Article Contents
Article Contents

# Hyperspectral unmixing by the alternating direction method of multipliers

• We have developed a method for hyperspectral image data unmixing that requires neither pure pixels nor any prior knowledge about the data. Based on the well-established Alternating Direction Method of Multipliers, the problem is formulated as a biconvex constrained optimization with the constraints enforced by Bregman splitting. The resulting algorithm estimates the spectral and spatial structure in the image through a numerically stable iterative approach that removes the need for separate endmember and spatial abundance estimation steps. The method is illustrated on data collected by the SpecTIR imaging sensor.
Mathematics Subject Classification: 65K10, 65F22, 15A23, 49K30.

 Citation:

•  [1] S. Boyd, N. Parkh, E. Chu, B. Peleato and J. Eckstein, Distributed optimization and statistical learning via the alternating direction method of multipliers, Foundations and Trends in Machine Learning, 3 (2011), 1-122.doi: 10.1561/2200000016. [2] C.-I. Cheng, Hyperspectral Data Processing: Algorithm Design and Analysis, John Wiley & Sons, Hoboken, NJ, 2013.doi: 10.1002/9781118269787. [3] M. D. Craig, Minimum-volume transforms for remotely sensed data, IEEE Trans. Geosci. Remote Sens., 32 (1994), 542-552.doi: 10.1109/36.297973. [4] T. Goldstein and S. Osher, The split Bregman method for l-1 regularized problems, SIAM J. on Imaging Sciences, 2 (2009), 323-343.doi: 10.1137/080725891. [5] G. H. Golub, S. Nash and C. van Loan, A Hessenberg-Schur method for the problem $AX + XB = C$, IEEE Trans. Automatic Control, 24 (1979), 909-913.doi: 10.1109/TAC.1979.1102170. [6] J. A. Herweg, J. P. Kerekes, O. Weatherbee, D. Messinger, J. van Aardt, E. J. Ientilucci, Z. Ninkov, J. Fauling, N. Raqueno and J. Meda, SpecTIR Hyperspectral Airborne Rochester Experiment (SHARE) Data Collection Campaign, Proc. SPIE 8390, 2012. [7] M. R. Hestenes, Multiplier and gradient methods, Journal of Optimization Theory and Applications, 4 (1969), 303-320.doi: 10.1007/BF00927673. [8] R. Lai and S. Osher, A Splitting Method for Orthogonally Constrained Problems, UCLA CAM Report 12-39, 2012. [9] L. Miao and H. Qi, Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization, IEEE Trans. Geosci. Remote Sens., 45 (2007), 765-777.doi: 10.1109/TGRS.2006.888466. [10] J. M. P. Nascimento and J. M. Bioucas Dias, Vertex component analysis: A fast algorithm to unmix hyperspectral data, IEEE Trans. Geosci. Remote Sens., 43 (2005), 898-910.doi: 10.1109/TGRS.2005.844293. [11] J. Nocedal and S. J. Wright, Numerical Optimization, Section 17.4, Springer, 1999.doi: 10.1007/b98874. [12] M. J. D. Powell, A method for nonlinear constraints in minimization problems, in Optimization (Sympos., Univ. Keele, Keele, 1968), Academic Press, London, 1969, 283-298. [13] R. T. Rockafellar, The multiplier method of Hestenes and Powell applied to convex programming, Journal of Optimization Theory and Applications, 12 (1973), 555-562.doi: 10.1007/BF00934777. [14] R. A. Schowengerdt, Remote Sensing: Models and Methods for Image Processing, 2nd Ed., Academic Press, NY, 1997. [15] M. E. Winter, N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data, in Imaging Spectrometry V, 3753 (1999), 266-275.doi: 10.1117/12.366289. [16] The earlier algorithm was developed under Purchase Order 1010 P PA379 to EO-Stat, Inc. from UCLA under NSF grant DMS-1118971. [17] Wide Area Arial Reconnaissance (WAAR) Project sponsored by the Edgewood Chemical Biological Center with data made available through the Advanced Threat Detection Program run by DTRA and NSF., Data collected by the Applied Physics Laboratory with a Telops Hyper-Cam sensor in 2009. Information supplied by Alison Carr of APL.