Geometric method of fully constrained least squares linear spectral mixture analysis

IEEE Transactions on Geoscience and Remote Sensing

Volumn 51, Issue 6, 2013, Pages 3558-3566

  Wang, Liguo ^a   Liu, Danfeng ^a   Wang, Qunming ^b  

^a HARBIN ENGINEERING UNIVERSITY   (China)

^b HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Fully constrained (FC) least squares (FCLS); hyperspectral; linear spectral mixture analysis (LSMA); spectral unmixing

Indexed keywords

COMPUTATIONAL BURDEN; FULLY CONSTRAINED LEAST SQUARES (FCLS); HYPERSPECTRAL; ITERATIVE PROCESSING; LEAST SQUARE; LEAST SQUARES CRITERION; LINEAR SPECTRAL MIXTURE ANALYSIS; SPECTRAL UNMIXING;

DATA PROCESSING; MIXTURES;

ITERATIVE METHODS;

DATA PROCESSING; GEOMETRY; IMAGE ANALYSIS; SPECTRAL ANALYSIS;

EID: 84878140363     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2012.2225841     Document Type: Article

References (22)

1. Y. Yan, Y. Zhao, H.-F. Xue, X.-D. Kou, and Y. Liu, "Integration of spatial-spectral information for hyperspectral image classification," in Proc. 2nd IITA-GRS, 2010, vol. 1, pp. 242-245.
2. J. B. Adams and M. O. Smith, "Spectral mixture modeling: A new analysis of rock and soil types at the Viking lander 1 suite," J. Geophys. Res., vol. 91, no. B8, pp. 8098-8112, Jul. 10, 1986.
3. S. Jia and Y. Qian, "Spectral and spatial complexity-based hyperspectral unmixing," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 12, pp. 3867-3879, Dec. 2007. (Pubitemid 350157825)
4. 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.
5. M. Brown, H. Lewis, and S. Gunn, "Linear spectral mixture models and support vector machine for remote sensing," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 5, pp. 2346-2360, Sep. 2000.
6. J. Li and L. M. Bruce, "Wavelet-based feature extraction for improved endmember abundance estimation in linear unmixing of hyperspectral signals," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 3, pp. 644-649, Mar. 2004.
7. Y. E. Shimabukuro and J. A. Smith, "The least-squares mixing models to generate fraction images derived from remote sensing multispectral data," IEEE Trans. Geosci. Remote Sens., vol. 29, no. 1, pp. 16-20, Jan. 1991.
8. J. Boardman, "Inversion of high spectral resolution data," in Proc. SPIE, 1990, vol. 1298, pp. 222-233.
9. Y. E. Shimabukuro, "Shade images derived from linear mixing models of multispectral measurements of forested areas," Ph.D. dissertation, Dept. Forest Wood Sci., Colorado State Univ., Fort Collins, CO, 1987.
10. J. J. Settle and N. A. Drake, "Linear mixing and estimation of ground cover proportions," Int. J. Remote Sens., vol. 14, no. 6, pp. 1159-1177, Apr. 1993. (Pubitemid 23384230)
11. D. C. Heinz and C.-I. 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. (Pubitemid 32400422)
12. D. Heinz, C.-I. Chang, and M. L. G. Althouse, "Fully constrained least squares-based linear unmixing," in Proc. Int. Geosci. Remote Sens. Symp., Hamburg, Germany, 1999, pp. 1401-1403. (Pubitemid 30539004)
13. X. Geng, B. Zhang, X. Zhang, and L. Zheng, "An unmixing method of hyperspectral imagery based on convex volume in high dimensional space," (in Chinese), Prog. Nat. Sci., vol. 14, no. 7, pp. 810-814, 2004.
14. M. Zortea and A. Plaza, "A quantitative and comparative analysis of different implementations of N-FINDR: A fast endmember extraction algorithm," IEEE Geosci. Remote Sens. Lett., vol. 6, no. 4, pp. 787-791, Oct. 2009.
15. L. Wang, X. Jia, and Y. Zhang, "A novel geometric algorithm of feature selection for hyperspectral imagery," IEEE Geosci. Remote Sens. Lett., vol. 4, no. 1, pp. 171-175, Jan. 2007. (Pubitemid 46180279)
16. L. Wang and Y. Zhang, "Speed-up for N-FINDR algorithm," J. Harbin Inst. Technol., vol. 15, no. 1, pp. 141-144, 2008, (New Series). (Pubitemid 351511558)
17. W. Luo, L. Zhong, B. Zhang, and L.-R. Gao, "Subspace distance based spectral unmixing method for hyperspectral imagery," (in Chinese), Prog. Nat. Sci., vol. 18, no. 10, pp. 1175-1180, 2008.
18. I. T. Jolliffe, Principal Component Analysis. New York: Springer-Verlag, 1986.
19. X. Liu, L. Gao, B. Zhang, X. Zhang, and W. Luo, "An improved MNF transform algorithm on hyperspectral images with complex mixing ground objects," in Proc. 1st Int. CISP, 2008, vol. 3, pp. 479-483.
20. R. Heylen, D. Burazerovic, and P. Scheunders, "Fully constrained least squares spectral unmixing by simplex projection," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 11, pp. 4112-4122, Nov. 2011.
21. L. Wang and X. Jia, "Integration of soft and hard classification using extended support vector machines," IEEE Geosci. Remote Sens. Lett., vol. 6, no. 3, pp. 543-547, Jul. 2009.
22. L. David, "Multispectral data analysis: A signal theory perspective," Purdue Univ., West Lafayette, IN, 1998.