Survey of geometric and statistical unmixing algorithms for hyperspectral images

2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2010 - Workshop Program

Volumn , Issue , 2010, Pages

  Parente, Mario ^a,b   Plaza, Antonio ^c  

^a Stanford University   (United States)

^b SETI INSTITUTE   (United States)

^c UNIVERSITY OF EXTREMADURA   (Spain)

Author keywords

Hyperspectral imaging; Spectral mixture analysis; Statistical versus geometric unmixing

Indexed keywords

COMPUTATIONAL APPROACH; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL IMAGING; IMAGING INSTRUMENTS; IMAGING SPECTROSCOPY; NATURAL ENVIRONMENTS; QUANTITATIVE ASSESSMENTS; SPATIAL EXTENT; SPATIAL RESOLUTION; SPECTRAL MIXTURE ANALYSIS; SPECTRAL SIGNATURE; SPECTRAL UNMIXING; STATISTICAL APPROACH; UNMIXING;

COMPUTATIONAL GEOMETRY; INDEPENDENT COMPONENT ANALYSIS; REMOTE SENSING; SIGNAL PROCESSING;

MIXTURES;

EID: 78649261132     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WHISPERS.2010.5594929     Document Type: Conference Paper

References (53)

1. 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, et al., "Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS)," Remote Sensing of Environment, vol. 65, no. 3, pp. 227-248, 1998.
2. 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," Journal of Geophysical Research, vol. 91, pp. 8098-8112, 1986.
3. N. Keshava and J. F. Mustard, "Spectral unmixing," IEEE Signal Processing Magazine, vol. 19, no. 1, pp. 44-57, 2002.
4. M Petrou, Mixed pixel classification: an overview, pp. 69-83, World Scientific, Feb 1999.
5. C.M. Bachmann, T.L. Ainsworth, and R.A. Fusina, "Exploiting manifold geometry in hyperspectral imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 3, pp. 441-454, 2005.
6. C.M. Bachmann, T.L. Ainsworth, and R.A. Fusina, "Improved manifold coordinate representations of large-scale hyperspectral scenes," IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 10, pp. 2786, 2006.
7. C.-I Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Kluwer Academic/Plenum Publishers: New York, 2003.
8. K. J. Guilfoyle, M. L. Althouse, and C.-I Chang, "A quantitative and comparative analysis of linear and nonlinear spectral mixture models using radial basis function neural networks," IEEE Transactions on Geoscience and Remote Sensing, vol. 39, pp. 2314-2318, 2001.
9. J. Plaza, A. Plaza, R. Perez, and P. Martinez, "On the use of small training sets for neural network-based characterization of mixed pixels in remotely sensed hyperspectral images," Pattern Recognition, vol. 42, pp. 3032-3045, 2009.
10. N. Keshava, J. Kerekes, D. Manolakis, and G. Shaw, "An algorithm taxonomy for hyperspectral unmixing," Proceedings of SPIE, vol. 4049, pp. 42-63, May 2000.
11. A. Plaza, P. Martinez, R. Perez, and J. Plaza, "A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data," IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 3, pp. 650-663, 2004.
12. N. Dobigeon and V. Achard, "Performance comparison of geometric and statistical methods for endmembers extraction in hyperspectral imagery," Proceedings of SPIE, vol. 5982, pp. 598213-1, 2005.
13. Q. Du, N. Raksuntorn, N.H. Younan, and R.L. King, "End-member extraction for hyperspectral image analysis," Applied Optics, vol. 47, no. 28, pp. 77-84, 2008.
14. M.A. Veganzones and M. Graña, "Endmember extraction methods: A short review," 12th international confernce on knowledge-based intelligent information and engineering systems, vol. 5179, pp. 400-407, Jan 2008.
15. A Santos-García, M Vélez-Reyes, S Rosario-Torres, and J.D. Chinea, "A comparison of unmixing algorithms for hyperspectral imagery," Proceedings of SPIE, vol. 7334, Jan 2009.
16. D. Heinz and C.-I Chang, "Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 39, pp. 529-545, 2001.
17. M.D. Craig, "Minimum-volume transforms for remotely sensed data," IEEE Transactions on Geoscience and Remote Sensing, vol. 32, no. 3, pp. 542-552, 1994.
18. J. M. Bioucas-Dias, "A variable splitting augmented lagrangian approach to linear spectral unmixing," Proceedings of the First IEEE GRSS Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Grenoble, France, 2009.
19. C.A Bateson, G.P Asner, and C.A Wessman, "Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis," IEEE Transactions on Geoscience and Remote Sensing, vol. 38, no. 2, pp. 1083-1094, Jun 2000.
20. J. H. Bowles, P. J. Palmadesso, J. A. Antoniades, M. M. Baumback, and L. J. Rickard, "Use of filter vectors in hyperspectral data analysis," Proc. SPIE Infrared Spaceborne Remote Sensing III, vol. 2553, pp. 148-157, 1995.
21. M. E.Winter, "N-FINDR: An algorithm for fast autonomous spectral endmember determination in hyperspectral data," Proceedings of SPIE, vol. 3753, pp. 266-277, 1999.
22. M. Zortea and A. Plaza, "A quantitative and comparative analysis of different implementations of N-FINDR: A fast endmember extraction algorithm," IEEE Geoscience and Remote Sensing Letters, vol. 6, pp. 787-791, 2009.
23. C.-I. Chang, C.-C. Wu, W. Liu, and Y.-C. Ouyang, "A new growing method for simplex-based endmember extraction algorithm," IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 10, pp. 2804-2819, 2006.
24. J. W. Boardman, F. A. Kruse, and R. O. Green, "Mapping target signatures via partial unmixing of AVIRIS data," Proc. JPL Airborne Earth Sci. Workshop, pp. 23-26, 1995.
25. 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 Transactions on Geoscience and Remote Sensing, vol. 42, no. 10, pp. 2085-2095, 2004.
26. A. Zare and P. Gader, "Hyperspectral band selection and endmember detection using sparsity promoting priors," IEEE Geoscience and Remote Sensing Letters, vol. 5, no. 2, pp. 256-260, 2008.
27. L. Miao and H. Qi, "Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization," IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 3, pp. 765-777, 2007.
28. M. Parente, J.L. Bishop, and J.F. Bell, "Spectral unmixing for mineral identification in pancam images of soils in gusev crater, mars," Icarus, vol. 203, no. 2, pp. 421-436, 2009.
29. A. Ifarraguerri and C.-I Chang, "Multispectral and hyperspectral image analysis with convex cones," IEEE Transactions on Geoscience and Remote Sensing, vol. 37, no. 2, pp. 756-770, 1999.
30. J.M.P. Nascimento and J.M. Bioucas-Dias, "Vertex component analysis: A fast algorithm to unmix hyperspectral data," IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 4, pp. 898-910, 2005.
31. X. Tao, B.Wang, and L. Zhang, "A new approach to decomposition of mixed pixels based on orthogonal bases of data space," Lecture notes in computer science, vol. 4681, pp. 1029, 2007.
32. X. Tao, B. Wang, and L. Zhang, "A new approach based on orthogonal bases of data space to decomposition of mixed pixels for hyperspectral imagery," Sci. China Ser. F-Inf. Sci., vol. 52, no. 5, pp. 843-857, May 2009.
33. J. Gruninger, A. Ratkowski, and M. Hoke, "The sequential maximum angle convex cone (smacc) endmember model," Proceedings of SPIE, vol. 5425, 2004.
34. R.A Neville, K Staenz, T Szeredi, J Lefevbre, and P Hauff, "Automatic endmember extraction from hyperspectral data for mineral exploration," International Airborne Remote Sensing Conference and Exhibition, 4 th/21 st Canadian Symposium on Remote Sensing, Ottawa, Canada, 1999.
35. J. Broadwater, A. Banerjee, and P. Burlina, Kernel methods for unmixing hyperspectral imagery, chapter 11, Wiley, 2009.
36. M.T. Eismann and R.C. Hardie, "Stochastic spectral unmixing with enhanced endmember class separation," Applied Optics, vol. 43, no. 36, pp. 6596-6608, 2004.
37. N. Dobigeon, S. Moussaoui, M. Coulon, J.Y. Tourneret, and A.O. Hero, "Joint bayesian endmember extraction and linear unmixing for hyperspectral imagery," IEEE Transactions on Signal Processing, vol. 57, no. 11, pp. 4355-4368, 2009.
38. L. Liu, B. Wang, and L. Zhang, "Decomposition of mixed pixels based on bayesian self-organizing map and gaussian mixture model," Pattern Recognition Letters, vol. 30, no. 9, pp. 820-826, Jan 2009.
39. J. Wang and C.-I Chang, "Applications of independent component analysis in endmember extraction and abundance quantification for hyperspectral imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 9, pp. 2601- 2616, Aug 2006.
40. J.M.P. Nascimento and J.M. Bioucas-Dias, "Does independent component analysis play a role in unmixing hyperspectral data?," IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 1, pp. 175-187, 2005.
41. J.M.P. Nascimento and J.M. Bioucas-Dias, "Dependent component analysis: A hyperspectral unmixing algorithm," Lecture notes in computer science, vol. 4478, pp. 612, 2007.
42. C.F. Caiafa, E. Salerno, A.N. Proto, and L. Fiumi, "Blind spectral unmixing by local maximization of non-gaussianity," Signal Processing, vol. 88, no. 1, pp. 50-68, 2008.
43. A.M. Filippi and R. Archibald, "Support vector machine-based endmember extraction," IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 3, pp. 771-791, 2009.
44. A. Filippi, R. Archibald, B. Bhaduri, and E. Bright, "Hyperspectral agricultural mapping using support vector machine-based endmember extraction (SVM-BEE)," Optics Express, vol. 17, no. 26, pp. 23823-23842, Jan 2009.
45. H Xia and P Guo, "Decomposition mixed pixels of remote sensing image based on 2-dwt and kernel ica," Lecture notes in computer science, vol. 5863, pp. 373-380, 2009.
46. W. Liu, K.C. Seto, E.Y. Wu, S. Gopal, and C.E. Woodcock, "Art-mmap: a neural network approach to subpixel classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 9, pp. 1976-1983, 2004.
47. R. Pu, P. Gong, R. Michishita, and T. Sasagawa, "Spectral mixture analysis for mapping abundance of urban surface components from the terra/aster data," Remote Sensing of Environment, vol. 112, no. 3, pp. 939-954, 2008.
48. A. Plaza, P. Martinez, R. Perez, and J. Plaza, "Spatial/spectral endmember extraction by multidimensional morphological operations," IEEE Transactions on Geoscience and Remote Sensing, vol. 40, no. 9, pp. 2025-2041, 2002.
49. 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 Sensing of Environment, vol. 110, no. 3, pp. 287-303, 2007.
50. R.S. Rand and D.M. Keenan, "A spectral mixture process conditioned by gibbs-based partitioning," IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 7, pp. 1421-1434, 2001.
51. M. Zortea and A. Plaza, "Spatial preprocessing for endmember extraction," IEEE Transactions on Geoscience and Remote Sensing, vol. 47, pp. 2679-2693, 2009.
52. 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," Journal of Geophysical Research, vol. 108, pp. 1-44, 2003.
53. J. M. Bioucas-Dias and J. M. P. Nascimento, "Hyperspectral subspace identification," IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 8, pp. 2435-2445, 2008.