Table Of Contents

Abundance maps classes


FCLS

class abundance_maps.FCLS

Performs fully constrained least squares. Fully constrained least squares is least squares with the abundance sum-to-one constraint (ASC) and the abundance nonnegative constraint (ANC).

display(colorMap='jet', suffix=None)

Plot the abundance maps on a IPython Notebook.

Parameters:
colorMap: string [default jet]
A matplotlib color map.
suffix: string [default None]
Suffix to add to the title.
map(M, U, normalize=False)

Performs fully constrained least squares of each pixel in M using the endmember signatures of U.

Parameters:
M: numpy array
A HSI cube (m x n x p).
U: numpy array
A spectral library of endmembers (q x p).
normalize: boolean [default False]
If True, M and U are normalized before doing the signals mapping.
Returns: numpy array
An abundance maps (m x n x q).
plot(path, colorMap='jet', suffix=None)

Plot the abundance maps.

Parameters:
path: string
The path where to put the plot.
colorMap: string [default jet]
A matplotlib color map.
suffix: string [default None]
Suffix to add to the file name.

NNLS

class abundance_maps.NNLS

NNLS performs non-negative constrained least squares with the abundance nonnegative constraint (ANC). Utilizes the method of Bro.

display(colorMap='jet', suffix=None)

Plot the abundance maps on a IPython Notebook.

Parameters:
colorMap: string [default jet]
A matplotlib color map.
suffix: string [default None]
Suffix to add to the title.
map(M, U, normalize=False)

NNLS performs non-negative constrained least squares of each pixel in M using the endmember signatures of U.

Parameters:
M: numpy array
A HSI cube (m x n x p).
U: numpy array
A spectral library of endmembers (q x p).
normalize: boolean [default False]
If True, M and U are normalized before doing the signals mapping.
Returns: numpy array
An abundance maps (m x n x q).
plot(path, colorMap='jet', suffix=None)

Plot the abundance maps.

Parameters:
path: string
The path where to put the plot.
colorMap: string [default jet]
A matplotlib color map.
suffix: string [default None]
Suffix to add to the file name.

UCLS

class abundance_maps.UCLS

Performs unconstrained least squares abundance estimation.

display(colorMap='jet', suffix=None)

Plot the abundance maps on a IPython Notebook.

Parameters:
colorMap: string [default jet]
A matplotlib color map.
suffix: string [default None]
Suffix to add to the title.
map(M, U, normalize=False)

Performs unconstrained least squares abundance estimation on the HSI cube M using the signals library U.

Parameters:
M: numpy array
A HSI cube (m x n x p).
U: numpy array
A spectral library of endmembers (q x p).
normalize: boolean [default False]
If True, M and U are normalized before doing the signals mapping.
Returns: numpy array
An abundance maps (m x n x q).
plot(path, colorMap='jet', suffix=None)

Plot the abundance maps.

Parameters:
path: string
The path where to put the plot.
colorMap: string [default jet]
A matplotlib color map.
suffix: string [default None]
Suffix to add to the file name.