Endmember Induction Algorithms

Revisión del 12:38 27 abr 2011 de Maveganzones (Discusión | contribuciones) (Endmember Induction Algorithms collection)

Endmember Induction Algorithms toolbox

Download the latest Endmember Induction Algorithms (EIAs) toolbox and the documentation here:

This software is distributed under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

The here available Endmember Induction Algorithms (EIAs) toolbox have been developed with MATLAB 7.4 (licensed copy is needed to use it) and SCILAB 5.2 (open source and freely distributed).

If you are using the Endmember Induction Algorithms (EIAs) toolbox for your scientific research, please reference it as follows:

   Endmember Induction Algorithms (EIAs) toolbox.
   Grupo de Inteligencia Computacional, Universidad del País Vasco / Euskal Herriko Unibertsitatea (UPV/EHU), Spain. 

Copyright 2010 Grupo Inteligencia Computacional, Universidad del País Vasco / Euskal Herriko Unibertsitatea (UPV/EHU).

Acknowledgements to Prof. Antonio Plaza from the Department of Technology of Computers and Communications, University of Extremadura (Spain), and coordinator of the Hyper-I-Net project; and to Prof. Chein-I Chang from the Remote Sensing Signal and Image Processing Laboratory, University of Maryland (USA).

Endmember Induction Algorithms collection

Here you can find separately the EIAs included in the toolbox and their respective bibliographical references:

   M. Grana, I. Villaverde, J. O. Maldonado, and C. Hernandez
   Two lattice computing approaches for the unsupervised segmentation of hyperspectral images
   Neurocomput., vol. 72, nº. 10-12, págs. 2111-2120, 2009.
   M. Grana, D. Chyzhyk, M. García-Sebastián, and C. Hernández
   Lattice independent component analysis for functional magnetic resonance imaging
   Information Sciences, vol. 181, pág. 1910–1928, May. 2011.
   G. X. Ritter and G. Urcid
   A lattice matrix method for hyperspectral image unmixing
   Information Sciences, vol. In Press, Corrected Proof, Oct. 2010.
   Winter, M. E.
   N-FINDR: an algorithm for fast autonomous spectral endmember determination in hyperspectral data
   presented at the Imaging Spectrometry V, Denver, CO, USA, 1999, vol. 3753, págs. 266-275.
   Chang, C.-I. and Plaza, A.
   A fast iterative algorithm for implementation of pixel purity index
   Geoscience and Remote Sensing Letters, IEEE, vol. 3, nº. 1, págs. 63-67, 2006.
   A. Plaza and C.-I. Chang
   Impact of Initialization on Design of Endmember Extraction Algorithms
   Geoscience and Remote Sensing, IEEE Transactions on, vol. 44, nº. 11, págs. 3397-3407, 2006.
   Ifarraguerri, A. and C.-I. Chang
   Multispectral and hyperspectral image analysis with convex cones
   Geoscience and Remote Sensing, IEEE Transactions on, vol. 37, nº. 2, págs. 756-770, 1999.
   Nascimento, J. M. P. and Dias, J. M. B.
   Vertex component analysis: a fast algorithm to unmix hyperspectral data
   Geoscience and Remote Sensing, IEEE Transactions on, vol. 43, nº. 4, págs. 898-910, 2005.

Additional functions

Some of the algorithms require additional methods:

Some of the algorithms require as input the number of endmembers to search. If unknown, HFC virtual dimensionality algorithm can be used:

   Chang, C.-I. and Du, Q.
   Estimation of number of spectrally distinct signal sources in hyperspectral imagery
   Geoscience and Remote Sensing, IEEE Transactions on, vol. 42, nº. 3, págs. 608-619, 2004.

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