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Pattern Recognition - Applications and Methods pp 193–204Cite as

Hyperspectral Imagery Framework for Unmixing and Dimensionality Estimation

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 204))

Abstract

In hyperspectral imagery a pixel typically consists mixture of spectral signatures of reference substances, also called endmembers. Linear spectral mixture analysis, or linear unmixing, aims at estimating the number of endmembers, their spectral signatures, and their abundance fractions.

This paper proposes a framework for hyperpsectral unmixing. A blind method (SISAL) is used for the estimation of the unknown endmember signature and their abundance fractions. This method solve a non-convex problem by a sequence of augmented Lagrangian optimizations, where the positivity constraints, forcing the spectral vectors to belong to the convex hull of the endmember signatures, are replaced by soft constraints. The proposed framework simultaneously estimates the number of endmembers present in the hyperspectral image by an algorithm based on the minimum description length (MDL) principle. Experimental results on both synthetic and real hyperspectral data demonstrate the effectiveness of the proposed algorithm.

This work was supported by the Instituto de Telecomunicações and by the Fundação para a Ciência e Tecnologia under project HoHus (PEst-OE/EEI/LA0008/2011).

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References

  1. Arngren, M., Schmidt, M.N., larsen, J.: Bayesian Nonnegative Matrix Factorization with Volume Prior for Unmixing of Hyperspectral Images. In: IEEE Workshop on Machine Learning for Signal Processing (MLSP) (September 2009)

    Google Scholar 

  2. Bajorski, P.: Second Moment Linear Dimensionality as an Alternative to Virtual Dimensionality. IEEE Trans. Geosci. Remote Sensing 49(2), 672–678 (2011)

    Article  Google Scholar 

  3. Berman, M., Kiiveri, H., Lagerstrom, R., Ernst, A., Dunne, R., Huntington, J.F.: ICE: A Statistical Approach to Identifying Endmembers in Hyperspectral Images. IEEE Trans. Geosci. Remote Sensing 42(10), 2085–2095 (2004)

    Article  Google Scholar 

  4. Bioucas-Dias, J.M.: A Variable Splitting Augmented Lagrangian Approach to Linear Spectral Unmixing. In: First IEEE GRSS Workshop on Hyperspectral Image and Signal Processing-WHISPERS 2009 (2009)

    Google Scholar 

  5. Bioucas-Dias, J.M., Nascimento, J.M.P.: Hyperspectral Subspace Identification. IEEE Trans. Geosci. Remote Sensing 46(8), 2435–2445 (2008)

    Article  Google Scholar 

  6. Bioucas-Dias, J.M., Plaza, A.: Hyperspectral unmixing: geometrical, statistical, and sparse regression-based approaches, vol. 7830. SPIE (2010)

    Google Scholar 

  7. Boardman, J.: Automating Spectral Unmixing of AVIRIS Data using Convex Geometry Concepts. In: Summaries of the Fourth Annual JPL Airborne Geoscience Workshop, JPL Pub. 93-26, AVIRIS Workshop, vol. 1, pp. 11–14 (1993)

    Google Scholar 

  8. Broadwater, J., Meth, R., Chellappa, R.: Dimensionality Estimation in Hyper-spectral Imagery Using Minimum Description Length. In: Proceedings of the Army Science Conference, Orlando, FL (November 2004)

    Google Scholar 

  9. Chan, T.H., Ma, W.K., Ambikapathi, A., Chi, C.Y.: A simplex volume maximization framework for hyperspectral endmember extraction. IEEE Trans. Geosci. Remote Sensing 49(1), 1–17 (2011)

    Article  Google Scholar 

  10. Chan, T.H., Chi, C.Y., Huang, Y.M., Ma, W.K.: A Convex Analysis-Based Minimum-Volume Enclosing Simplex Algorithm for Hyperspectral Unmixing. IEEE Trans. Signal Processing 57(11), 4418–4432 (2009)

    Article  MathSciNet  Google Scholar 

  11. Chang, C.I., Du, Q.: Estimation of Number of Spectrally Distinct Signal Sources in Hyperspectral Imagery. IEEE Trans. Geosci. Remote Sensing 42(3), 608–619 (2004)

    Article  Google Scholar 

  12. Craig, M.D.: Minimum-volume Transforms for Remotely Sensed Data. IEEE Trans. Geosci. Remote Sensing 32, 99–109 (1994)

    Article  Google Scholar 

  13. Diani, N.A.M., Corsini, G.: Hyperspectral Signal Subspace Identification in the Presence of Rare Signal Components. IEEE Trans. Geosci. Remote Sensing 48(4), 1940–1954 (2010)

    Article  Google Scholar 

  14. Dobigeon, N., Moussaoui, S., Coulon, M., Tourneret, J.Y., Hero, A.O.: Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery. IEEE Trans. Signal Processing 57(11), 4355–4368 (2009)

    Article  MathSciNet  Google Scholar 

  15. Figueiredo, M.A.T., Jain, A.K.: Unsupervised Learning of Finite Mixture Models. IEEE Trans. Pattern Anal. Machine Intell. 44(3), 381–396 (2002)

    Article  Google Scholar 

  16. Heinz, D., Chang, C.-I.: Fully Constrained Least Squares Linear Spectral Mixture Analysis Method for Material Quantification in Hyperspectral Imagery. IEEE Transactions on Geoscience and Remote Sensing 39(3), 529–545 (2001)

    Article  Google Scholar 

  17. Keshava, N., Mustard, J.: Spectral Unmixing. IEEE Signal Processing Mag. 19(1), 44–57 (2002)

    Article  Google Scholar 

  18. Moussaoui, S., Hauksdóttir, H., Schmidt, F., Jutten, C., Chanussot, J., Brie, D., Douté, S., Benediktsson, J.A.: On the Decomposition of Mars Hyperspectral Data by ICA and Bayesian Positive Source Separation. Neurocomputing 71(10-12), 2194–2208 (2008)

    Article  Google Scholar 

  19. Nascimento, J.M.P., Bioucas-Dias, J.M.: Does Independent Component Analysis Play a Role in Unmixing Hyperspectral Data? IEEE Trans. Geosci. Remote Sensing 43(1), 175–187 (2005)

    Article  Google Scholar 

  20. Nascimento, J.M.P., Bioucas-Dias, J.M.: Vertex Component Analysis: A Fast Algorithm to Unmix Hyperspectral Data. IEEE Trans. Geosci. Remote Sensing 43(4), 898–910 (2005)

    Article  Google Scholar 

  21. Nascimento, J.M.P., Bioucas-Dias, J.M.: Hyperspectral unmixing based on mixtures of dirichlet components. IEEE Transactions on Geoscience and Remote Sensing 50(3), 863–878 (2012)

    Article  Google Scholar 

  22. Plaza, A., Martinez, P., Perez, R., Plaza, J.: Spatial/Spectral Endmember Extraction by Multidimensional Morphological Operations. IEEE Trans. Geosci. Remote Sensing 40(9), 2025–2041 (2002)

    Article  Google Scholar 

  23. Rissanen, J.: Modeling by Shortest Data Description. Automatica 14, 465–471 (1978)

    Article  MATH  Google Scholar 

  24. Settle, J.J.: On the Relationship Between Spectral Unmixing and Subspace Projection. IEEE Trans. Geosci. Remote Sensing 34, 1045–1046 (1996)

    Article  Google Scholar 

  25. Swayze, G., Clark, R., Sutley, S., Gallagher, A.: Ground-Truthing AVIRIS Mineral Mapping at Cuprite, Nevada. Summaries of the Third Annual JPL Airborne Geosciences Workshop, pp. 47–49 (1992)

    Google Scholar 

  26. Winter, M.E.: N-FINDR: An Algorithm for Fast Autonomous Spectral End-member Determination in Hyperspectral Data. In: Proc. of the SPIE Conference on Imaging Spectrometry, vol. 3753, pp. 266–275 (1999)

    Google Scholar 

  27. Zare, A., Gader, P.: Sparsity Promoting Iterated Constrained Endmember Detection in Hyperspectral Imagery. IEEE Geosci. Remote Sensing Let. 4(3), 446–450 (2007)

    Article  Google Scholar 

  28. Zymnis, A., Kim, S.J., Skaf, J., Parente, M., Boyd, S.: Hyperspectral Image Unmixing via Alternating Projected Subgradients. In: 41st Asilomar Conferece on Signals, Systems, and Computer, pp. 4–7 (2007)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Instituto de Telecomunicações and Instituto Superior de Engenharia de Lisboa, R. Conselheiro Emídio Navarro, N. 1, 1959-007, Lisbon, Portugal

    José M. P. Nascimento

  2. Instituto de Telecomunicações and Instituto Superior Técnico, Technical University of Lisbon, Av. Rovisco Pais, Torre Norte, Piso 10, 1049-001, Lisbon, Portugal

    José M. Bioucas-Dias

Authors
  1. José M. P. Nascimento
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  2. José M. Bioucas-Dias
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Editor information

Editors and Affiliations

  1. Department of Computer Languages, and Systems, Jaume I University, Vicent Sos Baynat Avenue, Castellon de la Plana, 12071, Spain

    Pedro Latorre Carmona

  2. Department of Programming Languages, and Information Systems, Jaume I University, Campus del Riu Sec s/n, Castellon de la Plana, 12071, Spain

    J. Salvador Sánchez

  3. IST - Technical University of Lisbon, Av. Rovisco Pais 1, Lisbon, 1049-001, Portugal

    Ana L.N. Fred

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Nascimento, J.M.P., Bioucas-Dias, J.M. (2013). Hyperspectral Imagery Framework for Unmixing and Dimensionality Estimation. In: Latorre Carmona, P., Sánchez, J., Fred, A. (eds) Pattern Recognition - Applications and Methods. Advances in Intelligent Systems and Computing, vol 204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36530-0_16

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  • DOI: https://doi.org/10.1007/978-3-642-36530-0_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36529-4

  • Online ISBN: 978-3-642-36530-0

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