1.Introduction
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The Endmember Induction Algorithms toolbox (EIA) is a set of endmember induction methods and other related utilities for its use from SCILAB and MATLAB. This file concerns to version 0.3 of the toolbox.

The Endmember Induction Algorithms (EIAs) available in the toolbox have been developed with MATLAB versions 7.4 and 8.1 (R2013b) (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/computationalintelligence/index.php/Endmember_Induction_Algorithms

2.Copyright
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Copyright (C) Grupo de Inteligencia Computacional, Universidad del País Vasco (UPV/EHU), Spain released under the terms of the GNU General Public License.
Endmember Induction Algorithms toolbox (EIA) is free software: you can redistribute it and/or modify it 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.
Endmember Induction Algorithms toolbox (EIA) is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with Endmember Induction Algorithms toolbox (EIA). If not, see <http://www.gnu.org/licenses/>.

3.Install
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Endmember Induction Algorithms (EIA) toolbox works over Matlab and Scilab, which are numerical computation software. Before using EIA toolbox, you'll need to install matlab or Scilab in your computer. Matlab is a comercial software and a license is required. Scilab is free and easy to install, and runs in Linux, Windows and MacOsX platforms. It is convenient to install the latest stable version available for your platform. At this moment, latest versions are Matlab R2011a and Scilab 5.3.0.

3.1 EIA Toolbox install in Matlab
No installation is required to use EIA toolbox in Matlab. It is recommended to add the EIA toolbox path to the Matlab search path so you don't have to launch EIA toolbox methods from the EIA toolbox directory. To add the path just do “File > Set path”, and the 'Set path' window will appear. Press the 'Add with Subfolders' button and select the EIA toolbox directory. Accept and the EIA toolbox path will be available from now on.

3.1 EIA Toolbox install in Scilab
No installation is required to use EIA toolbox in Matlab. However, EIA functions must be loaded before you can use them. To load a Scilab function just type:
   >> exec 'pathtofile';
If the function file is correct, Scilab will load it in memory and you could use it. Note that some methods call to other functions, so these functions must be loaded first.

4.Contents
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4.1 Directories and files
   - Endmember Induction Algorithm Toolbox
      - Code
          - MATLAB: Matlab implementation
          - SCILAB: Scilab implementation
      - Documentation
          - Manual: User's manual
          - Other: Other docs
      * CHANGELOG: list of changes for the different versions of the toolbox
      * COPYING: GNU General Public License
      * README: this file

4.2 List of currently available methods and utilities together with their respective bibliographies.

   4.2.1 Endmember Induction Algorithms
      - Endmember Induction Heuristic Algorithm (EIHA):
           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.
      - Incremental Strong Lattice Independent Algorithm (ILSIA):
           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.
      - Prof. Ritter's WM Algorithm (WM):
           G. X. Ritter and G. Urcid.
           A lattice matrix method for hyperspectral image unmixing.
           Information Sciences, vol. In Press, Corrected Proof, Oct. 2010.
      - N-FINDR:
           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.
      - Fast Iterative Pixel purity index (FIPPI):
           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.
      - Automatic Target Generation Process (ATGP):
           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.
      - Convex Cone Analysis (CCA):
           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.

   4.2.2 Launchers
      - EIA_1D
      - EIA_2D
      - EIA_3D

   4.2.3 Utilities
      - HFC method:
           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.
      - Lattice Associative Memories (LAMs):
           (1) Ritter, G. X., Diaz-de-Leon, J. L. and Sussner, P.
               Morphological bidirectional associative memories.
               Neural Networks, vol. 12, nº. 6, págs. 851-867, Jul. 1999.
           (2) Ritter, G. X., Sussner, P. and Diaz-de-Leon, J. L.
               Morphological associative memories.
               Neural Networks, IEEE Transactions on, vol. 9, nº. 2, págs. 281-293, 1998.
      - Chebyshev distance.

5.Contact
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 -- Miguel Angel Veganzones <miguelangel.veganzones@ehu.es>
 -- Ion Marqués <ion.marques@ehu.es>
 -- Prof. Manuel Graña <manuel.grana@ehu.es>
 -- Grupo de Inteligencia Computacional <http://www.ehu.es/computationalintelligence>
 -- Universidad del País Vasco (UPV/EHU), Spain
 -- Wed, 29 Aug 2012
