Spatial purity based endmember extraction for spectral mixture analysis

IEEE Transactions on Geoscience and Remote Sensing

Volumn 48, Issue 9, 2010, Pages 3434-3445

  Mei, Shaohui ^a   He, Mingyi ^a   Wang, Zhiyong ^b   Feng, Dagan ^b  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

^b UNIVERSITY OF SYDNEY   (Australia)

Author keywords

Endmember extraction (EE); Endmember identification; Hyperspectral remote sensing; Spatial purity (SP) index; Spectral mixture analysis (SMA); Spectral spatial

Indexed keywords

ENDMEMBER EXTRACTION; ENDMEMBERS; HYPERSPECTRAL REMOTE SENSING; SPATIAL PURITY (SP) INDEX; SPECTRAL MIXTURE ANALYSIS; SPECTRAL-SPATIAL;

ALGORITHMS; COMPUTATIONAL EFFICIENCY; IMAGE RECONSTRUCTION; MIXTURES; REMOTE SENSING;

PIXELS;

ALGORITHM; COMPUTER SIMULATION; IMAGE ANALYSIS; MEASUREMENT METHOD; PERFORMANCE ASSESSMENT; PIXEL; QUANTITATIVE ANALYSIS; REMOTE SENSING; SPATIAL ANALYSIS; SPECTRAL ANALYSIS;

EID: 77956057087     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2010.2046671     Document Type: Article

References (55)

1. A. Plaza, P. Martínez, R. Perez, and J. Plaza, "A comparative analysis of endmember extraction algorithms using AVIRIS hyperspectral imagery," in Proc. Summaries 11th JPL Airborne Earth Sci. Workshop, 2002.
2. J. Plaza, A. Plaza, P. Martínez, and R. Perez, "H-COMP: A tool for quantitative and comparative analysis of endmember identification algorithms," in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2003, vol.1, pp. 291-293.
3. R. Clark, G. Swayze, K. Livo, R. Kokaly, S. Sutley, J. Dalton, R. Mc-Dougal, and C. Gent, "Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems," J. Geophys. Res., vol. 108, no. E12, p. 5131, Dec. 2003.
4. D. 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.
5. J. Boardman, F. Kruse, and R. Green, "Mapping target signatures via partial unmixing of AVIRIS data," in Proc. Summaries JPL Airborne Earth Sci. Workshop, 1995, pp. 23-26.
6. M. Winter, "N-FINDR: An algorithm for fast autonomous spectral endmember determination in hyperspectral data," in Proc. SPIE Image Spectrometry V, 1999, vol.3753, pp. 266-277.
7. K. Staenz, T. Szeredi, and J. Schwarz, "ISDAS: A system for processing/ analyzing hyperspectral data," Can. J. Remote Sens., vol.24, no.2, pp. 99-113, Jun. 1998.
8. M. Zortea and A. Plaza, "Improved spectral unmixing of hyperspectral images using spatially homogeneous endmembers," in Proc. IEEE Int. Symp. Signal Process. Inf. Technol., 2008, pp. 258-263.
9. B. Rivard, D. Rogge, J. Feng, and J. Zhang, "Spatial constraints on endmember extraction and optimization of per-pixel endmember sets for spectral unmixing," in Proc. 1st Workshop Hyperspectral Image Signal Process.: Evol. Remote Sens., 2009, pp. 1-4.
10. A. Plaza, G. Martin, and M. Zortea, "On the incorporation of spatial information to endmember extraction: Survey and algorithm comparison," in Proc. 1stWorkshop Hyperspectral Image Signal Process.: Evol. Remote Sens., 2009, pp. 1-4.
11. M. Zortea and A. Plaza, "Spatial preprocessing for endmember extraction," IEEE Trans. Geosci. Remote Sens., vol.47, no.8, pp. 2679-2693, Aug. 2009.
12. 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.
13. D. 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.
14. J. Boardman, "Geometric mixture analysis of imaging spectrometry data," in Proc. IEEE Int. Geosci. Remote Sens. Symp., Aug. 1994, vol.4, pp. 2369-2371.
15. J. Theiler, D. Lavenier, N. Harvey, S. Perkins, and J. Szymanski, "Using blocks of skewers for faster computation of pixel purity index," in Proc. SPIE Int. Conf. Opt. Sci. Technol., 2000, vol.4132, pp. 61-71.
16. D. Lavenier, J. Theiler, J. Szymanski, M. Gokhale, and J. Frigo, "FPGA implementation of the pixel purity index algorithm," in Proc. SPIE Model., Signal Process. Control, Mar. 2002, pp. 30-41.
17. A. Bateson and B. Curtiss, "A method for manual endmember selection and spectral unmixing," Remote Sens. Environ., vol.55, no.3, pp. 229-243, Mar. 1996.
18. C. Chang, C. Wu, W. Liu, and Y. 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.
19. C. 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.
20. A. Ifarraguerri and C. Chang, "Multispectral and hyperspectral image analysis with convex cones," IEEE Trans. Geosci. Remote Sens., vol.37, no.2, pp. 756-770, Mar. 1999.
21. 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.
22. M. Craig, "Minimum-volume transforms for remotely sensed data," IEEE Trans. Geosci. Remote Sens., vol.32, no.3, pp. 542-552, May 1994.
23. 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.
24. K. Haskell and R. Hanson, "An algorithm for linear least squares problems with equality and nonnegativity constraints," Math. Program., vol.21, no.1, pp. 98-118, Dec. 1981.
25. J. C. Harsanyi and C.-I Chang, "Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach," IEEE Trans. Geosci. Remote Sens., vol.32, no.4, pp. 779-785, Jul. 1994.
26. D. Heinz and C. 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.
27. L. Miao, H. Qi, and H. Szu, "A maximum entropy approach to unsupervised mixed-pixel decomposition," IEEE Trans. Image Process., vol.16, no.4, pp. 1008-1021, Apr. 2007.
28. J. Bowles, J. Antoniades, M. Baumback, J. Grossmann, D. Haas, P. Palmadesso, and J. Stracka, "Real-time analysis of hyperspectral data sets using NRL's ORASIS algorithm," in Proc. SPIE Imaging Spectrometry III, Jul. 1997, vol.3118, pp. 38-45.
29. H. Ren and C. Chang, "Automatic spectral target recognition in hyperspectral imagery," IEEE Trans. Aerosp. Electron. Syst., vol.39, no.4, pp. 1232-1249, Oct. 2003.
30. P. Martínez, J. Gualtieri, P. Aguilar, R. Pérez, M. Linaje, J. Preciado, and A. Plaza, "Hyperspectral image classification using a self-organizing map," in Proc. Summaries XI JPL Airborne Earth Sci. Workshop, 2001.
31. P. Aguilar, A. Plaza, P. Martínez, and R. Pérez, "Endmember extraction by a self-organizing neural network on hyperspectral images," in Proc. Int. Conf. Autom. Robot. Comput. Vis., 2000, pp. 58-66.
32. A. Banerjee, P. Burlina, and J. Broadwater, "A machine learning approach for finding hyperspectral endmembers," in Proc. IEEE Int. Geosci. Remote Sens. Symp., Jul. 2007, pp. 3817-3820.
33. 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.
34. M. Berman, H. Kiiveri, R. Lagerstrom, A. Ernst, R. Dunne, and J. 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.
35. J. Wang and C. Chang, "Applications of independent component analysis (ICA) in endmember extraction and abundance quantification for hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol.44, no.9, pp. 2601-2616, Sep. 2006.
36. V. Pauca, J. Piper, and R. Plemmons, "Nonnegative matrix factorization for spectral data analysis," Linear Algebra Appl., vol.416, no.1, pp. 29-47, Jul. 2006.
37. 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.
38. J. Nascimento and J. 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
39. L. Scharf and C. Demeure, Statistical Signal Processing: Detection, Estimation, and Time Series Analysis. Reading, MA: Addison-Wesley, 1991.
40. I. Jolliffe, Principal Component Analysis. Berlin, Germany: Springer-Verlag, 2002.
41. S. Danaher and E. O'Mongain, "Singular value decomposition in multispectral radiometry," Int. J. Remote Sens., vol.13, no.9, pp. 1771-1777, Jun. 1992.
42. J. Boardman, "Inversion of imaging spectrometry data using singular value decomposition," in Proc. IEEE Int. Geosci. Remote Sens. Symp., Jul. 1989, vol.4, pp. 2069-2072.
43. R. Balakrishnan, G. Sethuraman, and R. Wilson, Graph Theory and Its Applications. New Delhi, India: Narosa Publication House, 2004.
44. J. B. Macqueen, "Some methods of classification and analysis of multivariate observations," in Proc. 5th Berkeley Symp. Math. Stat. Probab., 1967, pp. 281-297.
45. G. Ball and D. Hall, "ISODATA, a novel method of data analysis and pattern classification," Storming Media, Menlo Park, CA, 1965.
46. J. Gualtieri and R. Cromp, "Support vector machines for hyperspectral remote sensing classification," in Proc. SPIE Int. Soc. Opt. Eng., 1999, vol.3584, pp. 221-232.
47. M. He and R. Huang, Feature Selection for Hyperspectral Data Classification Using Double Parallel Feedforward Neural Networks. Berlin, Germany: Springer-Verlag, 2005, p. 58.
48. 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.
49. C. Wu, W. Liu, and C. Chang, "Exploration of methods for estimation of number of endmembers in hyperspectral imagery," in Proc. SPIE Chem. Biol. Sens. Ind. Environ. Monitoring II, Oct. 2006, vol.6378, p. 63781C.
50. J. Bioucas-Dias and J. Nascimento, "Hyperspectral subspace identification," IEEE Trans. Geosci. Remote Sens., vol.46, no.8, pp. 2435-2445, Aug. 2008.
51. H. Kwon and N. Nasrabadi, "Kernel RX-algorithm: A nonlinear anomaly detector for hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol.43, no.2, pp. 388-397, Feb. 2005.
52. S. Mei, D. Feng, and M. He, "Hopfield neural network based mixed pixel unmixing for multispectral data," in Proc. SPIE Satell. Data Compression, Commun. Process. IV, Aug. 2008, vol.7084, p. 70840C.
53. M. He and S. Mei, "Fully constrained oblique projection approach to mixed pixel linear unmixing," in Proc. ISPRS, 2008, pp. 661-666.
54. A. Plaza, P. Martinez, R. Pérez, 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.
55. N. Otsu, "A threshold selection method from gray-level histograms," Automatica, vol.11, pp. 285-296, 1975.