012-1179-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-012-1179-8

Keywords

Search

Navigation