The three functions implemented in this library are:
- Vertex Component Analysis (VCA). Related article is J. Nascimento and J. Dias, "Vertex Component Analysis: A fast algorithm to unmix hyperspectral data", IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 4, pp. 898-910, 2005.
- Simplex Identification via Split Augmented Lagrangian (SISAL). Related article is 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.
- Sparse Unmixing via variable Splitting and Augmented Lagrangian methods(SUNSAL). Related article is Bioucas-Dias, J. M., & Figueiredo, M. A., "Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing." in Workshop in Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2010 (pp. 1-4).
Matlab versions of these codes are available in the Jose Bioucas Dias website.
This function were translated by Adrien Lagrange (view his github page).
### Usage
Ae, indice, Yp = vca(Y,R,verbose = True,snr_input = 0)
Y - matrix with dimensions L(channels) x N(pixels) each pixel is a linear mixture of R endmembers signatures Y = M x s, where
- s = gamma x alpha
- gamma is a illumination perturbation factor and
- alpha are the abundance fractions of each endmember.
R - positive integer number of endmembers in the scene
Ae - estimated mixing matrix (endmembers signatures)
indice - pixels that were chosen to be the most pure
Yp - Data matrix Y projected.
snr_input - (float) signal to noise ratio (dB)
v - [True | False]
Author: Adrien Lagrange ([email protected])
This code is a translation of a matlab code provided by Jose Nascimento ([email protected]) and Jose Bioucas Dias ([email protected]) available at http://www.lx.it.pt/~bioucas/code.htm under the GNU General Public License 2.0.
Translation of last version at 22-February-2018 (Matlab version 2.1 (7-May-2004)).
M,Up,my,sing_values = sisal(Y,p,**kwargs)
### Description
Simplex identification via split augmented Lagrangian (SISAL) estimates the vertices M={m_1,...m_p} of the (p-1)-dimensional simplex of minimum volume containing the vectors [y_1,...y_N], under the assumption that y_i belongs to a (p-1) dimensional affine set.
For details see José M. Bioucas-Dias, "A variable splitting augmented lagrangian approach to linear spectral unmixing", First IEEE GRSS Workshop on Hyperspectral Image and Signal Processing - WHISPERS, 2009. (http://arxiv.org/abs/0904.4635v1)
Y - matrix with dimension L(channels) x N(pixels). Each pixel is a linear mixture of p endmembers signatures Y = M*x + noise.
p - number of independent columns of M. Therefore, M spans a (p-1)-dimensional affine set. p is the number of endmembers.
mm_iters - Maximum number of constrained quadratic programs. Default: 80
tau - Regularization parameter in the problem
Q^* = arg min_Q -\log abs(det(Q)) + tau*|| Q*yp ||_h
subject to np.ones((1,p))*Q=mq
where mq = ones(1,N)*yp'inv(yp*yp) and ||x||_h is the "hinge" induced norm.
Default: 1
mu - Augmented Lagrange regularization parameter. Default: 1
spherize - {True, False} Applies a spherization step to data such that the spherized data spans over the same range along any axis. Default: True
tolf - Tolerance for the termination test (relative variation of f(Q)). Default: 1e-2
M0 - Initial M, dimension L x p. Defaults is given by the VCA algorithm.
verbose - {0,1,2,3}
- 0 - work silently
- 1 - display simplex volume
- 2 - display figures
- 3 - display SISAL information
- 4 - display SISAL information and figures Default: 1
M - estimated endmember signature matrix L x p
Up - isometric matrix spanning the same subspace as M, imension is L x p
my - mean value of Y
sing_values - (p-1) eigenvalues of Cy = (y-my)*(y-my)/N. The dynamic range of these eigenvalues gives an idea of the difficulty of the underlying problem
The identified affine set is given by
{z\in R^p : z=Up(:,1:p-1)*a+my, a\in R^(p-1)}
Author: Etienne Monier ([email protected])
This code is a translation of a matlab code provided by Jose Nascimento ([email protected]) and Jose Bioucas Dias ([email protected]) available at http://www.lx.it.pt/~bioucas/code.htm under the GNU General Public License 2.0.
Translation of last version at 20-April-2018 (Matlab version 2.1 (7-May-2004))
x = sunsal_v2(M,Y,**kwargs)
SUNSAL (sparse unmixing via variable splitting and augmented Lagrangian methods) algorithm implementation. Accepted constraints are:
-
- Positivity: X >= 0
-
- Addone: np.sum(X,axis=0) = np.ones(N)
For details see J. Bioucas-Dias and M. Figueiredo, “Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing”, in 2nd IEEE GRSS Workshop on Hyperspectral Image and Signal Processing-WHISPERS'2010, Raykjavik, Iceland, 2010.
M - endmember signature matrix with dimensions L(channels) x p(endmembers)
Y - matrix with dimensions L(channels) x N(pixels). Each pixel is a linear mixture of p endmembers signatures
al_iters - Minimum number of augmented Lagrangian iterations. Default: 100
lambda_p - regularization parameter. lambda is either a scalar or a vector with N components (one per column of x). Default: 0
positivity - {True, False} Enforces the positivity constraint. Default: False
addone - {True, False} Enforces the addone constraint. Default: False
tol - tolerance for the primal and dual residuals. Default: 1e-4
verbose = {True, False}
- False - work silently
- True - display iteration info Default: True
X - estimated abundance matrix of size p x N
Author: Etienne Monier ([email protected])
This code is a translation of a matlab code provided by Jose Nascimento ([email protected]) and Jose Bioucas Dias ([email protected]) available at http://www.lx.it.pt/~bioucas/code.htm under the GNU General Public License 2.0.
Translation of last version at 20-April-2018 (Matlab version 2.1 (7-May-2004))
Software translated from matlab to python by Etienne Monier ([email protected]), 2018.
Initial matlab author: Jose Bioucas-Dias, 2009
This code is distributed under the terms of the GNU General Public License 2.0.