Independent component analysis for blind unmixing of hyperspectral imagery with additional constraints

IEEE Transactions on Geoscience and Remote Sensing

Volumn 49, Issue 6 PART 1, 2011, Pages 2165-2179

  Xia, Wei ^a   Liu, Xuesong ^a   Wang, Bin ^a   Zhang, Liming ^a  

^a FUDAN UNIVERSITY   (China)

Author keywords

Abundance nonnegative constraint (ANC); abundance sum to one constraint (ASC); adaptive abundance modeling (AAM); hyperspectral unmixing; independent component analysis (ICA)

Indexed keywords

ABUNDANCE NONNEGATIVE CONSTRAINT (ANC); ABUNDANCE SUM-TO-ONE CONSTRAINT (ASC); ADAPTIVE ABUNDANCE MODELING (AAM); CONSTRAINED ICA; EFFECTIVE SOLUTION; ENDMEMBERS; GROUND OBJECTS; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL DATA; HYPERSPECTRAL IMAGERY; HYPERSPECTRAL UNMIXING; ICA ALGORITHMS; INDEPENDENCE ASSUMPTION; INDEPENDENT COMPONENTS; OBJECTIVE FUNCTIONS; PRIOR KNOWLEDGE; STATISTICAL CHARACTERISTICS; STATISTICAL DISTRIBUTION; UNMIXING;

ALGORITHMS; MULTIVARIANT ANALYSIS; REMOTE SENSING;

INDEPENDENT COMPONENT ANALYSIS;

ALGORITHM; CORRELATION; EXPERIMENTAL STUDY; IMAGE ANALYSIS; SPECTRAL REFLECTANCE; SPECTRAL RESOLUTION;

EID: 79957650412     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2010.2101609     Document Type: Article

References (56)

1. D. Landgrebe, "Hyperspectral image data analysis," IEEE Signal Process. Mag., vol. 19, no. 1, pp. 17-28, Jan. 2002. (Pubitemid 34237205)
2. D. Manolakis and G. A. Shaw, "Detection algorithms for hyperspectral imaging applications," IEEE Signal Process. Mag., vol. 19, no. 1, pp. 29- 43, Jan. 2002. (Pubitemid 34237206)
3. C.-I Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification. New York: Plenum, 2003.
4. C. Small, "Estimation of urban vegetation abundance by spectral mixture analysis," Int. J. Remote Sens., vol. 22, no. 7, pp. 1305-1334, 2001. (Pubitemid 32418346)
5. T. M. Lillesand and R. W. Kiefer, Remote Sensing and Image Interpretation, 4th ed. New York: Wiley, 2000.
6. C.-I Chang, S. S. Chiang, J. A. Smith, and I.W. Ginsberg, "Linear spectral random mixture analysis for hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 2, pp. 375-392, Feb. 2002. (Pubitemid 34505767)
7. J. Boardman, "Automating spectral unmixing of AVIRIS data using convex geometry concepts," in Proc. AVIRIS Workshop, JPL Pub. 93-26, 1993, vol. 1, pp. 11-14.
8. M. E. Winter, "N-findr: An algorithm for fast autonomous spectral endmember determination in hyperspectral data," in Proc. SPIE Conf. Imaging Spectrometry V,1999, vol. 3753, pp. 266-275.
9. 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)
10. J. Nascimento and J. 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. 2002. (Pubitemid 40476033)
11. C.-I Chang, C.-C.Wu,W. Liu, and Y.-C. Ouyang, "A new growing method for simplex-based endmember extraction algorithm," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 10, pp. 2804-2819, Oct. 2006.
12. X. Tao, B. Wang, and L. Zhang, "Orthogonal bases approach for decomposition of mixed pixels for hyperspectral imagery," IEEE Geosci. Remote Sens. Lett., vol. 6, no. 2, pp. 219-223, Apr. 2009.
13. M. Zortea and A. Plaza, "Spatial preprocessing for endmember extraction," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 8, pp. 2679-2693, Apr. 2009.
14. 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. (Pubitemid 32400422)
15. D. D. Lee and H. S. Seung, "Learning the parts of objects by non-negative matrix factorization," Nature, vol. 401, no. 6755, pp. 788-791, Oct. 1999.
16. A. Hyvarinen, J. Karhunen, and E. Oja, Independent Component Analysis. New York: Wiley, 2001.
17. A. Cichocki and S. Amari, Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. Hoboken, NJ: Wiley, 2002.
18. 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)
19. 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.
20. X. Liu, W. Xia, B. Wang, and L. Zhang, "An approach based on constrained nonnegative matrix factorization to unmix hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 2, pp. 757-772, Feb. 2011.
21. J. Bayliss, J. A. Gualtieri, and R. F. Cromp, "Analyzing hyperspectral data with independent component analysis," Proc. SPIE, vol. 3240, pp. 133- 143, 1997.
22. C. Chen and X. Zhang, "Independent component analysis for remote sensing study," Proc. SPIE, vol. 3871, pp. 150-158, 1999. (Pubitemid 30534636)
23. T. M. Tu, "Unsupervised signature extraction and separation in hyperspectral images: A noise-adjusted fast independent component analysis approach," Opt. Eng., vol. 39, no. 4, pp. 897-906, Apr. 2000.
24. S.-S. Chiang, C.-I Chang, and I. W. Ginsberg, "Unsupervised hyperspectral image analysis using independent component analysis," in Proc. IGARSS, 2000, vol. 7, pp. 3136-3138.
25. M. Lennon, M. Mouchot, G. Mercier, and L. Hubert-Moy, "Spectral unmixing of hyperspectral images with the independent component analysis and wavelet packets," in Proc. IGARSS, 2001, vol. 6, pp. 2896-2898. (Pubitemid 34245332)
26. J. Nascimento and J. Bioucas-Dias, "Does independent component analysis play a role in unmixing hyperspectral data?," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 4, pp. 175-187, Apr. 2005. (Pubitemid 40162736)
27. J. Wang and C.-I Chang, "Applications of independent component analysis in endmember extraction and abundance quantification for hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 9, pp. 2601-2616, Sep. 2006. (Pubitemid 44321553)
28. A. Hyvarinen and E. Oja, "A fast fixed-point for independent component analysis," Neural Comput., vol. 9, no. 7, pp. 1483-1492, 1997. (Pubitemid 127462801)
29. S. Moussaoui, H. Hauksdottir, F. Schmidt, C. Jutten, J. Chanussot, D. Brie, S. Doute, and J. A. Benediktsson, "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.
30. J. Nascimento and J. Bioucas-Dias, "Hyperspectral unmixing algorithm via dependent component analysis," in Proc. IEEE IGARSS, Jul. 2007, pp. 4033-4036.
31. P. Comon, "Independent component analysis, a new concept?" Signal Process., vol. 36, no. 3, pp. 287-314, Apr. 1994.
32. T. Cover and J. Tomas, Eds., Elements of Information Theory. New York: Wiley, 1991.
33. S. Amari, A. Cichocki, and H. H. Yang, "A new learning algorithm for blind signal separation," in Proc. Adv. Neural Inf. Process. Syst., 1996, vol. 8, pp. 752-763.
34. H. H. Yang and S. Amari, "Adaptive online learning algorithms for blind separation: Maximum entropy and minimum mutual information," Neural Comput., vol. 9, no. 7, pp. 1457-1482, Oct. 1997. (Pubitemid 127462800)
35. A. J. Bell and T. J. Sejnowski, "An information-maximization approach to blind separation and blind deconvolution," Neural Comput., vol. 7, no. 6, pp. 1129-1159, Nov. 1995.
36. D. Obradovic and G. Deco, "Information maximization and independent component analysis: Is there a difference?," Neural Comput., vol. 10, no. 9, pp. 2085-2101, Nov. 15, 1998. (Pubitemid 128464296)
37. S. Amari, "Natural gradient works efficiently in learning," Neural Comput., vol. 10, no. 2, pp. 251-276, Feb. 15, 1998. (Pubitemid 128463152)
38. J. Cardoso, "Infomax and maximum likelihood of source separation," IEEE Signal Process. Lett., vol. 4, no. 4, pp. 112-114, Apr. 1997. (Pubitemid 127554963)
39. K. Pearson, "Contributions to the theory of mathematical evolution," Philos. Trans. R. Soc. Lond., vol. 185, pp. 71-110, 1894.
40. M. Girolami, "An alternative perspective on adaptive independent component analysis algorithms," Neural Comput., vol. 10, no. 8, pp. 2103-2114, Nov. 1998. (Pubitemid 128464297)
41. T. Lee, M. Girolami, and T. J. Sejnowski, "Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources," Neural Comput., vol. 11, no. 2, pp. 417-441, Feb. 1999.
42. I. T. Jolliffe, Principal Component Analysis. New York: Springer- Verlag, 1986.
43. M. O. Smith, P. E. Johnson, and J. B. Adams, "Quantitative determination of mineral types and abundances from reflectance spectra using principal component analysis," J. Geophys. Res., vol. 90, no. 2, pp. C797-C804, Oct. 1985.
44. L. L. Scharf, Statistical Signal Processing, Detection Estimation and Time Series Analysis. Reading, MA: Addison-Wesley, 1991.
45. 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.
46. 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, pp. 608-619, Mar. 2004.
47. J. M. Bioucas-Dias and J. M. P. Nascimento, "Hyperspectral subspace identification," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 8, pp. 2435-2445, Aug. 2008.
48. C. L. Lawson and R. J. Hanson, Solving Least Squares Problems. Englewood Cliffs, NJ: Prentice-Hall, 1974, ch. 23, p. 161.
49. N. Keshava and J. F. Mustard, "Spectral unmixing," IEEE Signal Process. Mag., vol. 19, no. 1, pp. 44-57, Mar. 2001. (Pubitemid 34237207)
50. C.-I Chang and D. Heinz, "Constrained subpixel target detection for remotely sensed imagery," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 3, pp. 1144-1159, May 2000.
51. 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.
52. H. Yang, Q. Du, H. Su, and Y. Sheng, "An efficient method for supervised hyperspectral band selection," IEEE Geosci. Remote Sens. Lett., vol. 8, no. 1, pp. 138-142, Jan. 2011.
53. J. S. Bethel, C. Lee, and D. A. Landgrebe, "Geometric registration and classification of hyperspectral airborne pushbroom data," in Proc. 19th Int. Symp. Photogramm. Remote Sens., 2000, pp. 567-571.
54. S. Cai, Q. Du, and R. Moorhead, "Hyperspectral imagery visualization using double layers," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 10, pp. 3028-3036, Oct. 2007.
55. Q. Du, N. Raksuntorn, N. Younan, and R. King, "End-member extraction for hyperspectral image analysis," Appl. Opt., vol. 47, no. 28, pp. 77-84, Oct. 2008.
56. S. Cai, Q. Du, and R. Moorhead, "Feature-driven multilayer visualization for remotely sensed hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 9, pp. 3471-3481, Sep. 2010.