Endmember extraction of hyperspectral remote sensing images based on the discrete particle swarm optimization algorithm

IEEE Transactions on Geoscience and Remote Sensing

Volumn 49, Issue 11 PART 1, 2011, Pages 4173-4176

  Zhang, Bing ^a   Sun, Xun ^a,b   Gao, Lianru ^a   Yang, Lina ^b,c  

^a CENTER FOR EARTH OBSERVATION AND DIGITAL EARTH   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^c INSTITUTE OF REMOTE SENSING APPLICATIONS   (China)

Author keywords

Endmember extraction; hyperspectral remote sensing; particle swarm optimization (PSO)

Indexed keywords

COMBINATORIAL OPTIMIZATION PROBLEMS; DISCRETE PARTICLE SWARM OPTIMIZATION; DISCRETE PARTICLE SWARM OPTIMIZATION ALGORITHM; ENDMEMBER EXTRACTION; ENDMEMBERS; HYPERSPECTRAL REMOTE SENSING; HYPERSPECTRAL REMOTE SENSING DATA; HYPERSPECTRAL REMOTE SENSING IMAGE; ORIGINAL IMAGES; ROOT-MEAN SQUARE ERRORS; VERTEX COMPONENT ANALYSIS;

ALGORITHMS; COMBINATORIAL OPTIMIZATION; IMAGE RECONSTRUCTION; REMOTE SENSING;

PARTICLE SWARM OPTIMIZATION (PSO);

ALGORITHM; IMAGE ANALYSIS; OPTIMIZATION; REMOTE SENSING; SATELLITE DATA; SPECTRAL ANALYSIS; VELOCITY;

EID: 80455164727     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2011.2131145     Document Type: Article

References (11)

1. J.W. Boardman, F. A. Kruse, and R. O. Green, Mapping target signatures via partial unmixing of aviris data, in Proc. Summaries JPL Airborne Earth Sci. Workshop, 1995, pp. 23-26
2. M. E. Winter, N-findr: An algorithm for fast autonomous spectral end-member determination in hyperspectral data, Proc. SPIE, vol. 3753, pp. 266-275, 1999
3. J. M. Nascimento and J. M. Dias, Vertex component analysis: A fast algorithm to unmix hyperspectral data, IEEE Trans. Geosci. Remote Sens., vol. 43, no. 4, pp. 898-910, Apr. 2005 (Pubitemid 40476033)
4. J. H. Gruninger, A. J. Ratkowski, and M. L. Hoke, The sequential maximum angle convex cone (SMACC) endmember model, in Proc. SPIE, vol. 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, Orlando, FL, 2004, pp. 1-14
5. R. Neville, Automatic endmember extraction from hyperspectral data for mineral exploration, in Proc. Int. Airborne Remote Sens. Conf. Exhib., 4th/21st Can. Symp. Remote Sens., Ottawa, ON, Canada, 1999
6. J. W. Boardman, Geometric mixture analysis of imaging spectrometry data, in Proc. Int. Geosci. Remote Sens. Symp., Pasadena, CA, 1994, vol. 4, pp. 2369-2371
7. R. Eberhart and J. Kennedy, A new optimizer using particle swarm theory, in Proc. MHS, 1995, pp. 39-43
8. M. Omran, A. P. Engelbrecht, and A. Salman, A PSO-based end-member selection method for spectral unmixing of multispectral satellite images, Int. J. Comput. Intell., vol. 2, no. 2, pp. 124-132, 2005
9. 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)
10. 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
11. G. Swayze, R. Clark, F. Kruse, S. Sutley, and A. Gallagher, Groundtruthing AVIRIS mineral mapping at Cuprite, Nevada, in Proc. Summaries 3rd Annu. JPL Airborne Geosci. Workshop, Pasadena, CA, 1992, vol. 1, pp. 47-49