Hyperspectral data unmixing algorithm comparative analysis based on linear spectral mixture model

Proceedings - 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2013

Volumn 2, Issue , 2013, Pages 11-14

  Baozhi, Cheng ^a  

^a DAQING NORMAL UNIVERSITY   (China)

Author keywords

Endmember extraction; Hyperspectral images unmixing; Linear spectral mixture model

Indexed keywords

BETTER PERFORMANCE; COMPARATIVE ANALYSIS; ENDMEMBER EXTRACTION; HYPERSPECTRAL DATA UNMIXING; LINEAR SPECTRAL MIXTURE MODEL; QUANTITATIVE APPROACH; UNMIXING; UNMIXING ALGORITHMS;

CYBERNETICS; MIXTURES; SPECTROSCOPY;

ALGORITHMS;

EID: 84891874869     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IHMSC.2013.150     Document Type: Conference Paper

References (15)

1. ZHANG Wei, DU Pei-jun, ZHANG Hua-peng, "Mixed pixel decomposition in hyperspectral remote sensing image based on neural network", Bulletin of surveying and mapping,2007(7): 23-26.
2. Plaza, A., Martínez, P., and Pérez, R., "A quantitative and compareative analysis of endmember extraction algorithms from hyperspectral data," IEEE Trans. Geoscience Remote Sense vol. 42, pp. 650-663, 2004.
3. J. Boardman,"Automating spectral unmixing of AVIRIS data using convegeometry concept", in Proc. AVI-RIS Workshop, JPL Pub.93-26, 1993, vol. 1, pp. 11-14.
4. M. E. Winter, "N-finder:An algorithm for fast autonomous spectral endmember determination in hyperspectral data", in Proc. SPIE Conf. Imaging Spectrometry V, vol. 3753, pp. 266-275,1999.
5. J. Nascimento and J. Bioucas-Dias, "Vertex component analysis: A fast algorithm to unmix hyperspectral data", IEEE Trans. Geosci. Remote Sens., vol. 37, no. 2, pp. 898-910, Apr. 2002.
6. Agustin Ifarraguerri, Chein-I Chang, "Multispectral and Hyperspectral Image Analysis with Convex Cones", IEEE Trans. Geosci. Remote Sens., vol. 37, no. 2, pp. 756-770, Jan. 1999.
7. 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.
8. Chein-I Chang, Chao-Cheng Wu, Ching-Tsorng Tsai. "Random N-Finder (N-FINDR) Endmember Extraction Algorithms for Hyperspectral Imagery", IEEE Trans. Geosci. Remote Sens., vol. 20, pp. 641-656,2011.
9. Jun Li, J. M. Bioucas-Dias, "Minimum Volume Simplex Analysis: A Fast Algorithm To Unmix Hyperspectral Data", IGARSS 2008, pp. 250-253.
10. N. Keshava, J. F. Mustard, "Spectral unmixing", IEEE Signal Process. Mag., vol. 19,44-57,2002.
11. A. Plaza, P. Martinez, R. Perez, J. Plaza, "A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data", IEEE Trans. Geosci. Remote Sens., vol. 42, 650-663,2004.
12. R. N. Clark, G. A. Swayze, R. Wise, et al, "USGS Digital Spectral Library splib05a", USGS Open File Report 03-395, 2003.
13. C Ichoku, A Karnieli, "A review of mixture modeling techniques for sub-pixel land cover estimation," Remote Sensing Reviews, vol. 13, pp. 161-186, 1996.
14. Christopher M., "Bishop. Pattern Recognition and Machine Learning", Springer, pp. 450, 2006.
15. John A. Richards, Xiuping Jia, "Remote Sensing Didital Image Analysis", 4rd ed., Ye ZHANG et al., Tran., Beijing:pub. House of electronics industry, pp. 159,2009.