Endmember Induction Algorithms
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.
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. 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.
- 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.
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 endmember 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.