Two effective and computationally efficient pure-pixel based algorithms for hyperspectral endmember extraction

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2011, Pages 1369-1372

  Ambikapathi, Arulmurugan ^a   Chan, Tsung Han ^a   Chi, Chong Yung ^a   Keizer, Kannan ^a  

^a NATIONAL TSING HUA UNIVERSITY   (Taiwan)

Author keywords

Endmember extraction; Endmember identifiability; Hyperspectral images; Pure pixels

Indexed keywords

COMPUTATIONALLY EFFICIENT; ENDMEMBER EXTRACTION; ENDMEMBER EXTRACTION ALGORITHMS; ENDMEMBERS; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL; HYPERSPECTRAL OBSERVATIONS; HYPERSPECTRAL UNMIXING; IDENTIFIABILITY; MINERAL COMPOSITION; MONTE CARLO SIMULATION; PURE PIXEL;

ALGORITHMS; COMPUTER SIMULATION; MONTE CARLO METHODS; PIXELS; SIGNAL PROCESSING; SPEECH COMMUNICATION;

COMPUTATIONAL EFFICIENCY;

EID: 80051658187     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2011.5946667     Document Type: Conference Paper

References (12)

1. N. Keshava and J. Mustard, " Spectral unmixing," IEEE Signal Process. Mag., vol. 19, no. 1, pp. 44-57, Jan. 2002.
2. J. W. Boardman, F. A. Kruse, and R. O. Green, " Mapping target signatures via partial unmixing of AVIRIS data," in Proc. Summ. JPL Airborne Earth Sci. Workshop, Pasadena, CA, Dec. 9-14, 1995, pp. 23-26.
3. M. E. Winter, " N-findr: An algorithm for fast autonomous spectral end-member determination in hyperspectral data," in Proc. SPIE Conf. Imaging Spectrometry, Pasadena, CA, Oct. 1999, pp. 266-275.
4. A. Ifarraguerri and C.-I. Chang, " Multispectral and hyperspectral image analysis with convex cones," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 2, pp. 756-770, Mar. 1999.
5. C. I. Chang, C. C. Wu, W. M. Liu, and Y. C. Ouyang, "A new growing method for simplex-based endmember extraction algorithm," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 10, pp. 2804-2819, Oct. 2006.
6. C. I. Chang, C. C. Wu, C. S. Lo, and M. L. Chang, " Real-time simplex growing algorithms for hyperspectral endmember extraction," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 4, pp. 1834-1850, Apr. 2010.
7. J. M. P. Nascimento and J. M. B. 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.
8. T.-H. Chan, C.-Y. Chi, W.-K. Ma, and A. Ambikapathi, " Hyperspectral unmixing from a convex analysis and optimization perspective," in Proc. First IEEE WHISPERS, Grenoble, France, Aug. 26-28, 2009.
9. D. Heinz and C.-I. Chang, " Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 3, pp. 529-545, 2001.
10. T.-H. Chan, C.-Y Chi, Y.-M. Huang, and W-K. Ma, "A convex analysis based minimum-volume enclosing simplex algorithm for hyperspectral unmixing," IEEE Trans. Signal Processing, vol. 57, no. 11, pp. 4418-4432, Nov. 2009.
11. S. Boyd and L. Vandenberghe, Convex Optimization, UK: Cambridge Univ. Press, 2004.
12. Tech. Rep., Available online: http://speclab.cr.usgs.gov/cuprite.html.