Abstract
The Linear Mixing Model (LMM) of hyperspectral images asumes that pixel spectra are affine combinations of basic spectral signatures, called endmembers, which are the vertices of a convex polytope covering the image data. Endmember induction algorithms (EIA) extract the endmembers from the image data, obtaining a precise spectral characterization of the image. The WM algorithm assumes that a set of Affine Independent vectors can be extracted from the rows and columns of dual Lattice Autoassociative Memories (LAAM) built on the image spectra. Indeed, the set of endmembers induced by this algorithm defines a convex polytope covering the hyperspectral image data. However, the number of induced endmembers obtained by this procedure is too high for practical purposes, besides they are highly correlated. In this paper, we apply a greedy sparsification algorithm aiming to select the minimal set of endmembers that explains the data in the image. We report results on a well known benchmark image.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Keshava, N., Mustard, J.F.: Spectral unmixing. IEEE Signal Processing Magazine 19(1), 44–57 (2002)
Graña, M., Villaverde, I., Maldonado, J.O., Hernandez, C.: Two lattice computing approaches for the unsupervised segmentation of hyperspectral images. Neurocomput. 72(10-12), 2111–2120 (2009)
Graña, M., Veganzones, M.A.: An endmember-based distance for content based hyperspectral image retrieval. Pattern Recognition 49(9), 3472–3489 (2012)
Ritter, G.X., Urcid, G.: A lattice matrix method for hyperspectral image unmixing. Information Sciences 181(10), 1787–1803 (2010)
Graña, M., Veganzones, M.A.: Endmember induction by lattice associative memories and multi-objective genetic algorithms. EURASIP Journal on Advances in Signal Processing 2012, 64 (2012)
Blumensath, T., Davies, M.: Gradient pursuits. IEEE Transactions on Signal Processing 56(6), 2370–2382 (2008)
Ritter, G., Urcid, G.: Lattice algebra approach to endmember determination in hyperspectral imagery. In: Hawkes, P.W. (ed.) Advances in Imaging and Electron Physics, vol. 160, pp. 113–169. Academic Press, Burlington (2010)
Ritter, G.X., Sussner, P., Diaz-de-Leon, J.L.: Morphological associative memories. IEEE Transactions on Neural Networks 9(2), 281–293 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Marques, I., Graña, M. (2013). Greedy Sparsification WM Algorithm for Endmember Induction in Hyperspectral Images. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., de la Paz López, F., Toledo Moreo, F.J. (eds) Natural and Artificial Computation in Engineering and Medical Applications. IWINAC 2013. Lecture Notes in Computer Science, vol 7931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38622-0_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-38622-0_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38621-3
Online ISBN: 978-3-642-38622-0
eBook Packages: Computer ScienceComputer Science (R0)