sisal_demo

所属分类:数学计算
开发工具:matlab
文件大小:472KB
下载次数:183
上传日期:2012-07-06 17:31:31
上 传 者kikgy
说明:  高光谱解混合方法,变分增强拉格朗日线性解混合方法。
(A Variable splitting Augmented Lagragian Approach to Linear Spectral Unmixing)

文件列表:
sisal_demo\dataProj.m (4825, 2009-05-07)
sisal_demo\demo_sisal.m (13747, 2009-12-29)
sisal_demo\demo_sisal_large_n.m (13388, 2009-12-29)
sisal_demo\demo_sisal_large_n_p.m (13409, 2009-12-29)
sisal_demo\demo_sisal_noise_comparison.m (14604, 2009-12-29)
sisal_demo\demo_sisal_outliers_comparison.m (14616, 2009-12-29)
sisal_demo\dirichlet.m (1503, 2004-10-19)
sisal_demo\estNoise.m (3215, 2008-05-15)
sisal_demo\hinge.m (82, 2009-04-14)
sisal_demo\hysime.m (2892, 2009-02-25)
sisal_demo\mvsa.m (15335, 2009-05-18)
sisal_demo\sisal.m (15318, 2009-12-29)
sisal_demo\soft_neg.m (185, 2009-04-14)
sisal_demo\spectMixGen.m (11937, 2009-03-03)
sisal_demo\USGS_1995_Library.mat (438954, 2007-03-12)
sisal_demo\VCA.m (6901, 2008-04-18)
sisal_demo (0, 2009-12-29)

% This package contains a matlab implementation of the SISAL % algorithm [1]. % % %-------------------------------------------------------------------------- % Files included %------------------------------------------------------------------------- % % sisal.m -> SISAL algorithm [1] % mvsa.m -> MVSA algorithm [2] % vca.m -> VCA algorithm [3] % estNoise -> Noise estimation algorithm. See [4] % dataProj -> Project data algorithm % hysime -> Hysime algorithm [5] % USGS_1995_Library.mat -> USGS spectral library % % DEMOS: % % demo_sisal.m -> basic demo % demo_sisal_large_n.m -> large data set % demo_sisal_large_n_p.m -> large data set and 15 endmembers % demo_sisal_noise_comparison.m -> illustration of sisal robustness to % noise % demo_sisal_noise_comparison.m -> illustration of sisal robustness to % outliers % %-------------------------------------------------------------------------- % How to run %------------------------------------------------------------------------- % % Simply download the complete package to a directory and run the demos % % % SISAL: Simplex identification via split augmented Lagrangian % % [1] J. Bioucas-Dias, "A variable splitting augmented Lagrangian approach % to linear spectral unmixing", in First IEEE GRSS Workshop on % Hyperspectral Image and Signal Processing-WHISPERS'2009, Grenoble, % France, 2009. Available at http://arxiv.org/abs/0904.4635v % % MVSA: Minimum volume simple analysis % % [2] Jun Li and José M. Bioucas-Dias % "Minimum volume simplex analysis: A fast algorithm to unmix hyperspectral data" % in IEEE International Geoscience and Remote sensing Symposium % IGARSS’2008, Boston, USA, 2008. % % VCA: Vertex component analysis % % [3] J. Nascimento and J. Bioucas-Dias, "Vertex component analysis", % IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 4, % pp. 8***-910, 2005. % % [4] J. Bioucas- Dias and J. Nascimento, "Hyperspectral subspace % identification", IEEE Transactions on Geoscience and Remote Sensing, % vol. 46, no. 8, pp. 2435-2445, 2008 % % % NOTE: VCA (Vertex Component Analysis) is used to initialize SISAL. However, % VCA is a pure-pixel based algorithm and thus it is not suited to % the data sets herein considered. Nevertheless, we plot VCA results, % to highlight the advantage of non-pure-pixel based algorithms over the % the pure-pixel based ones. % % % Author: Jose M. Bioucas-Dias (December, 2009) %

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