Diferencia entre revisiones de «Endmember Induction Algorithms»

(Endmember Induction Algorithms collection)
Línea 22: Línea 22:
  
 
* '''Endmember Induction Heuristic Algorithm (EIHA)'''
 
* '''Endmember Induction Heuristic Algorithm (EIHA)'''
** MATLAB: [[Media:EIA_EIHA.m | download (xxx MB)]]
+
** MATLAB: [[Media:EIA_EIHA.m | download (xxx KB)]]
** SCILAB: [[Media:EIA_EIHA.sci | download (xxx MB)]]
+
** SCILAB: [[Media:EIA_EIHA.sci | download (xxx KB)]]
 
     M. Grana, I. Villaverde, J. O. Maldonado, and C. Hernandez
 
     M. Grana, I. Villaverde, J. O. Maldonado, and C. Hernandez
 
     ''Two lattice computing approaches for the unsupervised segmentation of hyperspectral images''
 
     ''Two lattice computing approaches for the unsupervised segmentation of hyperspectral images''
Línea 29: Línea 29:
  
 
* '''Incremental Strong Lattice Independent Algorithm (ILSIA)'''
 
* '''Incremental Strong Lattice Independent Algorithm (ILSIA)'''
** MATLAB: [[Media:EIA_ILSIA.m | download (xxx MB)]]
+
** MATLAB: [[Media:EIA_ILSIA.m | download (7.4 KB)]]
** SCILAB: [[Media:EIA_ILSIA.sci | download (xxx MB)]]
+
** SCILAB: [[Media:EIA_ILSIA.sci | download (xxx KB)]]
 
     M. Grana, D. Chyzhyk, M. García-Sebastián, and C. Hernández
 
     M. Grana, D. Chyzhyk, M. García-Sebastián, and C. Hernández
 
     ''Lattice independent component analysis for functional magnetic resonance imaging''
 
     ''Lattice independent component analysis for functional magnetic resonance imaging''
Línea 36: Línea 36:
  
 
* '''Ritter's Strong Lattice Independent Algorithm (RLSIA)'''
 
* '''Ritter's Strong Lattice Independent Algorithm (RLSIA)'''
** MATLAB: [[Media:EIA_RLSIA.m | download (xxx MB)]]
+
** MATLAB: [[Media:EIA_RLSIA.m | download (2.4 KB)]]
** SCILAB: [[Media:EIA_RLSIA.sci | download (xxx MB)]]
+
** SCILAB: [[Media:EIA_RLSIA.sci | download (xxx KB)]]
 
     G. X. Ritter and G. Urcid
 
     G. X. Ritter and G. Urcid
 
     ''A lattice matrix method for hyperspectral image unmixing''
 
     ''A lattice matrix method for hyperspectral image unmixing''
Línea 43: Línea 43:
  
 
* '''N-FINDR'''
 
* '''N-FINDR'''
** MATLAB: [[Media:EIA_NFINDR.m | download (xxx MB)]]
+
** MATLAB: [[Media:EIA_NFINDR.m | download (xxx KB)]]
** SCILAB: [[Media:EIA_NFINDR.sci | download (xxx MB)]]
+
** SCILAB: [[Media:EIA_NFINDR.sci | download (xxx KB)]]
 
     Chang, C.-I. and Plaza, A.
 
     Chang, C.-I. and Plaza, A.
 
     ''A fast iterative algorithm for implementation of pixel purity index''
 
     ''A fast iterative algorithm for implementation of pixel purity index''
Línea 50: Línea 50:
  
 
* '''FIPPI'''
 
* '''FIPPI'''
** MATLAB: [[Media:EIA_FIPPI.m | download (xxx MB)]]
+
** MATLAB: [[Media:EIA_FIPPI.m | download (3.7 KB)]]
** SCILAB: [[Media:EIA_FIPPI.sci | download (xxx MB)]]
+
** SCILAB: [[Media:EIA_FIPPI.sci | download (3.7 KB)]]
 
     Winter, M. E.
 
     Winter, M. E.
 
     ''N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data''
 
     ''N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data''
Línea 57: Línea 57:
  
 
* '''Automatic Target Generation Process (ATGP)'''
 
* '''Automatic Target Generation Process (ATGP)'''
** MATLAB: [[Media:EIA_ATGP.m | download (xxx MB)]]
+
** MATLAB: [[Media:EIA_ATGP.m | download (3.8 KB)]]
** SCILAB: [[Media:EIA_ATGP.sci | download (xxx MB)]]
+
** SCILAB: [[Media:EIA_ATGP.sci | download (xxx KB)]]
 
     A. Plaza and C.-I. Chang
 
     A. Plaza and C.-I. Chang
 
     ''Impact of Initialization on Design of Endmember Extraction Algorithms''
 
     ''Impact of Initialization on Design of Endmember Extraction Algorithms''
Línea 64: Línea 64:
  
 
* '''Convex Cone Analysis (CCA)'''
 
* '''Convex Cone Analysis (CCA)'''
** MATLAB: [[Media:EIA_CCA.m | download (xxx MB)]]
+
** MATLAB: [[Media:EIA_CCA.m | download (3.5 KB)]]
** SCILAB: [[Media:EIA_CCA.sci | download (xxx MB)]]
+
** SCILAB: [[Media:EIA_CCA.sci | download (xxx KB)]]
 
     Ifarraguerri, A. and C.-I. Chang
 
     Ifarraguerri, A. and C.-I. Chang
 
     ''Multispectral and hyperspectral image analysis with convex cones''
 
     ''Multispectral and hyperspectral image analysis with convex cones''
Línea 80: Línea 80:
  
 
* '''Lattice Associative Memories (LAMs)'''
 
* '''Lattice Associative Memories (LAMs)'''
** MATLAB: [[Media:EIA_LAM.m | download (xxx MB)]]
+
** MATLAB: [[Media:EIA_LAM.m | download (3.0 KB)]]
** SCILAB: [[Media:EIA_LAM.sci | download (xxx MB)]]
+
** SCILAB: [[Media:EIA_LAM.sci | download (xxx KB)]]
  
 
* '''Chebyshev distance'''
 
* '''Chebyshev distance'''
** MATLAB: [[Media:EIA_CHEBYSHEV.m | download (xxx MB)]]
+
** MATLAB: [[Media:EIA_CHEBYSHEV.m | download (2.0 KB)]]
** SCILAB: [[Media:EIA_CHEBYSHEV.sci | download (xxx MB)]]
+
** SCILAB: [[Media:EIA_CHEBYSHEV.sci | download (xxx KB)]]
  
 
Some of the algorithms require as input the number of endmembers to search. If unknown, virtual dimensionality algorithms can be used:
 
Some of the algorithms require as input the number of endmembers to search. If unknown, virtual dimensionality algorithms can be used:
  
 
* '''HFC method'''
 
* '''HFC method'''
** MATLAB: [[Media:EIA_HFC.m | download (xxx MB)]]
+
** MATLAB: [[Media:EIA_HFC.m | download (3.2 KB)]]
** SCILAB: [[Media:EIA_HFC.sci | download (xxx MB)]]
+
** SCILAB: [[Media:EIA_HFC.sci | download (xxx KB)]]
 
     Chang, C.-I. and Du, Q.
 
     Chang, C.-I. and Du, Q.
 
     ''Estimation of number of spectrally distinct signal sources in hyperspectral imagery''
 
     ''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.
 
     ''Geoscience and Remote Sensing, IEEE Transactions on'', vol. 42, nº. 3, págs. 608-619, 2004.

Revisión del 13:39 8 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, 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.
   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.
   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 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.

Some of the algorithms require additional methods:

Some of the algorithms require as input the number of endmembers to search. If unknown, virtual dimensionality algorithms 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.