runATGP.zip_ATGP_MATLAB ATGP_endmember extraction_hyperspectra

% // ==================================================================== % // 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 <http://www.gnu.org/licenses/>. % // ==================================================================== % %% [E,C] = EIA_ATGP(data,p) % % Manuel Grana <manuel.grana[AT]ehu.es> % Miguel Angel Veganzones <miguelangel.veganzones[AT]ehu.es> % 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. % Copyright (2007) GRNPS group @ University of Extremadura, Spain. % % ATGP endmembers induction algorithm. % ------------------------------------------------------------------------------ % Input: data : column data matrix [nvariables x nsamples] % p : number of endmembers to be induced. If not provided it is calculated by HFC method with tol=10^(-5). % % Output: E : set of induced endmembers [nvariables x p] % C : induced endmembers indexes vector [nsamples] with {0,1} values, where '1' indicates that the corresponding sample has been identified as an endmember. % % 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. function [E,C] = EIA_ATGP(data,p) %% Parameters if (nargin < 1) error('Insufficient parameters'); end if (nargin < 2 || p <= 0) p = EIA_HFC(data,10^(-5)); end %% data size [nvariables,nsamples] = size(data); %% Algorithm initialization % the sample with max energy is selected as the initial endmember max = -1; idx = 0; for i = 1:nsamples r = data(:,i); val = r'*r; if val > max max = val; idx = i; end end e_0 = data(:,idx); % Initialization of the set of endmembers and the endmembers index vector E = zeros(nvariables,p); E(:,1) = e_0; C = zeros(1,nsamples); C(idx) = 1; % Generate the identity matrix. I = eye(nvariables); %% Algorithm for i = 1:p-1 UC = E(:,1:i); % Calculate the orthogonal projection with respect to the pixels at present chosen. % This part can be replaced with any other distance PU = I-UC*pinv(UC'*UC)*UC'; max = -1; idx = 0; % Calculate the most different pixel from the already selected ones according to % the orthogonal projection (or any other distance selected) for j = 1:nsamples r = data(:,j); result = PU*r; val = result'*result; if (val > max) max = val; idx = j; end end % The next chosen pixel is the most different from the already chosen ones e_i = data(:,idx); E(:,i+1) = e_i; C(idx) = 1; end