Sparse unmixing of hyperspectral data

IEEE Transactions on Geoscience and Remote Sensing

Volumn 49, Issue 6 PART 1, 2011, Pages 2014-2039

  Iordache, Marian Daniel ^a   Bioucas Dias, José M ^b   Plaza, Antonio ^a  

^a UNIVERSITY OF EXTREMADURA   (Spain)

^b INSTITUTO SUPERIOR TÉCNICO   (Portugal)

Author keywords

Abundance estimation; convex optimization; hyperspectral imaging; sparse regression (SR); spectral unmixing

Indexed keywords

CONVEX OPTIMIZATION; FIGHTER AIRCRAFT; LIBRARIES; NASA; PIXELS; SPECTROMETERS; SPECTROSCOPY; THERMOGRAPHY (IMAGING);

ABUNDANCE ESTIMATION; AIRBORNE VISIBLE INFRARED IMAGING SPECTROMETER; ENDMEMBER EXTRACTION ALGORITHMS; LINEAR SPARSE REGRESSION; LINEAR SPECTRAL UNMIXING; SPARSE REGRESSION; SPECTRAL UNMIXING; U.S. GEOLOGICAL SURVEYS;

HYPERSPECTRAL IMAGING;

ALGORITHM; AVIRIS; DATA INTERPRETATION; DATA SET; EXPERIMENTAL STUDY; GEOLOGICAL SURVEY; IMAGE ANALYSIS; IMAGING METHOD; OPTIMIZATION; PIXEL; REGRESSION ANALYSIS; REMOTE SENSING; SATELLITE DATA; SPATIAL RESOLUTION; THEORETICAL STUDY;

EID: 79957667304     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2010.2098413     Document Type: Article

References (46)

1. A. F. H. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, "Imaging spectrometry for Earth remote sensing," Science, vol. 228, no. 4704, pp. 1147- 1153, Jun. 1985.
2. R. O. Green, M. L. Eastwood, C. M. Sarture, T. G. Chrien, M. Aronsson, B. J. Chippendale, J. A. Faust, B. E. Pavri, C. J. Chovit, M. Solis, M. R. Olah, and O. Williams, "Imaging spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)," Remote Sens. Environ., vol. 65, no. 3, pp. 227-248, Sep. 1998. (Pubitemid 29360700)
3. D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing. New York: Wiley, 2003.
4. C.-I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification. New York: Kluwer, 2003.
5. N. Keshava and J. F. Mustard, "Spectral unmixing," IEEE Signal Process. Mag., vol. 19, no. 1, pp. 44-57, Jan. 2002. (Pubitemid 34237207)
6. J. B. Adams,M. O. Smith, and P. E. Johnson, "Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 site," J. Geophys. Res., vol. 91, no. B8, pp. 8098-8112, 1986.
7. D. C. Heinz and C.-I. Chang, "Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 3, pp. 529-545, Mar. 2001. (Pubitemid 32400422)
8. J. M. P. Nascimento and J. M. Bioucas-Dias, "Does independent component analysis play a role in unmixing hyperspectral data?" IEEE Trans. Geosci. Remote Sens., vol. 43, no. 11, pp. 75-187, Jan. 2005.
9. A. Plaza, P. Martinez, R. Perez, and J. Plaza, "A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 3, pp. 650-663, Mar. 2004.
10. A. Plaza and C.-I. Chang, "Impact of initialization on design of endmember extraction algorithms," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 11, pp. 3397-3407, Nov. 2006. (Pubitemid 44711681)
11. J.W. Boardman, F. A. Kruse, and R. O. Green, "Mapping target signatures via partial unmixing of AVIRIS data," in Proc. JPL Airborne Earth Sci. Workshop, 1995, pp. 23-26.
12. M. E. Winter, "N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data," in Proc. SPIE Imaging Spectrometry V,2003, vol. 3753, pp. 266-275.
13. H. Ren and C.-I. Chang, "Automatic spectral target recognition in hyperspectral imagery," IEEE Trans. Aerosp. Electron. Syst., vol. 39, no. 4, pp. 1232-1249, Oct. 2003.
14. J. M. P. Nascimento and J. M. Bioucas-Dias, "Vertex component analysis: A fast algorithm to unmix hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 4, pp. 898-910, Apr. 2005. (Pubitemid 40476033)
15. J. H. Bowles, P. J. Palmadesso, J. A. Antoniades, M. M. Baumback, and L. J. Rickard, "Use of filter vectors in hyperspectral data analysis," in Proc. SPIE Infrared Spaceborne Remote Sens. III, 1995, vol. 2553, pp. 148-157.
16. A. Ifarraguerri and C.-I. Chang, "Multispectral and hyperspectral image analysis with convex cones," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 2, pp. 756-770, Mar. 1999.
17. R. A. Neville, K. Staenz, T. Szeredi, J. Lefebvre, and P. Hauff, "Automatic endmember extraction from hyperspectral data for mineral exploration," in Proc. 21st Can. Symp. Remote Sens., 1999, pp. 21-24.
18. A. Plaza, P. Martinez, R. Perez, and J. Plaza, "Spatial/spectral endmember extraction by multidimensional morphological operations," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 9, pp. 2025-2041, Sep. 2002. (Pubitemid 35458399)
19. M. Berman, H. Kiiveri, R. Lagerstrom, A. Ernst, R. Dunne, and J. F. Huntington, "ICE: A statistical approach to identifying endmembers in hyperspectral images," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 10, pp. 2085-2095, Oct. 2004.
20. L. Miao and H. Qi, "Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 3, pp. 765-777, Mar. 2007. (Pubitemid 46375748)
21. D. M. Rogge, B. Rivard, J. Zhang, A. Sanchez, J. Harris, and J. Feng, "Integration of spatial-spectral information for the improved extraction of endmembers," Remote Sens. Environ., vol. 110, no. 3, pp. 287-303, Oct. 2007. (Pubitemid 47285207)
22. A. Zare and P. Gader, "Sparsity promoting iterated constrained endmember detection for hyperspectral imagery," IEEE Geosci. Remote Sens. Lett., vol. 4, no. 3, pp. 446-450, Jul. 2007. (Pubitemid 47117457)
23. J. Li and J. M. Bioucas-Dias, "Minimum volume simplex analysis: A fast algorithm to unmix hyperspectral data," in Proc. IEEE IGARSS, Jul. 2008, vol. 3, pp. III-250-III-253.
24. J. M. Bioucas-Dias, "A variable splitting augmented Lagrangian approach to linear spectral unmixing," in Proc. 1st WHISPERS, Aug. 2009, pp. 1-4.
25. A. Bruckstein, M. Elad, and M. Zibulevsky, "On the uniqueness of nonnegative sparse solutions to underdetermined systems of equations," IEEE Trans. Inf. Theory, vol. 54, no. 11, pp. 4813-4820, Nov. 2008.
26. A. M. Bruckstein, D. L. Donoho, and M. Elad, "From sparse solutions of systems of equations to sparse modeling of signals and images," SIAM Rev., vol. 51, no. 1, pp. 34-81, Feb. 2009.
27. E. J. Candes and T. Tao, "Decoding by linear programming," IEEE Trans. Inf. Theory, vol. 51, no. 12, pp. 4203-4215, Dec. 2005. (Pubitemid 41800353)
28. E. Candès, J. Romberg, and T. Tao, "Stable signal recovery from incomplete and inaccurate measurements," Commun. Pure Appl. Math., vol. 59, no. 8, pp. 1207-1223, Aug. 2006. (Pubitemid 43988295)
29. J. Bioucas-Dias and M. Figueiredo, Alternating Direction Algorithms for Constrained Sparse Regression: Application to Hyperspectral Unmixing, 2010, Arxiv preprint arXiv:1002.4527.
30. D. L. Donoho and M. Elad, "Optimal sparse representation in general (non-orthogonal) dictionaries via l1 minimization," Proc. Nat. Acad. Sci., vol. 100, no. 5, pp. 2197-2202, Mar. 2003. (Pubitemid 36297476)
31. I. Gorodnitsky and B. Rao, "Sparse signal reconstruction from limited data using FOCUSS: A re-weighted minimum norm algorithm," IEEE Trans. Signal Process., vol. 45, no. 3, pp. 600-616, Mar. 1997. (Pubitemid 127765947)
32. B. Natarajan, "Sparse approximate solutions to linear systems," SIAM J. Comput., vol. 24, no. 2, pp. 227-234, Apr. 1995.
33. Y. C. Pati, R. Rezaiifar, and P. Krishnaprasad, "Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition," in Proc. 27th Annu. Asilomar Conf. Signals, Syst., Comput., Los Alamitos, CA, 1993, pp. 40-44.
34. S. Chen, D. Donoho, and M. Saunders, "Atomic decomposition by basis pursuit," SIAM Rev., vol. 43, no. 1, pp. 129-159, 2001. (Pubitemid 32406896)
35. S. Foucart and M. Lai, "Sparsest solutions of underdetermined linear systems via lq-minimization for 0," Appl. Comput. Harmon. Anal., vol. 26, no. 3, pp. 395-407, May 2009.
36. J. Tropp and A. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Trans. Inf. Theory, vol. 53, no. 12, pp. 4655-4666, Dec. 2007.
37. C. Bateson, G. Asner, and C. Wessman, "Endmember bundles: A new approach to incorporating endmember variability into spectral mixture analysis," IEEE Trans. Geosci. Remote Sens., vol. 38, pt. 2, no. 2, pp. 1083-1094, Mar. 2000. (Pubitemid 30594771)
38. D. Donoho, M. Elad, and V. Temlyakov, "Stable recovery of sparse overcomplete representations in the presence of noise," IEEE Trans. Inf. Theory, vol. 52, no. 1, pp. 6-18, Jan. 2006. (Pubitemid 43263116)
39. J. Tropp, "Just relax: Convex programming methods for subset selection and sparse approximation," Univ. Texas, Austin, TX, ICES Rep. 04-04, 2004.
40. J. Eckstein and D. Bertsekas, "On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators," Math. Program., vol. 55, no. 3, pp. 293-318, Jun. 1992.
41. M. Figueiredo, J. M. Bioucas-Dias, and M. Afonso, "Fast frame-based image deconvolution using variable splitting and constrained optimization," Proc. 15th IEEE/SP Workshop SSP, pp. 109-112, 2009.
42. M. Afonso, J. M. Bioucas-Dias, and M. Figueiredo, "A fast algorithm for the constrained formulation of compressive image reconstruction and other linear inverse problems," in Proc. IEEE ICASSP, Dallas, TX, Mar. 2010, pp. 4034-4037.
43. Z. Guo, T. Wittman, and S. Osher, "L1 unmixing and its application to hyperspectral image enhancement," in Proc. SPIE Conf. Algorithms Technol. Multispectral, Hyperspectral, Ultraspectral Imaging XV, Orlando, FL, 2009, p. 733 41M.
44. D. M. Rogge, B. Rivard, J. Zhang, and J. Feng, "Iterative spectral unmixing for optimizing per-pixel endmember sets," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 12, pp. 3725-3736, Dec. 2006.
45. J. M. P. Nascimento and J. M. Bioucas-Dias, "Hyperspectral subspace identification," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 8, pp. 2435-2445, Aug. 2008.
46. R. N. Clark, G. A. Swayze, K. E. Livo, R. F. Kokaly, S. J. Sutley, J. B. Dalton, R. R. McDougal, and C. A. Gent, "Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems," J. Geophys. Res., vol. 108, no. E12, p. 5131, 2003.