Abundance maps functions


FCLS

pysptools.abundance_maps.amaps.FCLS(M, U)

Performs fully constrained least squares of each pixel in M using the endmember signatures of U. Fully constrained least squares is least squares with the abundance sum-to-one constraint (ASC) and the abundance nonnegative constraint (ANC).

Parameters:
M: numpy array
2D data matrix (N x p).
U: numpy array
2D matrix of endmembers (q x p).
Returns: numpy array
An abundance maps (N x q).
References:
Daniel Heinz, Chein-I Chang, and Mark L.G. Fully Constrained Least-Squares Based Linear Unmixing. Althouse. IEEE. 1999.
Notes:
Three sources have been useful to build the algorithm:

NNLS

pysptools.abundance_maps.amaps.NNLS(M, U)

NNLS performs non-negative constrained least squares of each pixel in M using the endmember signatures of U. Non-negative constrained least squares with the abundance nonnegative constraint (ANC). Utilizes the method of Bro.

Parameters:
M: numpy array
2D data matrix (N x p).
U: numpy array
2D matrix of endmembers (q x p).
Returns: numpy array
An abundance maps (N x q).
References:
Bro R., de Jong S., Journal of Chemometrics, 1997, 11, 393-401.

UCLS

pysptools.abundance_maps.amaps.UCLS(M, U)

Performs unconstrained least squares abundance estimation.

Parameters:
M: numpy array
2D data matrix (N x p).
U: numpy array
2D matrix of endmembers (q x p).
Returns: numpy array
An abundance maps (N x q).