Diferencia entre revisiones de «Endmember Induction Algorithms»

De Grupo de Inteligencia Computacional (GIC)
Sin resumen de edición
Línea 33: Línea 33:
     M. Grana, D. Chyzhyk, M. García-Sebastián, y C. Hernández
     M. Grana, D. Chyzhyk, M. García-Sebastián, y C. Hernández
     ''Lattice independent component analysis for functional magnetic resonance imaging''
     ''Lattice independent component analysis for functional magnetic resonance imaging''
     ''Information Sciences: an International Journal'', vol. 181, pág. 1910–1928, May. 2011.
     ''Information Sciences'', vol. 181, pág. 1910–1928, May. 2011.
 
* '''Ritter's Strong Lattice Independent Algorithm (RLSIA)'''
** MATLAB: [[Media:EIA_RLSIA.m | download (xxx MB)]]
** SCILAB: [[Media:EIA_RLSIA.sci | download (xxx MB)]]
    ''A lattice matrix method for hyperspectral image unmixing''
    ''Information Sciences'', vol. In Press, Corrected Proof, Oct. 2010.
 
* '''N-FINDR'''
** MATLAB: [[Media:EIA_NFINDR.m | download (xxx MB)]]
** SCILAB: [[Media:EIA_NFINDR.sci | download (xxx MB)]]
    Winter, M. E.
    ''N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data''
    presented at the ''Imaging Spectrometry V'', Denver, CO, USA, 1999, vol. 3753, págs. 266-275.
 
* '''Automatic Target Generation Process (ATGP)'''
** MATLAB: [[Media:EIA_ATGP.m | download (xxx MB)]]
** SCILAB: [[Media:EIA_ATGP.sci | download (xxx MB)]]
    A. Plaza y 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.
 
* '''Convex Cone Analysis (CCA)'''
** MATLAB: [[Media:EIA_CCA.m | download (xxx MB)]]
** SCILAB: [[Media:EIA_CCA.sci | download (xxx MB)]]
    Ifarraguerri, A. y 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.
 
* '''Vertex Component Analysis (VCA)'''
** MATLAB: [[Media:EIA_VCA.m | download (xxx MB)]]
** SCILAB: [[Media:EIA_VCA.sci | download (xxx MB)]]
    Nascimento, J. M. P. y 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.
 
Some of the algorithms require additional methods:
 
* '''Lattice Associative Memories (LAMs)'''
** MATLAB: [[Media:EIA_LAM.m | download (xxx MB)]]
** SCILAB: [[Media:EIA_LAM.sci | download (xxx MB)]]
 
* '''Chebyshev distance'''
** MATLAB: [[Media:EIA_CHEBYSHEV.m | download (xxx MB)]]
** SCILAB: [[Media:EIA_CHEBYSHEV.sci | download (xxx MB)]]
 
Some of the algorithms requires as input the number of endmembers to search. If unknown, virtual dimensionality algorithms can be used:
 
* '''HFC method'''
** MATLAB: [[Media:EIA_HFC.m | download (xxx MB)]]
** SCILAB: [[Media:EIA_HFC.sci | download (xxx MB)]]
    Chang, C.-I. y 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.

Revisión del 16:00 5 abr 2011

Download the latest Endmember Induction Algorithms (EIAs) toolbox 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. 
   http://www.ehu.es/ccwintco/index.php/Endmember_Induction_Algorithms_%28EIAs%29_for_MATLAB_and_SCILAB

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 the EIAs included in the collection and their respective bibliographical references: 

   M. Grana, I. Villaverde, J. O. Maldonado, y 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, y C. Hernández
   Lattice independent component analysis for functional magnetic resonance imaging
   Information Sciences, vol. 181, pág. 1910–1928, May. 2011.

   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 end-member determination in hyperspectral data
   presented at the Imaging Spectrometry V, Denver, CO, USA, 1999, vol. 3753, págs. 266-275.

   A. Plaza y 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. y 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. y 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.

Some of the algorithms require additional methods:

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

   Chang, C.-I. y 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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