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=== Endmember Induction Algorithms collection ===
=== Endmember Induction Algorithms collection ===


Here you can find separately the EIAs included in the toolbox's version 0.2 and their respective bibliographical references:
Here you can find separately the EIAs included in the toolbox's '''version 0.2''' and their respective bibliographical references:


* '''Endmember Induction Heuristic Algorithm (EIHA)'''
* '''Endmember Induction Heuristic Algorithm (EIHA)'''

Revisión actual - 12:22 6 oct 2014

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 has been developed with MATLAB 7.4 and MATLAB 8.1 (R2013a) (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

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

Acknowledgements to Prof. Gerhard Ritter from the Department of Computer and Information Science and Engineering, University of Florida (USA); 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's version 0.2 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.

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.

Data's spatial dimensionality-based launchers

Here you can find launchers for the EIA toolbox which treat data in base to their spatial dimensionality. This launchers are divided into 1D, 2D and 3D.

  • 1D Launcher: 1-spatial data is defined as a matrix where first dimension represents the spectral information and second dimension the spatial. Examples of 1-spatial data are contingency matrix where each sample (spatial dimensionality) is an N-dimensional feature vector (spectral dimensionality).
  • 2D Launcher: 2-spatial data is defined as a cube where first dimension represents the spectral information and, second and third dimensions are the spatial ones. Examples of 2-spatial data are images where each pixel (spatial dimensionality) is an N-dimensional feature vector (spectral dimensionality). For binaries or grey-scale images N==1. For RGB images N==3. For hyperspectral images N is high.
  • 3D Launcher: 3-spatial data is defined as an hypercube where first dimension represents the spectral information and, second, third and fourth dimensions are the spatial ones. Examples of 3-spatial data are hyperspectral MRI images where each voxel (spatial dimensionality) is an N-dimensional feature vector (spectral dimensionality).

Loading hyperspectral data

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