High-order statistics-based approaches to endmember extraction for hyperspectral imagery

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 6966, Issue , 2008, Pages

  Chu, Shih Yu ^a   Ren, Hsuan ^b   Chang, Chein I ^a  

^a University of Marylandw Baltimore County   (United States)

^b NATIONAL CENTRAL UNIVERSITY   (Taiwan)

Author keywords

Endmember extraction algorithm (EEA); High order statistics (HOS)

Indexed keywords

ENDMEMBER EXTRACTION; ENDMEMBER EXTRACTION ALGORITHMS; ENDMEMBERS; HIGH ORDER STATISTICS (HOS); HYPER-SPECTRAL; HYPERSPECTRAL IMAGERY (HSI); MULTI-SPECTRAL (MS); ULTRASPECTRAL;

ALGORITHMS; CLUSTER ANALYSIS; DISCRIMINANT ANALYSIS; EVOLUTIONARY ALGORITHMS; EXTRACTION; FIBER OPTIC SENSORS; NEODYMIUM; REGRESSION ANALYSIS; SET THEORY; SOLVENT EXTRACTION; STATISTICAL METHODS; TECHNOLOGY;

STATISTICS;

EID: 44949200346     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.777725     Document Type: Conference Paper

References (14)

1. nd ed., Academic Press, 1997.
2. J.W. Boardman, "Geometric mixture analysis of imaging spectrometery data," Proc. Int. Geoscience and Remote Sensing Symp., vol. 4, pp. 2369-2371, 1994
3. M.E. Winter, "N-finder: an algorithm for fast autonomous spectral endmember determination in hyperspectral data," Image Spectrometry V, Proc. SPIE 3753, pp. 266-277, 1999.
4. J.M.P. Nascimento and J.M. Dias, "Vertex component analysis: a fast algorithm to unmix hyperspectral data," IEEE Trans. Geoscience and Remote Sensing, vol. 43, no. 4, pp. 898-910, April 2005.
5. C.-I Chang, C.-C. Wu, W. Liu and Y.C. Ouyang, C.-I Chang, C. Wu, W. Liu and Y.C. Ouyang, "A growing method for simplex-based endmember extraction algorithms," IEEE Trans. on Geoscience and Remote Sensing, vol. 44, no. 10, pp. 2804-2819, October, 2006
6. S. Chu, C.C. Wu and C.-I Chang, "Statistics-based endmember extraction algorithms for hyperspectral imagery," SPIE Conf. Imaging Spectrometry XII, SPIE Symposium on Optics & Photonics, San Diego, CA, August, 2007.
7. D. Heinz and C.-I Chang, "Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imagery," IEEE Trans. Geosci. Remote Sensing, vol. 39, no. 3, pp. 529-545, 2001.
8. R.A. Neville, K. Staenz, T. Szeredi, J. Lefebvre and P. Hauff, "Automatic endmember extraction from hyperspectral data for mineral exploration," In: Proc. 4th International Airborne Remote Sensing Conf. and Exhibition/21st Canadian Symposium on remote Sensing, Ottawa, Ontario, Canada, pp. 21-24, June 1999.
9. Ren, H, Q. Du, J. Wang, C.-I Chang and J. Jensen (2006), "Automatic traget recognition hyperspectral imagery using high order statistics," IEEE Trans. on Aerospace and Electronic Systems, vol. 42, no. 4, pp. 1372-1385, Oct. 2006.
10. A. Hyvarinen, J. Karhunen and E. Oja, Independent Component Analysis, John Wiley & Sons, 2001.
11. A. Hyvarinen and E. Oja, "A fast fixed-point for independent component analysis," Neural Computation, vol. 9, no. 7, pp. 1483-1492, 1997.
12. W. Liu and C.-I Chang, "Variants of Principal Components Analysis," 2007 International Geoscience and Remote Sensing Symposium, Barcelona, Spain, 2007.
13. http://aviris.jpl.nasa.gov/
14. A.A. Green, M. Berman, P. Switzer and M.D. Craig (1988), "A transformation for ordering multispectral data in terms of image quality with implications for noise removal," IEEE Trans. on Geosciences and Remote Sensing, vol. 26, pp. 65-74, 1988.