Hyperspectral subspace identification

IEEE Transactions on Geoscience and Remote Sensing

Volumn 46, Issue 8, 2008, Pages 2435-2445

  Bioucas Dias, José M ^a   Nascimento, José M P ^a,b  

^a INSTITUTO DE TELECOMUNICAÇÕES   (Portugal)

^b INSTITUTO SUPERIOR DE ENGENHARIA DE LISBOA   (Portugal)

Author keywords

Dimensionality reduction; Hyperspectral imagery; Hyperspectral signal subspace identification by minimum error (HySime); Hyperspectral unmixing; Linear mixture; Minimum mean square error (mse); Subspace identification

Indexed keywords

CHLORINE COMPOUNDS; DATA STORAGE EQUIPMENT; ERROR ANALYSIS; SIGNAL ANALYSIS; TARGET TRACKING;

ALGORITHM PERFORMANCE; CHANGE-DETECTION; DATA STORAGE; DIMENSIONALITY REDUCTION; EIGEN-DECOMPOSITION; EIGENVALUES; HYPER-SPECTRAL; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL IMAGERY; HYPERSPECTRAL PROCESSING; HYPERSPECTRAL SIGNAL SUBSPACE IDENTIFICATION BY MINIMUM ERROR (HYSIME); HYPERSPECTRAL UNMIXING; LEAST-SQUARED ERRORS; LINEAR MIXTURE; MINIMUM ERROR; MINIMUM MEAN SQUARE ERROR; MINIMUM MEAN SQUARE ERROR (MSE); NOISE CORRELATIONS; SIGNAL IDENTIFICATION; SIGNAL SUB-SPACE; STATE-OF-THE-ART PERFORMANCE; SUBSPACE IDENTIFICATION; TARGET DETECTIONS; TUNING PARAMETERS; UN-MIXING;

RADAR TARGET RECOGNITION;

EID: 48849088937     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2008.918089     Document Type: Article

References (57)

1. J. M. Bioucas-Dias and J. M. P. Nascimento, "Estimation of signal subspace on hyperspectral data," in Proc. SPIE - Conf. Image and Signal Processing for Remote Sensing XI, L. Bruzzone, Ed., 2005, vol. 5982, pp. 191-198.
2. J. M. P. Nascimento and J. M. Bioucas-Dias, "Signal subspace identification in hyperspectral linear mixtures," in Pattern Recognition and Image Analysis, vol. 3523, J. S. Marques, N. P. de la Blanca, and P. Pina, Eds. New York: Springer-Verlag, 2005, pp. 207-214.
3. T. M. Lillesand, R. W. Kiefer, and J. W. Chipman, Remote Sensing and Image Interpretation, 5th ed. Hoboken, NJ: Wiley, 2004.
4. J. P. Kerekes and J. E. Baum, "Spectral imaging system analytical model for subpixel object detection," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 5, pp. 1088-1101, May 2002.
5. N. Keshava and J. Mustard, "Spectral unmixing," IEEE Signal Process. Mag., vol. 19, no. 1, pp. 44-57, Jan. 2002.
6. M. E. Winter, "Fast autonomous spectral endmember determination in hyperspectral data," in Proc. 13th Int. Conf. Appl. Geologic Remote Sens., 1999, vol. II, pp. 337-344.
7. F. V. D. Meer, "Iterative spectral unmixing," Int. J. Remote Sens., vol. 20, no. 17, pp. 3431-3436, Nov. 1999.
8. 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 - Conf. Infrared Spaceborne Remote Sensing III, 1995, vol. 2553, pp. 148-157.
9. J. W. Boardman, "Analysis, understanding, and visualization of hyperspectral data as convex sets in n-space," in Proc. SPIE - Conf. Imaging Spectrometry, M. R. Descour, J. M. Mooney, D. L. Perry, and L. R. Illing, Eds., 1995, vol. 2480, pp. 14-22.
10. M. O. Smith, J. B. Adams, and D. E. Sabol, Spectral Mixture Analysis - New Strategies For the Analysis of Multispectral Data. Brussels, Belgium: Kluwer, 1994, pp. 125-143.
11. J. B. Adams, M. Smith, and A. R. Gillespie, Imaging Spectroscopy: Interpretation Based on Spectral Mixture Analysis. New York: Cambridge Univ. Press, 1993, pp. 145-166.
12. J. Boardman, "Automating spectral unmixing of AVIRIS data using convex geometry concepts," in Proc. Summaries 4th Annu. JPL Airborne Geosci. Workshop, AVIRIS Workshop, 1993, vol. 1, pp. 11-14. JPL Pub. 93-26.
13. J. B. Adams and M. O. Smith, "Spectral mixing model: 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.
14. D. Landgrebe, "Hyperspectral image data analysis," IEEE Signal Process. Mag., vol. 19, no. 1, pp. 17-28, Jan. 2002.
15. C. Chang and S. Wang, "Constrained band selection for hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 6, pp. 1575-1585, Jun. 2006.
16. S. D. Backer, P. Kempeneers, W. Debruyn, and P. Scheunders, "A band selection technique for spectral classification," IEEE Geosci. Remote Sens. Lett., vol. 2, no. 3, pp. 319-323, Jul. 2005.
17. R. Huang and M. He, "Band selection based on feature weighting for classification of hyperspectral data," IEEE Geosci. Remote Sens. Lett., vol. 2, no. 2, pp. 156-159, Apr. 2005.
18. S. S. Shen and E. M. Bassett, "Information-theory-based band selection and utility evaluation for reflective spectral systems," in Proc. SPIE - Conf. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII, 2002, vol. 4725, pp. 18-29.
19. C. Chang, Q. Du, T. Sun, and M. Althouse, "A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 7, no. 6, pp. 2631-2641, Nov. 1999.
20. L. Sun and W. Gao, "Method of selecting the best classification bands from hyperspectral images based on genetic algorithm and rough set," in Proc. SPIE - Conf. Hyperspectral Remote Sensing and Application, R. O. Green and Q. Tong, Eds., 1998, vol. 3502, pp. 179-184.
21. I. T. Jolliffe, Principal Component Analysis. New York: Springer-Verlag, 1986.
22. L. L. Scharf, Statistical Signal Processing, Detection Estimation and Time Series Analysis. Reading, MA: Addison-Wesley, 1991.
23. A. Green, M. Berman, P. Switzer, and M. D. Craig, "A transformation for ordering multispectral data in terms of image quality with implications for noise removal," IEEE Trans. Geosci. Remote Sens., vol. 26, no. 1, pp. 65-74, Jan. 1988.
24. J. B. Lee, S. Woodyatt, and M. Berman, "Enhancement of high spectral resolution remote-sensing data by a noise-adjusted principal components transform," IEEE Trans. Geosci. Remote Sens., vol. 28, no. 3, pp. 295-304, May 1990.
25. J. Bruske and G. Sommer, "Intrinsic dimensionality estimation with optimally topology preserving maps," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 5, pp. 572-575, May 1998.
26. P. Demartines and J. Hérault, "Curvilinear component analysis: A self-organizing neural network for nonlinear mapping of data sets," IEEE Trans. Neural Netw., vol. 8, no. 1, pp. 148-154, Jan. 1997.
27. M. Lennon, G. Mercier, M. Mouchot, and L. Hubert-Moy, "Curvilinear component analysis for nonlinear dimensionality reduction of hyperspectral images," in Proc. SPIE - Symp. Remote Sensing Conf. Image and Signal Processing for Remote Sensing VII, 2001, vol. 4541, pp. 157-169.
28. C. Bachmann, T. Ainsworth, and R. Fusina, "Improved manifold coordinate representations of large-scale hyperspectral scenes," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 10, pp. 2786-2803, Oct. 2006.
29. C. Bachmann, T. Ainsworth, and R. Fusina, "Exploiting manifold geometry in hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 441-454, Mar. 2005.
30. C. Yangchi, M. Crawford, and J. Ghosh, "Applying nonlinear manifold learning to hyperspectral data for land cover classification," in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2005, vol. 6, pp. 4311-4314.
31. D. Gillis, J. Bowles, G. M. Lamela, W. J. Rhea, C. M. Bachmann, M. Montes, and T. Ainsworth, "Manifold learning techniques for the analysis of hyperspectral ocean data," in Proc. SPIE - Conf. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, S. S. Shen and P. E. Lewis, Eds., 2005, vol. 5806, pp. 342-351.
32. J. Wang and C.-I Chang, "Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 6, pp. 1586-1600, Jun. 2006.
33. M. Lennon, M. Mouchot, G. Mercier, and L. Hubert-Moy, "Independent component analysis as a tool for the dimensionality reduction and the representation of hyperspectral images," in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2001, pp. 2893-2895.
34. A. Ifarraguerri and C.-I Chang, "Unsupervised hyperspectral image analysis with projection pursuit," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 6, pp. 127-143, Nov. 2000.
35. C. Bachmann and T. Donato, "An information theoretic comparison of projection pursuit and principal component features for classification of Landsat TM imagery of central Colorado," Int. J. Remote Sens., vol. 21, no. 15, pp. 2927-2935, Oct. 2000.
36. H. Othman and S.-E. Qian, "Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 2, pp. 397-408, Feb. 2006.
37. S. Kaewpijit, J. L. Moigne, and T. El-Ghazawi, "Automatic reduction of hyperspectral imagery using wavelet spectral analysis," IEEE Trans. Geosci. Remote Sens., vol. 41, no. 4, pp. 863-871, Apr. 2003.
38. J. H. Bowles, J. A. Antoniades, M. M. Baumback, J. M. Grossmann, D. Haas, P. J. Palmadesso, and J. Stracka, "Real-time analysis of hyperspectral data sets using NRL's ORASIS algorithm," in Proc. SPIE - Conf. Imaging Spectrometry III, 1997, vol. 3118, pp. 38-45.
39. N. Keshava, "A survey of spectral unmixing algorithms," Lincoln Lab. J., vol. 14, no. 1, pp. 55-78, 2003.
40. G. Schwarz, "Estimating the dimension of a model," Ann. Stat., vol. 6, no. 2, pp. 461-464, Mar. 1978.
41. J. Rissanen, "Modeling by shortest data description," Automatica, vol. 14, pp. 465-471, 1978.
42. H. Akaike, "A new look at the statistical model identification, " IEEE Trans. Autom. Control, vol. AC-19, no. 6, pp. 716-723, Dec. 1974.
43. 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.
44. M. Wax and T. Kailath, "Detection of signals by information theoretic criteria," IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-33, no. 2, pp. 387-392, Apr. 1985.
45. J. Harsanyi, W. Farrand, and C.-I Chang, "Determining the number and identity of spectral endmembers: An integrated approach using Neyman-Pearson eigenthresholding and iterative constrained rms error minimization," in Proc. 9th Thematic Conf. Geologic Remote Sens., 1993.
46. D. Manolakis, C. Siracusa, and G. Shaw, "Hyperspectral subpixel target detection using linear mixing model," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 7, pp. 1392-1409, Jul. 2001.
47. G. Shaw and D. Manolakis, "Signal processing for hyperspectral image exploitation," IEEE Signal Process. Mag., vol. 19, no. 1, pp. 12-16, Jan. 2002.
48. R. N. Clark, G. A. Swayze, A. Gallagher, T. V. King, and W. M. Calvin, "The U.S. Geological Survey digital spectral library: Version 1: 0.2 to 3.0 μm," U.S. Geol. Survey, 1993. Open File Report 93-592.
49. A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin, Bayesian Data Analysis, 2nd ed. London, U.K.: Chapman & Hall, 2004.
50. T. Minka, "Estimating a Dirichlet distribution," MIT, Cambridge, MA, 2000. Tech. Rep.
51. G. H. Golub and C. F. V. Loan, Matrix Computations, 3rd ed., ser. Mathematical Sciences. Baltimore, MD: Johns Hopkins Univ. Press, 1996.
52. G. W. Stewart and J.-G. Sun, Matrix Perturbation Theory. New York: Academic, 1990.
53. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. Hoboken, NJ: Wiley, 2001.
54. R. Roger and J. Arnold, "Reliably estimating the noise in AVIRIS hyperspectral imagers," Int. J. Remote Sens., vol. 17, no. 10, pp. 1951-1962, 1996.
55. G. Vane, R. Green, T. Chrien, H. Enmark, E. Hansen, and W. Porter, "The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)," Remote Sens. Environ., vol. 44, no. 2/3, pp. 127-143, May/Jun. 1993.
56. P.-F. Hsieh and D. Landgrebe, "Classification of high dimensional data," Ph.D. dissertation, School Electr. Comput. Eng., Purdue Univ., West Lafayette, IN, 1998. Tech. Rep. TR-ECE 98-4.
57. D. Landgrebe, "Multispectral data analysis: A signal theory perspective," Purdue Univ., West Lafayette, IN, 1998. Tech. Rep.