On endmember identification in hyperspectral images without pure pixels: A comparison of algorithms

Journal of Mathematical Imaging and Vision

Volumn 42, Issue 2-3, 2012, Pages 163-175

  Plaza, Javier ^a   Hendrix, Eligius M T ^b   García, Inmaculada ^b   Martín, Gabriel ^a   Plaza, Antonio ^a  

^a UNIVERSITY OF EXTREMADURA   (Spain)

^b UNIVERSITY OF MÁLAGA   (Spain)

Author keywords

Endmember extraction; Hyperspectral imaging; Minimum enclosing simplex; Spectral unmixing

Indexed keywords

ACTIVE AREA; COMPARATIVE ANALYSIS; COMPARATIVE ASSESSMENT; ENDMEMBER EXTRACTION; ENDMEMBER EXTRACTION ALGORITHMS; ENDMEMBERS; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL; HYPERSPECTRAL DATA; HYPERSPECTRAL IMAGING; IDENTIFICATION ALGORITHMS; IMAGING SENSORS; JET PROPULSION LABORATORY; MINIMUM ENCLOSING SIMPLEX; MIXED PIXEL; PURE PIXEL; RECENT TRENDS; SPATIAL RESOLUTION; SPECTRAL SIGNATURE; SPECTRAL UNMIXING;

ALGORITHMS; IMAGE RESOLUTION; INTERNAL COMBUSTION ENGINES; NASA;

PIXELS;

EID: 84863442455     PISSN: 09249907     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10851-011-0276-0     Document Type: Article

References (29)

1. Goetz, A.F.H., Vane, G., Solomon, J.E., Rock, B.N.: Imaging spectrometry for Earth remote sensing. Science 228, 1147-1153 (1985)
2. Green, R.O., Eastwood, M.L., Sarture, C.M., Chrien, T.G., Aronsson, M., Chippendale, B.J., Faust, J.A., Pavri, B.E., Chovit, C.J., Solis, M., et al.: Imaging spectroscopy and the airborne visible/ infrared imaging spectrometer (AVIRIS). Remote Sens. Environ. 65(3), 227-248 (1998) (Pubitemid 29360700)
3. Plaza, A., Martinez, P., Perez, R., Plaza, J.: A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data. IEEE Trans. Geosci. Remote Sens. 42(3), 650-663 (2004)
4. Du, Q., Raksuntorn, N., Younan, N.H., King, R.L.: End-member extraction for hyperspectral image analysis. Appl. Opt. 47, 77-84 (2008)
5. Harsanyi, J.C., Chang, C.-I.: Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection. IEEE Trans. Geosci. Remote Sens. 32(4), 779-785 (1994)
6. Winter, M.E.: N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data. In: Proc. SPIE Image Spectrometry V, vol. 3753, pp. 266-277 (2003)
7. Nascimento, J.M.P., Bioucas-Dias, J.M.: Vertex component analysis: A fast algorithm to unmix hyperspectral data. IEEE Trans. Geosci. Remote Sens. 43(4), 898-910 (2005) (Pubitemid 40476033)
8. Neville, R.A., Staenz, K., Szeredi, T., Lefebvre, J., Hauff, P.: Automatic endmember extraction from hyperspectral data for mineral exploration. In: Proc. 21st Canadian Symp. Remote Sens, pp. 21-24 (1999)
9. Plaza, A., Martinez, P., Perez, R., Plaza, J.: Spatial/spectral endmember extraction by multidimensional morphological operations. IEEE Trans. Geosci. Remote Sens. 40(9), 2025-2041 (2002) (Pubitemid 35458399)
10. Grana, M., Villaverde, I., Maldonado, J., Hernandez, C.: Two lattice computing approaches for the unsupervised segmentation of hyperspectral images. Neurocomputing 72, 2111-2120 (2009)
11. Ritter, G.X., Urcid, G.: A lattice matrix method for hyperspectral image unmixing. Inf. Sci. 181(10), 1787-1803 (2011)
12. Zortea, M., Plaza, A.: A quantitative and comparative analysis of different implementations of N-FINDR: A fast endmember extraction algorithm. IEEE Geosci. Remote Sens. Lett. 6, 787-791 (2009)
13. Plaza, J., Plaza, A., Perez, R., Martinez, P.: On the use of small training sets for neural network-based characterization of mixed pixels in remotely sensed hyperspectral images. Pattern Recognit. 42, 3032-3045 (2009)
14. Craig, M.D.: Minimum-volume transforms for remotely sensed data. IEEE Trans. Geosci. Remote Sens. 32, 542-552 (1994)
15. Miao, L., Qi, H.: Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization. IEEE Trans. Geosci. Remote Sens. 45, 765-777 (2007) (Pubitemid 46375748)
16. Li, J., Bioucas-Dias, J.: Minimum volume simplex analysis: a fast algorithm to unmix hyperspectral data. In: Proc. IEEE International Geoscience and Remote Sensing Symposium, vol. 3, pp. 250-253 (2008)
17. Chan, T.-H., Chi, C.-Y., Huang, Y.-M., Ma, W.-K.: A convex analysis-based minimum-volume enclosing simplex algorithm for hyperspectral unmixing. IEEE Trans. Signal Process. 57, 4418-4432 (2009)
18. Bioucas-Dias, J.: A variable splitting augmented Lagrangian approach to linear spectral unmixing. In: First IEEE GRSS Workshop on Hyperspectral Image and Signal Processing-WHISPERS'2009 (2009)
19. Berman, M., Kiiveri, H., Lagerstrom, R., Ernst, A., Dunne, R., Huntington, J.F.: ICE: a statistical approach to identifying endmembers in hyperspectral images. IEEE Trans. Geosci. Remote Sens. 42(10), 2085-2095 (2004)
20. Richards, J.A., Jia, X.: Remote Sensing Digital Image Analysis: An Introduction. Springer, Berlin (2006)
21. Green, A.A., Berman, M., Switzer, P., Craig, M.D.: A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Trans. Geosci. Remote Sens. 26, 65-74 (1988) (Pubitemid 18596008)
22. Ren, H., Chang, C.-I.: Automatic spectral target recognition in hyperspectral imagery. IEEE Trans. Aerosp. Electron. Syst. 39(4), 1232-1249 (2003)
23. Hendrix, E.M.T., Garcia, I., Plaza, J., Plaza, A.: Minimum volume simplicial enclosure for spectral unmixing. Working Paper Mansholt Graduate School MPW-50, Wageningen University (2010). http://www.wass.wur.nl/NR/ rdonlyres/7270E45EAB79-4DD5-8E2A-29556BE14A98/101189/201050def.pdf
24. Hartigan, J.A., Wong, M.A.: Algorithm as 136: A k-means clustering algorithm. J. R. Stat. Soc., Ser. C, Appl. Stat. 28, 100-108 (1979)
25. Veganzones, M.A., Hernandez, C.: On the use of a hybrid approach to contrast endmember induction algorithms. In: Lecture Notes in Artificial Intelligence, vol. 6077, pp. 69-76 (2010)
26. Clark, R.N., Swayze, G.A., Livo, K.E., Kokaly, R.F., Sutley, S.J., Dalton, J.B., McDougal, R.R., Gent, C.A.: Imaging spectroscopy: Earth and planetary remote sensing with the usgs tetracorder and expert systems. J. Geophys. Res. 108, 1-44 (2003)
27. Keshava, N., Mustard, J.F.: Spectral unmixing. IEEE Signal Process. Mag. 19(1), 44-57 (2002) (Pubitemid 34237207)
28. Bioucas-Dias, J.M., Nascimento, J.M.P.: Hyperspectral subspace identification. IEEE Trans. Geosci. Remote Sens. 46(8), 2435-2445 (2008)
29. Chang, C.-I., Du, Q.: Estimation of number of spectrally distinct signal sources in hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 42(3), 608-619 (2004)