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Nonnegative matrix factorization-quadratic minimum volume (NMF-QMV) based hyperspectral unmixing
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LinaZhuang/NMF-QMV_demo
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%% The code and data herein distributed reproduces the results published in % the paper % % Lina Zhuang, Chia-Hsiang Lin, Mario A.T. Figueiredo, and Jose M. Bioucas-Dias, % "Regularization Parameter Selection in Minimum Volume Hyperspectral Unmixing", % TGRS, 2019. % % URL:http://www.lx.it.pt/~bioucas/publications.html % or https://sites.google.com/hkbu.edu.hk/linazhuang/home % %% Notes: % % % 1) demo_simulatedDataset1.m <-- This demo illustrates the NMF_QMV hyperspectral % unmixing algorithm operating in simulated Dataset1 (SCENARIO: non pure % pixels, various number of endmembers and noise level) % % 2) demo_unmixing_Rcuprite_TERRAIN.m <-- This demo illustrates the NMF_QMV % hyperspectral unmixing algorithm operating in Rcuprite image and TERRAIN image. % % 3) GenerateHSIFromTerrain.m <-- This script illustrates the procudure to generates % a simulated image from TERRAIN data. % % 4) NMF_QMV.m <-- hyperspectral unmixing method based on Nonnegative Matrix % Factorization via Quadractic Minimum Volume (NMF_QMV) % % 5) nmf_qmv_subspace.m <-- NMF-QMV working in a dimension-reduced space. % % % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % Author: Lina Zhuang and Jose M. Bioucas Dias, Oct. 2018 %
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Nonnegative matrix factorization-quadratic minimum volume (NMF-QMV) based hyperspectral unmixing
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