Minimum dispersion constrained nonnegative matrix factorization to unmix hyperspectral data

IEEE Transactions on Geoscience and Remote Sensing

Volumn 48, Issue 6, 2010, Pages 2590-2602

  Huck, Alexis ^a   Guillaume, Mireille ^a   Blanc Talon, Jacques ^b  

^a AIX MARSEILLE UNIVERSITÉ   (France)

^b Office for Advanced Research and Innovation (MRIS)   (France)

Author keywords

Blind source separation; Hyperspectral unmixing; Nonnegative matrix factorization; Projected gradient; Regularization function

Indexed keywords

APRIORI; COMPARATIVE ANALYSIS; DEGENERATED SOLUTIONS; ENDMEMBERS; HYPERSPECTRAL DATA; HYPERSPECTRAL IMAGE ANALYSIS; HYPERSPECTRAL UNMIXING; LINEAR MIXTURES; MINIMUM VARIANCE; MIXING COEFFICIENT; NONCONVEXITY; NONNEGATIVE MATRIX FACTORIZATION; PHYSICAL CONSTRAINTS; PROJECTED GRADIENT; PURE MATERIALS; REAL-WORLD; REGULARIZATION FUNCTION; SIZE ESTIMATION; SPECTRAL SINGULARITIES; SPECTRAL UNMIXING; UNMIXING;

BLIND SOURCE SEPARATION; DISPERSIONS; FACTORIZATION; IMAGE ANALYSIS; LIGHT MEASUREMENT; PIXELS; SIGNAL ANALYSIS;

MATRIX ALGEBRA;

ALGORITHM; COMPARATIVE STUDY; IMAGE ANALYSIS; NUMERICAL METHOD; PIXEL; REMOTE SENSING; SIGNAL-TO-NOISE RATIO; SPECTRAL ANALYSIS;

EID: 77952582975     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2009.2038483     Document Type: Article

References (43)

1. J. M. P. Nascimento and J. M. B. Dias, "Does independent component analysis play a role in unmixing hyperspectral data?" IEEE Trans. Geosci. Remote Sens., vol. 43, no. 1, pp. 175-187, Jan. 2005.
2. R. B. Singer and T. B. McCord, "Mars-Large scale mixing of bright and dark surface materials and implications for analysis of spectral reflectance, " in Proc. Lunar Planetary Sci. Conf., N. W. Hinners, Ed., 1979, vol. 10, pp. 1835-1848.
3. C. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification. New York: Kluwer, 2003.
4. S. Moussaoui, C. Carteret, D. Brie, and A. Djafari, "Bayesian analysis of spectral mixture data using Markov chain Monte Carlo methods, " Chemometr. Intell. Lab. Syst., vol. 81, no. 2, pp. 137-148, Apr. 2006.
5. O. Duran and M. Petrou, "Spectral unmixing with negative and superunity abundances for subpixel anomaly detection, " IEEE Geosci. Remote Sens. Lett., vol. 6, no. 1, pp. 152-156, Jan. 2009.
6. M. Zortea and A. Plaza, "Spatial preprocessing for endmember extraction, " IEEE Trans. Geosci. Remote Sens., vol. 47, no. 8, pp. 2679-2693, Aug. 2009.
7. C.-I Chang and B. Ji, "Weighted abundance-constrained linear spectral mixture analysis, " IEEE Trans. Geosci. Remote Sens., vol. 44, no. 2, pp. 378-388, Feb. 2006.
8. M. E. Winter, "Fast autonomous spectral end-member determination in hyperspectral data, " in Proc. 13th Int. Conf. Appl. Geologic Remote Sens., 1999, vol. 2, pp. 337-344.
9. J. Nascimento and J. 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)
10. A. Plaza and C.-I Chang, "An improved N-FINDR algorithm in implementation, " in Proc. SPIE-Algorithms Technol. Multispectral, Hyperspectral, Ultraspectral Imagery XI, S. S. Shen and P. E. Lewis, Eds., Jun. 2005, vol. 5806, pp. 298-306.
11. A. Filippi and R. Archibald, "Support vector machine-based endmember extraction, " IEEE Trans. Geosci. Remote Sens., vol. 47, no. 3, pp. 771-791, Mar. 2009.
12. D. C. Heinz and C. I Chang, "Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery, " IEEE Trans. Geosci. Remote Sens., vol. 39, no. 3, pp. 529-545, Mar. 2001.
13. N. Keshava and J. Mustard, "Spectral unmixing, " IEEE Signal Process. Mag., vol. 19, no. 1, pp. 44-57, Jan. 2002.
14. N. Dobigeon and J.-Y. Tourneret, "Spectral unmixing of hyperspectral images using a hierarchical Bayesian model, " in Proc. IEEE ICASSP, Apr. 15-20, 2007, vol. 3, pp. III-1209-III-1212.
15. J. Nascimento and J. Bioucas-Dias, "Hyperspectral unmixing algorithm via dependent component analysis, " in Proc. IEEE IGARSS, Jul. 2007, pp. 4033-4036.
16. 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.
17. A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis. Hoboken, NJ: Wiley, 2001.
18. J. Bayliss, J. A. Gualtieri, and R. F. Cromp, "Analyzing hyperspectral data with independent component analysis, " in Proc. SPIE, J. M. Selander, Ed., Mar. 1998, vol. 3240, pp. 133-143.
19. M. D. Plumbley, "Algorithms for nonnegative independent component analysis, " IEEE Trans. Neural Netw., vol. 14, no. 3, pp. 534-543, May 2003.
20. S. Moussaoui, D. Brie, O. Caspary, and A. Mohammad-Djafari, "A Bayesian method for positive source separation, " in Proc. IEEE ICASSP, 2004, pp. 485-488.
21. S. Moussaoui, H. Hauksdottir, F. Schmidt, C. Jutten, D. Chanussot, J. Brie, S. Douté, and J. Benediktson, "On the decomposition of mars hyperspectral data by ICA and Bayesian positive source separation, " Neurocomputing, vol. 71, no. 10-12, pp. 2194-2208, Jun. 2008.
22. V. P. Pauca, J. Piper, and R. J. Plemmons, "Nonnegative matrix factorization for spectral data analysis, " Linear Algebra Appl., vol. 416, no. 1, pp. 29-47, Jul. 2006.
23. M. W. Berry, M. Browne, A. N. Langville, P. V. Pauca, and R. J. Plemmons, "Algorithms and applications for approximate nonnegative matrix factorization, " Comput. Stat. Data Anal., vol. 52, no. 1, pp. 155-173, Sep. 2007.
24. P. O. Hoyer, "Non-negative sparse coding, " CoRR, 2002. cs. NE/0202009, informal publication. [Online]. Available: http://arxiv.org/abs/cs. NE/0202009
25. P. O. Hoyer, "Non-negative matrix factorization with sparseness constraints, " J. Mach. Learn. Res., vol. 5, pp. 1457-1469, Dec. 2004.
26. R. Zdunek and A. Cichocki, "Nonnegative matrix factorization with constrained second-order optimization, " Signal Process., vol. 87, no. 8, pp. 1904-1916, Aug. 2007.
27. A. Cichocki and R. Zdunek, "Regularized alternating least squares algorithms for non-negative matrix/tensor factorizations, " in Proc. ISNN: Adv. Neural Netw., vol. 4493, Lecture Notes in Computer Science, Nanjing, China, 2007.
28. S. Jia and Y. Qian, "Constrained nonnegative matrix factorization for hyperspectral unmixing, " IEEE Trans. Geosci. Remote Sens., vol. 47, no. 1, pp. 161-173, Jan. 2009.
29. A. Cichocki and R. Zdunek, "Multilayer nonnegative matrix factorization using projected gradient approaches, " Int. J. Neural Syst., vol. 17, no. 6, pp. 431-446, Dec. 2007.
30. A. Cichocki, R. Zdunek, and S. Amari, "Non-negative matrix and tensor factorization, " IEEE Signal Process. Mag., pp. 142-145, Jan. 2008.
31. S. Robila and L. Maciak, "Considerations on parallelizing nonnegative matrix factorization for hyperspectral data unmixing, " IEEE Geosci. Remote Sens. Lett., vol. 6, no. 1, pp. 57-61, Jan. 2009.
32. D. D. Lee and H. S. Seung, "Algorithms for non-negative matrix factorization, " in Proc. Conf. Adv. Neural Inf. Process. Syst., 2001, vol. 13, pp. 556-562.
33. C. J. Lin, "Projected gradient methods for non-negative matrix factorization, " Neural Comput., vol. 19, no. 10, pp. 2756-2779, Oct. 2007, Tech. Rep.
34. W. Wang and X. Zou, "Non-negative matrix factorization based on projected nonlinear conjugate gradient algorithm, " in Proc. ICARN, Liverpool, U. K., Sep. 25-26, 2008, pp. 5-8.
35. J. M. P. Nascimento and J. M. B. B. Dias, "Hyperspectral subspace identification, " IEEE Trans. Geosci. Remote Sens., vol. 46, no. 8, pp. 2435-2445, Aug. 2008.
36. J. Chen, X. Jia, W. Yang, and B. Matsushita, "Generalization of subpixel analysis for hyperspectral data with flexibility in spectral similarity measures, " IEEE Trans. Geosci. Remote Sens., vol. 47, no. 7, pp. 2165-2171, Jul. 2008.
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, no. 2, pp. 1083-1094, Mar. 2000.
38. J. Broadwater, "Effects of endmember dimensionality on subpixel detection performance, " in Proc. IEEE IGARSS, Jul. 2008, vol. 2, pp. II-613-II-616.
39. C.-I Chang and D. Heinz, "Constrained subpixel target detection for remotely sensed imagery, " IEEE Trans. Geosci. Remote Sens., vol. 38, no. 3, p. 1144, May 2000.
40. [Online]. Available: http://speclab.cr.usgs.gov/spectral-lib.html
41. [Online]. Available: http://aviris.jpl.nasa.gov/
42. C. I Chang and Q. Du, "Estimation of number of spectrally distinct signal sources in hyperspectral imagery, " IEEE Trans. Geosci. Remote Sens., vol. 42, no. 3, p. 608, Mar. 2004.
43. [Online]. Available: http://speclab.cr.usgs.gov/PAPERS/cuprite.gr.truth. 1992/swayze.1992.html