Abstract
Linear spectral mixture analysis, or linear unmixing, has proven to be a useful tool in hyperspectral remote sensing applications. It aims at estimating the number of reference substances, also called endmembers, their spectral signature and abundance fractions, using only the observed data (mixed pixels). This paper presents new method that performs unsupervised endmember extraction from hyperspectral data. The algorithm exploits a simple geometric fact: endmembers are vertices of a simplex. The algorithm complexity, measured in floating points operations, is O(n), where n is the sample size. The effectiveness of the proposed scheme is illustrated using simulated data.
This work was supported by the Fundação para a ciência e Tecnologia, under the project POSI/34071/CPS/2000.
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References
Hapke, B.: Theory of Reflectance and Emmittance Spectroscopy. Cambridge Univ. Press, Cambridge (1993)
Clark, R.N., Roush, T.L.: Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications. J. of Geophysical Research 89(B7), 6329–6340 (1984)
Lillesand, T., Kiefer, R.: Remote Sensing and Image Interpretation, 3rd edn. John Wiley & Sons, Inc., Chichester (1994)
Vane, G., Green, R., Chrien, T., Enmark, H., Hansen, E., Porter, W.: The airborne visible/infrared imaging spectrometer (aviris). Remote Sens. Environ. 44, 127–143 (1993)
Smith, M.O., Adams, J.B., Sabol, D.E.: Spectral mixture analysis-New strategies for the analysis of multispectral data. In: Hill, J., Mergier, J. (eds.) Brussels and Luxemburg, Belgium: Image Spectrometry-A Tool for Environmental Observations (1994)
Gillespie, A.R., Smith, M.O., Adams, J.B., Willis, S.C., Fisher, A.F., Sabol, D.E.: Interpretation of residual images: Spectral mixture analysis of aviris images, owens valley, california. In: Green, R.O. (ed.) Proc 2nd AVIRIS Workshop, Jpl Publ., June 1990, vol. 90-54, pp. 243–270 (1990)
Settle, J.J.: On the relationship between spectral unmixing and subspace projection. IEEE Trans. Geosci. Remote Sensing 34, 1045–1046 (1996)
Hu, Y.H., Lee, H.B., Scarpace, F.L.: Optimal linear spectral unmixing. IEEE Trans. Geosci. Remote Sensing 37, 639–644 (1999)
Petrou, M., Foschi, P.G.: Confidence in linear spectral unmixing of single pixels. IEEE Trans. Geosci. Remote Sensing 37, 624–626 (1999)
Borel, C.C., Gerstl, S.A.: Nonlinear spectral mixing models for vegetative and soils surface. Remote Sensing of the Environment 47(2), 403–416 (1994)
Manolakis, D., Siracusa, C., Shaw, G.: Hyperspectral subpixel target detection using linear mixing model. IEEE Trans. Geosci. Remote Sensing 39(7), 1392–1409 (2001)
Ifarraguerri, A., Chang, C.-I.: Multispectral and hyperspectral image analysis with convex cones. IEEE Trans. Geosci. Remote Sensing 37(2), 756–770 (1999)
Boardman, J.: Automating spectral unmixing of aviris data using convex geometry concepts. In: Summaries of the Fourth Annual JPL Airborne Geoscience Workshop, JPL Pub. 93-26, AVIRIS Workshop, vol. 1, pp. 11–14 (1993)
Craig, M.D.: Minimum-volume transforms for remotely sensed data. IEEE Trans. Geosci. Remote Sensing 32, 99–109 (1994)
Theiler, J., Lavenier, D., Harvey, N., Perkins, S., Szymanski, J.: Using blocks of skewers for faster computation of pixel purity index. In: Proc. SPIE Int. Conf. Optical Science and Technology (2000)
Lay, S.R.: Convex Sets and Their Applications. John Wiley & Sons, Inc., New York (1982)
Staenz, K., Szeredi, T., Schwarz, J.: Isdas - a system for processing/analysing hyperspectral data. Can. J. of Remote Sensing 24, 99–113 (1998)
Winter, M.E.: N-findr: an algorithm for fast autonomous spectral end-member determination in hyperspectral data. In: Proc. SPIE Imaging Spectrometry V, pp. 266–275 (1999)
Roberts, D., Gardener, M., Regelbrugge, J., Pedreros, D., Ustin, S.: Mapping the distribution of wildfire fuels using aviris in the santa monica mountains. In: Summaries of the VIII JPL Airborne Earth Science Workshop (1998)
Bateson, C., Asner, G., Wessman, C.: Endmember bundles: A new approach to incorporating endmember variability into spectral mixture analysis. IEEE Trans. Geosci. Remote Sensing 38, 1083–1094 (2000)
Plaza, A., Martinez, P., Perez, R., Plaza, J.: Spatial/spectral endmember extraction by multidimensional morphological operations. IEEE Trans. Geosci. Remote Sensing 40(9), 2025–2041 (2002)
Bayliss, J., Gualtieri, J.A., Cromp, R.: Analysing hyperspectral data with independent component analysis. In: Proc. SPIE, vol. 3240, pp. 133–143 (1997)
Chen, C., Zhang, X.: Independent component analysis for remote sensing study. In: EOS/SPIE Symp. Remote Sensing Conference on Image and Signal Processing for Remote Sensing V, September 20-24, vol. 3871, pp. 150–158 (1999)
Tu, T.M.: Unsupervised signature extraction and separation in hyperspectral images: A noise-adjusted fast independent component analysis approach. Opt. Eng./SPIE 39(4), 897–906 (2000)
Chiang, S.-S., Chang, C.-I., Ginsberg, I.W.: Unsupervised hyperspectral image analysis using independent component analysis. In: Proc. IEEE Int. Geoscience and Remote Sensing Symp, July 24-28 (2000)
Nascimento, J.M.P., Dias, J.M.B.: Does independent component analysis play a role in unmixing hyperspectral data? In: IbPRIA 2003 (September 2003) (to be published)
Clark, R.N., Swayze, G.A., Gallagher, A., King, T.V., Calvin, W.M.: The u.s. geological survey digital spectral library: Version 1: 0.2 to 3.0 μm, U. S. Geological Survey. Open File Report 93-592 (1993)
Attias, H.: Independent factor analysis. Neural Computation 11(4), 803–851 (1999)
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Nascimento, J.M.P., Dias, J.M.B. (2003). Vertex Component Analysis: A~Fast Algorithm to Extract Endmembers Spectra from Hyperspectral Data. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_73
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DOI: https://doi.org/10.1007/978-3-540-44871-6_73
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