Fuzzy assessment of spectral unmixing algorithms

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 7, Issue 6, 2014, Pages 1947-1955

  Jia, Xiuping ^a,b   Wang, Liguo ^c  

^a BEIJING NORMAL UNIVERSITY   (China)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

^c HARBIN ENGINEERING UNIVERSITY   (China)

Author keywords

Accuracy assessment; fuzzy measures; spectral unmixing

Indexed keywords

ALGORITHMS; FUZZY SYSTEMS;

ACCURACY ASSESSMENT; FUZZY ASSESSMENTS; FUZZY MEASURES; GROUND TRUTH DATA; MEAN SQUARED ERROR; SPECTRAL MEASUREMENT; SPECTRAL UNMIXING; SPECTRAL VARIATION;

UNCERTAINTY ANALYSIS;

ACCURACY ASSESSMENT; ALGORITHM; PERFORMANCE ASSESSMENT; SPECTRAL ANALYSIS; UNCERTAINTY ANALYSIS;

EID: 84905917436     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2013.2264313     Document Type: Article

References (36)

1. J. A. Richards, Remote Sensing Digital Image Analysis, 5th ed. Berlin, Germany: Springer-Verlag, 2013.
2. N. Keshava and J. F. Mustard, "Spectral unmixing," IEEE Signal Process. Mag., pp. 44-57, 2002. (Pubitemid 34237207)
3. A. Plaza, Q. Du, J. M. Bioucas-Dias, X. Jia, and F. A. Kruse, "Foreword to the special issue on spectral unmixing of remotely sensed data," IEEE Trans. Geosci. Remote Sens., vol. 49, pp. 4103-4110, 2011.
4. J. M. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, and J. Chanussot, "Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, pp. 354-379, 2012.
5. 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, pp. 650-663, 2004.
6. 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, pp. 2601-2616, 2006. (Pubitemid 44321553)
7. 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, pp. 765-777, 2007. (Pubitemid 46375748)
8. J. B. Adams, M. O. Smith, and P. E. Johnson, "Spectralmixture modeling: A new analysis of rock and soil types at the Viking Lander site," J. Geophys. Res., vol. 91, pp. 8098-8112, 1986.
9. T. W. Ray and B. C. Murray, "Nonlinear spectral mixing in desert vegetation," Remote Sens. Environ., vol. 55, pp. 59-64, 1996.
10. X. Chen, J. Chen, X. Jia, B. Somers, J. Wu, and P. Coppin, "A quantitative analysis of virtual endmembers' increased impact on the collinearity effect in spectral unmixing," IEEE Trans. Geosci. Remote Sens., vol. 49, pp. 2945-2956, 2011.
11. F. D. van der Meer and X. Jia, "Collinearity and orthogonality of endmembers in linear spectral unmixing," Int. J. Appl. Earth Observ. Geoinf., vol. 18, pp. 491-503, 2012.
12. D. A. Roberts, M. O. Smith, and J. B. Adams, "Discriminating green vegetation, non-photosynthetic vegetation, and soils in AVIRIS data," Remote Sens. Environ., vol. 44, pp. 255-269, 1993. (Pubitemid 23383187)
13. J. Settle, "On the effect of variable endmember spectra in the linear mixture model," IEEE Trans. Geosci. Remote Sens., vol. 44, pp. 389-396, 2006. (Pubitemid 43146058)
14. D. C. Heinz and C.-I Chang, "Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imager," IEEE Trans. Geosci. Remote Sens., vol. 39, pp. 529-545, 2001. (Pubitemid 32400422)
15. L. Wang and X. Jia, "Integration of soft and hard classification using extended support vector machine," IEEE Geosci. Remote Sens. Lett., vol. 6, pp. 543-547, 2009.
16. 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, pp. 3725-3736, 2006.
17. C. Song, "Spectralmixture analysis for subpixel vegetation fractions in the urban environment: How to incorporate endmember variability?," Remote Sens. Environ., vol. 95, pp. 248-263, 2005. (Pubitemid 40328616)
18. D. A. Roberts, M. Gardner, R. Church, S. Ustin, G. Scheer, and R. O. Green, "Mapping chaparral in the Santa Monica mountains using multiple endmember spectral mixture models," Remote Sens. Environ., vol. 65, pp. 267-279, 1998. (Pubitemid 29360703)
19. G. P. Asner and D. B. Lobell, "A biogeophysical approach for automated SWIR unmixing of soils and vegetation," Remote Sens. Environ., vol. 74, pp. 99-112, 2000.
20. P. E. Dennison and D. A. Roberts, "Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE," Remote Sens. Environ., pp. 123-135, 2003. (Pubitemid 39661164)
21. C. A. Bateson, G. P. Asner, and C. A.Wessman, "Endmember bundles: A new approach to incorporating endmember variability into spectral mixture analysis," IEEE Trans. Geosci. Remote Sens., vol. 38, pp. 1083-1094, 2000. (Pubitemid 30594771)
22. A. Zare and P. Gader, "PCE: Piecewise vonvex endmember detection," IEEE Trans. Geosci. Remote Sens., vol. 48, pp. 2620-2632, 2010.
23. 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, pp. 582-591, 2010.
24. B. Somers, M. Zortea, A. Plaza, and G. P. Asner, "Automated extraction of image-based endmember bundles for improved spectral unmixing," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, pp. 396-408, 2012.
25. D. T. Anderson and A. Zare, "Spectral unmixing cluster validity index for multiple sets of endmembers," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, pp. 1282-1295, 2012.
26. A. H. Strahler, L. Boschetti, G. M. Foody, M. Friedl, M. Hansen, P. Mayaux, J. Morisette, S. Stehman, and C. E. Woodcock, "Global land cover validation: Recommendations for evaluation and accuracy assessment of global land cover maps," 2006 [Online]. Available: http://nofc.cfs.nrcan.gc.ca/ gofc-gold/Report Series/GOLD-25.pdf
27. R. G. Congalton and K. Green, Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, 2nd ed. Boca Raton, FL, USA: CRC Press, 2008.
28. P. C. Smits, S. G. Dellepiane, and R. A. Schowengerdt, "Quality assessment of image classification algorithms for land-cover mapping: A review and a proposal for a cost-based approach," Int. J. Remote Sens., vol. 20, pp. 1461-1486, 2009.
29. M. J. Carlotto, "Effect of errors in ground truth on classification accuracy," Int. J. Remote Sens., vol. 30, pp. 4831-4849, 2009.
30. G. M. Foody, "Status of land cover classification accuracy assessment," Remote Sens. Environ., vol. 80, pp. 185-201, 2002. (Pubitemid 34271212)
31. G. M. Foody, "Assessing the accuracy of land cover change with imperfect ground reference data," Remote Sens. Environ., vol. 114, pp. 2271-2285, 2010.
32. S. Gopal and C. E. Woodcock, "Theory and methods for accuracy assessment of thematic maps using fuzzy sets," PE&RS, vol. 60, pp. 181-188, 1994. (Pubitemid 24404027)
33. T. Bajjouk, J. Populus, and B. Guillaumont, "Quantification of subpixel cover fractions using principal component analysis and a linear programming method: Application to the coastal zone of Roscoff (France)," Remote Sens. Environ., vol. 64, pp. 153-165, 1998. (Pubitemid 29360695)
34. I. I. Dikin, "Iterative solution of problems of linear and quadratic programming," Soviet Math. Dokl., vol. 8, pp. 674-675, 1967.
35. D. A. Roberts, M. O. Smith, and J. B. Adams, "Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data," Remote Sens. Environ., vol. 44, pp. 255-269, 1993. (Pubitemid 23383187)
36. D. A. Roberts, K. Q. Halligan, and P. E. Dennison, ViperTools, 2007 [Online]. Available: http://www.vipertools.org