// ==================================================================== // This file is part of the Endmember Induction Algorithms Toolbox for MATLAB // 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 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 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. // If not, see . // ==================================================================== function [E,C] = EIA_3D(data,algorithm,varargin) //// [E,C] = EIA_3D(data,algorithm,varargin) // // Manuel Grana // Miguel Angel Veganzones // Grupo de Inteligencia Computacional (GIC), Universidad del Pais Vasco / // Euskal Herriko Unibertsitatea (UPV/EHU) // http://www.ehu.es/computationalintelligence // // Copyright (2011) Grupo de Inteligencia Computacional @ Universidad del Pais Vasco, Spain. // // Endmembers induction algorithms for 3-spatial dimensionality data. // 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). // ------------------------------------------------------------------------------ // Input: data : column data cube [nvariables x voxel_dim1 x voxel_dim2 x voxel_dim3] // algorithm : EIA to be used. It can be one of this: // {'ILSIA'(default)|'EIHA'|'WM'|'NFINDR'|'FIPPI'|'ATGP'} // varargin : options to be passed to the algorithm (see below). // // Output: E : set of induced endmembers [nvariables x p] // C : induced endmembers indexes vector [nrows x ncolumns] with {0,1} // values, where '1' indicates that the corresponding sample // has been identified as an endmember. Some of the // algorithms do not select pixels as the endmembers // and, in that case C is empty. // // Now, a description of the algorithms is offered together to the options // that can be passed as parameters. // // * ILSIA: Incremental lattice Source Induction Algorithm (ILSIA) endmembers induction algorithm. // - Bibliographical references: // [1] M. Graña, D. Chyzhyk, M. García-Sebastián, y C. Hernández, “Lattice independent component analysis for functional magnetic resonance imaging”, Information Sciences, vol. 181, nº. 10, págs. 1910-1928, May. 2011. // - Options: {'alpha'} // 'alpha': Chebyshev-best approximation tolerance threshold (>= 0). Default = 0. // // * EIHA: Endmember induction heuristic algorithm (EIHA) endmembers induction algorithm. // - Bibliographical references: // [1] 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. // - Options: {'alpha'} // 'alpha': perturbation tolerance. Default = 2. // // * WM: Prof. Ritter's WM endmembers induction algorithm. // - Bibliographical references: // [1] G. X. Ritter y G. Urcid, “A lattice matrix method for hyperspectral image unmixing”, Information Sciences, vol. In Press, Corrected Proof, Oct. 2010. // - Options: none. // // * NFINDR: N-FINDR endmembers induction algorithm. // - Bibliographical references: // [1] 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. // - Options: {'p'|'maxit'} // 'p': number of endmembers to be induced. If not provided it is calculated by HFC method with tol=10^(-5). // 'maxit': maximum number of iterations. Default = 3*p. // // * FIPPI: Fast Iterative Pixel Purity Index (FIPPI) endmembers induction algorithm. // - Bibliographical references: // [1] Chang, C.-I., “A fast iterative algorithm for implementation of pixel purity index”, Geoscience and Remote Sensing Letters, IEEE, vol. 3, nº. 1, págs. 63-67, 2006. // - Options: {'alpha'} // 'p': number of endmembers to be induced. If not provided it is calculated by HFC method with tol=10^(-5). // 'maxit': maximum number of iterations. Default = 3*p. // // * ATGP: ATGP endmembers induction algorithm. // - Bibliographical references: // [1] 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. // - Options: {'p'} // 'p': number of endmembers to be induced. If not provided it is calculated by HFC method with tol=10^(-5). // //// Arguments [lhs,rhs]=argn(0); if lhs < 1 error('Insuficient parameters'); end //// Reshape data [p m n o] = size(data); data = reshape(data,p,m*n*o); //// Algorithms [lhs,rhs]=argn(0); select lower(algorithm) case 'eiha' then disp('EIHA algorithm'); if rhs > 0 alpha = []; for i=1:2:rhs if strcmpi(varargin(i),'alpha') alpha = varargin(i+1); break; end end [E,C] = EIA_EIHA(data,alpha); else [E,C] = EIA_EIHA(data); end case 'wm' then disp('WM algorithm'); [E] = EIA_WM(data); C = []; case 'nfindr' then disp('N-FINDR algorithm'); if rhs > 0 p = []; maxit = []; for i=1:2:rhs if strcmpi(varargin(i),'p') p = varargin(i+1); elseif strcmpi(varargin(i),'maxit') maxit = varargin(i+1); end end [E,C] = EIA_NFINDR(data,p,maxit); else [E,C] = EIA_NFINDR(data); end case 'fippi' then disp('FIPPI algorithm'); if rhs > 0 p = []; maxit = []; for i=1:2:rhs if strcmpi(varargin(i),'p') p = varargin(i+1); elseif strcmpi(varargin(i),'maxit') maxit = varargin(i+1); end end [E,C] = EIA_FIPPI(data,p,maxit); else [E,C] = EIA_FIPPI(data); end case 'atgp' then disp('ATGP algorithm'); if rhs > 0 p = []; maxit = []; for i=1:2:rhs if strcmpi(varargin(i),'p') p = varargin(i+1); elseif strcmpi(varargin(i),'maxit') maxit = varargin(i+1); end end [E,C] = EIA_ATGP(data,p,maxit); else [E,C] = EIA_ATGP(data); end else // ILSIA disp('ILSIA algorithm'); if rhs > 0 alpha = []; for i=1:2:rhs if strcmpi(varargin(i),'alpha') alpha = varargin(i+1); break; end end [E,C] = EIA_ILSIA(data,alpha); else [E,C] = EIA_ILSIA(data);www.e end end //// Reshape C if size(C) > 0 C = reshape(c,p,m,n,o); end endfunction