Abundance maps classes


FCLS

class pysptools.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(mask=None, interpolation='none', colorMap='jet', columns=None, suffix=None)

Display the abundance maps to a IPython Notebook.

Parameters:
mask: numpy array [default None]
A binary mask, when True the selected pixel is displayed.
interpolation: string [default none]
A matplotlib interpolation method.
colorMap: string [default jet]
A matplotlib color map.
columns: int [default None]
Display all the images in one figure organized by columns.
suffix: string [default None]
Suffix to add to the title.
map(M, U, normalize=False, mask=None)

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 spectra mapping.
mask: numpy array [default None]
A binary mask, when True the selected pixel is unmixed.
Returns: numpy array
An abundance maps (m x n x q).
plot(path, mask=None, interpolation='none', colorMap='jet', columns=None, suffix=None)

Plot the abundance maps.

Parameters:
path: string
The path where to put the plot.
mask: numpy array [default None]
A binary mask, when True the selected pixel is displayed.
interpolation: string [default none]
A matplotlib interpolation method.
colorMap: string [default jet]
A matplotlib color map.
columns: int [default None]
Display all the images in one figure organized by columns.
suffix: string [default None]
Suffix to add to the file name.

NNLS

class pysptools.abundance_maps.NNLS

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

display(mask=None, interpolation='none', colorMap='jet', columns=None, suffix=None)

Display the abundance maps to a IPython Notebook.

Parameters:
mask: numpy array [default None]
A binary mask, when True the selected pixel is displayed.
interpolation: string [default none]
A matplotlib interpolation method.
colorMap: string [default jet]
A matplotlib color map.
columns: int [default None]
Display all the images in one figure organized by columns.
suffix: string [default None]
Suffix to add to the title.
map(M, U, normalize=False, mask=None)

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 spectra mapping.
mask: numpy array [default None]
A binary mask, when True the selected pixel is unmixed.
Returns: numpy array
An abundance maps (m x n x q).
plot(path, mask=None, interpolation='none', colorMap='jet', columns=None, suffix=None)

Plot the abundance maps.

Parameters:
path: string
The path where to put the plot.
mask: numpy array [default None]
A binary mask, when True the selected pixel is displayed.
interpolation: string [default none]
A matplotlib interpolation method.
colorMap: string [default jet]
A matplotlib color map.
columns: int [default None]
Display all the images in one figure organized by columns.
suffix: string [default None]
Suffix to add to the file name.

UCLS

class pysptools.abundance_maps.UCLS

Performs unconstrained least squares abundance estimation.

display(mask=None, interpolation='none', colorMap='jet', columns=None, suffix=None)

Display the abundance maps to a IPython Notebook.

Parameters:
mask: numpy array [default None]
A binary mask, when True the selected pixel is displayed.
interpolation: string [default none]
A matplotlib interpolation method.
colorMap: string [default jet]
A matplotlib color map.
columns: int [default None]
Display all the images in one figure organized by columns.
suffix: string [default None]
Suffix to add to the title.
map(M, U, normalize=False, mask=None)

Performs unconstrained least squares abundance estimation on the HSI cube M using the spectral 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 spectra mapping.
mask: numpy array [default None]
A binary mask, when True the selected pixel is unmixed.
Returns: numpy array
An abundance maps (m x n x q).
plot(path, mask=None, interpolation='none', colorMap='jet', columns=None, suffix=None)

Plot the abundance maps.

Parameters:
path: string
The path where to put the plot.
mask: numpy array [default None]
A binary mask, when True the selected pixel is displayed.
interpolation: string [default none]
A matplotlib interpolation method.
colorMap: string [default jet]
A matplotlib color map.
columns: int [default None]
Display all the images in one figure organized by columns.
suffix: string [default None]
Suffix to add to the file name.