Skip to main content
SpringerLink
Log in

Blind Hyperspectral Unmixing Using Deep-Independent Information

  • Conference paper
  • First Online:
Intelligent Computing Theories and Application (ICIC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9772))

Included in the following conference series:

  • 1807 Accesses

Abstract

In linear mixing model (LMM), the endmember fractional abundances should satisfy the sum-to-one constraint, which makes the well-known independent component analysis (ICA) based blind source separation (BSS) algorithms not well suited to blind hyperspectral unmixing (bHU) problem. A novel framework for bHU consulting dependent component analysis (DCA) is presented in this paper. By using the idea of subband decomposition, wavelet packet decomposition based bHU algorithm (termed as SDWP-bHU) is proposed, where the deep independent information of the source signals is exploited to fulfill the endmember signatures extraction and abundances separation tasks. Experiments based on the synthetic data are performed to evaluate the validity of the proposed approach.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ma, W.-K., Bioucas-Dias, J.M., Chan, T.-H., Gillis, N., Gader, P., Plaza, A., Ambikapathi, A., Chi, C.-Y.: A signal processing perspective on hyperspectral unmixing. IEEE Signal Process. Mag. 31(1), 67–81 (2014)

    Article  Google Scholar 

  2. Bioucas-Dias, J.M., Plaza, A., Dobigeon, N., Parente, M., Du, Q., Gader, P., Chanussot, J.: Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 5(2), 354–379 (2012)

    Article  Google Scholar 

  3. Heylen, R., Burazerovic, D., Scheunders, P.: Fully constrained least squares spectral unmixing by simplex projection. IEEE Trans. Geosci. Remote Sens. 49(11), 4112–4122 (2011)

    Article  Google Scholar 

  4. Zhang, K., Chan, L.W.: An adaptive method for subband decomposition ICA. Neural Comput. 18(1), 191–223 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  5. Lin, C.-H., Ma, W.-K., Li, W.-C., Chi, C.-Y., Ambikapathi, A.: Identifiability of the simplex volume minimization criterion for blind hyperspectral unmixing: The no pure-pixel case. IEEE Trans. Geosci. Remote Sens. 53(10), 5530–5546 (2015)

    Article  Google Scholar 

  6. Nascimento, J., Dias, J.: Does independent component analysis play a role in unmixing hyperspectral data? IEEE Trans. Geosci. Remote Sens. 43(1), 175–187 (2005)

    Article  Google Scholar 

  7. Cardoso, J.F.: Multidimensional independent component analysis. In: Proceedings of ICASSP 1998, pp. 1941–1944. MIT Press (1998)

    Google Scholar 

  8. Szabo, Z., Poczos, B., Lorincz, A.: Separation theorem for independent subspace analysis and its consequences. Pattern Recogn. 45(4), 1782–1791 (2012)

    Article  MATH  Google Scholar 

  9. Caiafa, C.F., Salerno, E., Protoa, A.N., Fiumi, L.: Blind spectral unmixing by local maximization of non-Gaussianity. Sig. Process. 88(1), 50–68 (2008)

    Article  MATH  Google Scholar 

  10. Ouedraogo, W., Souloumiac, A., Jaidane, M., Jutten, C.: Non-negative blind source separation algorithm based on minimum aperture simplicial cone. IEEE Trans. Signal Process. 62(2), 376–389 (2014)

    Article  MathSciNet  Google Scholar 

  11. Erdogan, A.T.: A class of bounded component analysis algorithms for the separation of both independent and dependent sources. IEEE Trans. Signal Process. 61(22), 5730–5743 (2013)

    Article  MathSciNet  Google Scholar 

  12. Wang, F.S., Wang, Z.Y., Li, R., Zhang, L.R.: An efficient algorithm for harmonic retrieval by combining blind source separation with wavelet packet decomposition. Digit. Signal Proc. 46, 133–150 (2015)

    Article  MathSciNet  Google Scholar 

  13. Mallat, S.: A Wavelet Tour of Signal Processing. Academic Press, New York (1999)

    MATH  Google Scholar 

  14. Cardoso, J.F.: Dependence, correlation and gaussianity in independent component analysis. J. Mach. Learn. Res. 4, 1177–1203 (2003)

    MathSciNet  MATH  Google Scholar 

  15. Comon, P., Jutten, C.: Handbook of Blind Source Separation: Independent Component Analysis and Applications. Elsevier, Oxford (2010)

    Google Scholar 

  16. Kopriva, I., Sersic, D.: Wavelet packets approach to blind separation of statistically dependent sources. Neurocomputing 71(7–9), 1642–1655 (2008)

    Article  Google Scholar 

  17. Li, Y., Cichocki, A., Amari, S.: Analysis of sparse representation and blind source separation. Neural Comput. 16(6), 1193–1234 (2004)

    Article  MATH  Google Scholar 

Download references

Acknowledgments

This research is financially supported by the National Natural Science Foundation of China (No.61401401, 61402421, 61571401), the China Postdoctoral Science Foundation (No.2015T80779, 2014M561998) and the open research fund of National Mobile Communications Research Laboratory, Southeast University (No.2016D02).

Author information

Authors and Affiliations

  1. School of Information Engineering, Zhengzhou University, Zhengzhou, 450001, China

    Fasong Wang, Jiankang Zhang & Li Jiang

  2. School of Sciences, Henan University of Technology, Zhengzhou, China

    Rui Li

Authors
  1. Fasong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiankang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Li Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Fasong Wang or Jiankang Zhang .

Editor information

Editors and Affiliations

  1. Tongji University , Shanghai, China

    De-Shuang Huang

  2. University of Ulsan , Ulsan, Korea (Republic of)

    Kang-Hyun Jo

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Wang, F., Li, R., Zhang, J., Jiang, L. (2016). Blind Hyperspectral Unmixing Using Deep-Independent Information. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-42294-7_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42293-0

  • Online ISBN: 978-3-319-42294-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation