Nonlinear spectral mixture analysis for hyperspectral imagery in an unknown environment

IEEE Geoscience and Remote Sensing Letters

Volumn 7, Issue 4, 2010, Pages 836-840

  Raksuntorn, Nareenart ^a   Du, Qian ^b  

^a SUAN SUNANDHA RAJABHAT UNIVERSITY   (Thailand)

^b MISSISSIPPI STATE UNIVERSITY   (United States)

Author keywords

Abundance estimation; hyperspectral imagery; nonlinear mixture analysis; nonlinear mixture model (NLMM)

Indexed keywords

ABUNDANCE ESTIMATION; CONSTRUCTION PROCESS; ENDMEMBERS; HYPERSPECTRAL IMAGERY; IMAGE SCENE; LINEAR MIXTURE MODELS; LINEAR UNMIXING; MULTIPLE SCATTERING EFFECT; NON-LINEAR MIXTURE MODELS; NONLINEAR MIXTURES; NONLINEAR TERMS; NONNEGATIVITY CONSTRAINTS; PRIOR INFORMATION; RECONSTRUCTION ERROR; SPECTRAL MIXTURE ANALYSIS; UNKNOWN ENVIRONMENTS;

ESTIMATION; MIXTURES; PIXELS; REMOTE SENSING;

NONLINEAR ANALYSIS;

EID: 77957976782     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2010.2049334     Document Type: Article

References (23)

1. D. B. Nash and J. E. Conel, "Spectral reflectance systematics for mixtures of powdered hypersthene, labradorite, and ilmenite", J. Geophy. Res., vol. 79, pp. 1615-1621, 1974.
2. D. A. Roberts, J. B. Adams, and M. O. Smith, "Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data", Remote Sens. Environ., vol. 44, no. 2/3, pp. 255-269, May/Jun. 1993.
3. C. C. Borel and S. A. W. Gerstl, "Nonlinear spectral mixing model for vegetative and soil surfaces", Remote Sens. Environ., vol. 47, no. 3, pp. 403-416, Mar. 1994.
4. J. F. Mustard, L. Li, and G. He, "Nonlinear spectral mixture modeling of lunar multispectral data: Implications for lateral transport", J. Geophy. Res., vol. 103, no. E8, pp. 19 419-19 425, Aug. 1998.
5. B. Hapke, Theory of Reflectance and Emittance Spectroscopy. Cambridge, U. K.: Cambridge Univ. Press, 1993.
6. A. Moody, S. Gopal, and A. H. Strahler, "Artificial neural network response to mixed pixels in coarse-resolution satellite data", Remote Sens. Environ., vol. 58, no. 3, pp. 329-343, Dec. 1996.
7. G. M. Foody, R. M. Lucas, P. J. Curran, and M. Honzak, "Non-linear mixture modeling without end-members using an artificial neural network", Int. J. Remote Sens., vol. 18, no. 4, pp. 937-953, Mar. 1997.
8. G. A. Carpenter, S. Gopal, S. Macomber, S. Martens, and C. E. Woodcock, "A neural network method for mixture estimation for vegetation mapping", Remote Sens. Environ., vol. 70, no. 2, pp. 138-152, Nov. 1999.
9. K. J. Guilfoyle, M. L. Althouse, and C.-I. Chang, "A quantitative and comparative analysis of linear and nonlinear mixture models using radial basis function neural networks", IEEE Trans. Geosci. Remote Sens., vol. 39, no. 8, pp. 2314-2318, Aug. 2001.
10. X.-H. Liu, A. K. Skidmore, and H. Van Oosten, "An experimental study on spectral discrimination capability of a backpropagation neural network classifier", Int. J. Remote Sens., vol. 24, no. 4, pp. 673-688, Feb. 2003.
11. W. Liu and E. Y. Wu, "Comparison of non-linear mixture models: Subpixel classification", Remote Sens. Environ., vol. 94, no. 2, pp. 145-154, Jan. 2005.
12. J. Ravi, S. T. Lee, M. Paulraj, and R. Hernandez, "Feasibility of neural network approach in spectral mixture analysis of reflectance spectra", Int. J. Remote Sens., vol. 29, no. 10, pp. 2981-2992, 2008.
13. C. Huang and J. R. G. Townshend, "A stepwise tree for nonlinear approximation: applications to estimating subpixel land cover", Int. J. Remote Sens., vol. 24, no. 1, pp. 75-90, 2003.
14. L. Zhang, B. Wu, B. Huang, and P. Li, "Nonlinear estimation of subpixel proportion via kernel least square regression", Int. J. Remote Sens., vol. 28, no. 18, pp. 4157-4172, 2007.
15. T. W. Ray and R. C. Murray, "Nonlinear spectral mixing in desert vegetation", Remote Sens. Environ., vol. 55, no. 1, pp. 59-64, Jan. 1996.
16. L. Zhang, D. Li, Q. Tong, and L. Zheng, "Study of the spectral mixture model of soil and vegetation in PoYang Lake area, China", Int. J. Remote Sens., vol. 19, no. 11, pp. 2077-2084, Jul. 1998.
17. X. Chen and L. Vierling, "Spectral mixture analyses of hyperspectral data acquired using a tethered balloon", Remote Sens. Environ., vol. 103, no. 3, pp. 338-350, Aug. 2006.
18. D. E. Sabol, J. B. Adams, and M. O. Smith, "Quantitative sub-pixel spectral detection of targets in multispectral images", J. Geophy. Res., vol. 97, no. E2, pp. 2659-2672, Feb. 1992.
19. 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, no. 3, pp. 267-279, Sep. 1998.
20. D. M. Rogge, B. Rivard, J. Zhang, and J. Feng, "Iterative spectral unmixing for optimizing per-pixel endmembers sets", IEEE Trans. Geosci. Remote Sens., vol. 44, no. 12, pp. 3725-3736, Dec. 2006.
21. N. Raksuntorn and Q. Du, "Endmember-variable linear mixture model for hyperspectral image analysis", in Proc. IEEE IGARSS, Jul. 2008, vol. III, pp. 258-261.
22. D. Heinz and C.-I. Chang, "Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imager", IEEE Trans. Geosci. Remote Sens., vol. 39, no. 3, pp. 529-545, Mar. 2001.
23. 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.