Spatially adaptive hyperspectral unmixing

IEEE Transactions on Geoscience and Remote Sensing

Volumn 49, Issue 11 PART 1, 2011, Pages 4248-4262

  Canham, Kelly ^a   Schlamm, Ariel ^a   Ziemann, Amanda ^a   Basener, Bill ^a   Messinger, David ^a  

^a ROCHESTER INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Hypercubes; image region analysis; remote sensing; spectral analysis

Indexed keywords

ENDMEMBERS; GLOBAL SCALE; HYPER-CUBES; HYPERSPECTRAL DATA ANALYSIS; HYPERSPECTRAL UNMIXING; IMAGE REGION ANALYSIS; LINEAR UNMIXING; RESIDUAL ERROR; SPATIAL INFORMATIONS; SPATIAL SCALE; SPATIALLY ADAPTIVE; SPECTRAL UNMIXING; UNMIXING;

DATA REDUCTION; PIXELS; REMOTE SENSING; SPECTRUM ANALYSIS;

ESTIMATION;

ALGORITHM; DATA INTERPRETATION; DATA SET; ERROR ANALYSIS; ESTIMATION METHOD; IMAGE ANALYSIS; PIXEL; REMOTE SENSING; SPATIAL ANALYSIS; SPECTRAL ANALYSIS;

EID: 80455124131     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2011.2169680     Document Type: Article

References (34)

1. C. C. Borel and S. A. Gerstl, Nonlinear spectral mixing models for vegetative and soil surfaces, Remote Sens. Environ., vol. 47, no. 3, pp. 403-416, Mar. 1994
2. N. Keshava and J. F. Mustard, Spectral unmixing, IEEE Signal Process. Mag., vol. 19, no. 1, pp. 44-57, Jan. 2002 (Pubitemid 34237207)
3. J. Fowler, Q. Du, W. Zhu, and N. Younan, Classification performance of random-projection-based dimensionality reduction of hyperspectral imagery, in Proc. IEEE IGARSS, Jul. 2009, vol. 5, pp. V-76-V-79
4. C.-C. Wu and C.-I. Chang, Does an endmember set really yield maximum simplex volume? in Proc. IEEE IGARSS, Jul. 23-28, 2007, pp. 3814-3816 (Pubitemid 351243577)
5. N. Keshava, Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries, IEEE Trans. Geosci. Remote Sens., vol. 42, no. 7, pp. 1552-1565, Jul. 2004
6. 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
7. O. Eches, N. Dobigeon, and J.-Y. Tourneret, Estimating the number of endmembers in hyperspectral images using the normal compositional model and a hierarchical bayesian algorithm, IEEE J. Sel. Topics Signal Process., vol. 4, no. 3, pp. 582-591, Jun. 2010
8. J. Broadwater and A. Banerjee, A Neyman-Pearson approach to estimating the number of endmembers, in Proc. IEEE IGARSS, Jul. 2009, vol. 4, pp. IV-693-IV-696
9. 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
10. T. Tu, H. Shyu, Y. Sun, and C. Lee, Determination of data dimensionality in hyperspectral imagery-PNAPCA, Multidimensional Syst. Signal Process., vol. 10, no. 3, pp. 255-273, Jul. 1999
11. M. Newman, Modularity and community structure in networks, Proc. Nat. Acad. Sci., vol. 103, no. 23, pp. 8577-8582, Jun. 2006 (Pubitemid 43878062)
12. D. W. Messinger, A. Ziemann, B. Basener, and A. Schlamm, Spectral image complexity estimated through local convex hull volume, in Proc. IEEE 2nd WHISPERS, 2010, pp. 1-4
13. D. Messinger, A. Ziemann, W. Basener, and A. Schlamm, A metric of spectral image complexity with application to large area search, Opt. Eng., submitted for publication
14. A. K. Ziemann, D. W. Messinger, and W. F. Basener, Iterative convex hull volume estimation in hyperspectral imagery for change detection, in Proc. SPIE-Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 2010, p. 76951I
15. 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 (Pubitemid 47285207)
16. 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
17. A. Zare and P. Gader, Sparsity promoting iterated constrained endmember detection in hyperspectral imagery, IEEE Geosci. Remote Sens. Lett., vol. 4, no. 3, pp. 446-450, Jul. 2007 (Pubitemid 47117457)
18. D. M. 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
19. 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)
20. M. Zortea and A. Plaza, Spatial preprocessing for endmember extraction, IEEE Trans. Geosci. Remote Sens., vol. 47, no. 8, pp. 2679-2693, Aug. 2009
21. J. H. Gruninger, A. J. Ratkowski, and M. L. Hoke, The sequential maximum angle convex cone (SMACC) endmember model, in Proc. SPIE-Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, S. S. Shen and P. E. Lewis, Eds., 2004, vol. 5425, no. 1, pp. 1-14
22. 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
23. J. Schott, K. Lee, R. Raqueño, G. Hoffman, and G. Healey, A subpixel target detection technique based on the invariant approach, presented at the AVIRIS Workshop, 2003
24. P. Bajorski, Simplex projection methods for selection of endmembers in hyperspectral imagery, in Proc. IEEE Int. Geosci. Remote Sens. Symp., Sep. 2004, vol. 5, pp. 3207-3210 (Pubitemid 40437775)
25. C. L. Lawson and R. J. Hanson, Solving Least Squares Problems. Philadelphia, PA: SIAM, 1995, ch. 23, pp. 160-164
26. J. B. Greer, Sparse demixing, in Proc. SPIE-Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 2010, vol. 7695, no. 1, p. 76951O
27. D. Gillis, J. Bowles, E. J. Ientilucci, and D. W. Messinger, A generalized linear mixing model for hyperspectral imagery, in Proc. SPIE- Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 2008, vol. 6966, no. 1, p. 69661B
28. A. Zare and P. Gader, Pce: Piecewise convex endmember detection, IEEE Trans. Geosci. Remote Sens., vol. 48, no. 6, pp. 2620-2632, Jun. 2010
29. S. Matteoli, N. Acito, M. Diani, and G. Corsini, Local approach to orthogonal subspace-based target detection in hyperspectral images, in Proc. 1st WHISPERS, Aug. 26-28, 2009, pp. 1-4
30. N. G. Raqueno, L. E. Smith, D. W. Messinger, C. Salvaggio, R. V. Raqueno, and J. R. Schott, Megacollect 2004: Hyperspectral collection experiment of terrestrial targets and backgrounds of the RIT megascene and surrounding area (Rochester, New York), Proc. SPIE, vol. 5806, p. 554, 2005
31. R. Clark, G. Swayze, and A. Gallagher. (1993, Nov.). Mapping minerals with imaging spectroscopy. Office Mineral Resour. Bull. Online.. 2039, pp. 141-150. Available: http://speclab.cr.usgs.gov
32. G. Swayze, R. Clark, S. Sutley, and A. Gallagher, Ground-truthing AVIRIS mineral mapping at Cuprite, Nevada, in Proc. Summ. 3rd Annu. JPL Airborne Geosci. Workshop. AVIRISWorkshop. JPL Publication 192- 14, 1992, vol. 1, pp. 47-49
33. R. Clark, G. Swayze, K. E. Livo, R. F. Kokaly, S. J. Sutley, J. B. Dalton, R. R. McDougal, and C. A. Gent. (2003). Imaging spectroscopy: Earth and planetary remote sensing with the USGS tetracorder and expert systems. J. Geophys. Res. Online.. 108(5131), pp. 1-44. Availbale: http://speclab.cr.usgs. gov/PAPERS/tetracorder
34. M. Folkman, J. Pearlman, L. Liao, and P. Jarecke, EO-1/Hyperion hyperspectral imager design, development, characterization, and calibration, in Proc. SPIE-Hyperspectral Remote Sensing of the Land and Atmosphere, 2001, vol. 4151, no. 1, pp. 40-51. (Pubitemid 32400542)