xdem.coreg.NuthKaab

class xdem.coreg.NuthKaab(max_iterations=10, offset_threshold=0.001, bin_before_fit=True, fit_optimizer=<function curve_fit>, bin_sizes=72, bin_statistic=<function nanmedian>, subsample=500000.0, vertical_shift=True, initial_shift=None)

Nuth and Kääb (2011) coregistration, https://doi.org/10.5194/tc-5-271-2011.

Estimate horizontal and vertical translations by iterative slope/aspect alignment.

The translation parameters are stored in the self.meta[“outputs”][“affine”] keys “shift_x”, “shift_y” and “shift_z” (in georeferenced units for horizontal shifts, and unit of the elevation dataset inputs for the vertical shift), as well as in the “matrix” transform.

__init__(max_iterations=10, offset_threshold=0.001, bin_before_fit=True, fit_optimizer=<function curve_fit>, bin_sizes=72, bin_statistic=<function nanmedian>, subsample=500000.0, vertical_shift=True, initial_shift=None)

Instantiate a new Nuth and Kääb (2011) coregistration object.

Parameters:

• max_iterations (int) – Maximum allowed iterations before stopping.

• offset_threshold (float) – Residual offset threshold after which to stop the iterations (in pixels).

• bin_before_fit (bool) – Whether to bin data before fitting the coregistration function. For the Nuth and Kääb (2011) algorithm, this corresponds to bins of aspect to compute statistics on dh/tan(slope).

• fit_optimizer (Callable[..., tuple[ndarray[tuple[Any, ...], dtype[floating[Any]]], Any]]) – Optimizer to minimize the coregistration function.

• bin_sizes (int | dict[str, int | Iterable[float]]) – Size (if integer) or edges (if iterable) for binning variables later passed in .fit().

• bin_statistic (Callable[[ndarray[tuple[Any, ...], dtype[floating[Any]]]], floating[Any]]) – Statistic of central tendency (e.g., mean) to apply during the binning.

• subsample (int | float) – Subsample the input for speed-up. <1 is parsed as a fraction. >1 is a pixel count.

• vertical_shift (bool) – Whether to apply the vertical shift or not (default is True).

• initial_shift (tuple[int | float | integer[Any] | floating[Any], int | float | integer[Any] | floating[Any]] | tuple[int | float | integer[Any] | floating[Any], int | float | integer[Any] | floating[Any], int | float | integer[Any] | floating[Any]] | None) – Tuple containing x, y and z shifts (in georeferenced units). These shifts are applied before the fit() part.

Methods

__init__([max_iterations, offset_threshold, ...])	Instantiate a new Nuth and Kääb (2011) coregistration object.
apply(elev[, bias_vars, resample, ...])
centroid()	Get the centroid of the coregistration, if defined.
copy()	Return an identical copy of the class.
fit(reference_elev, to_be_aligned_elev[, ...])	Estimate the coregistration transform on the given DEMs.
fit_and_apply(reference_elev, to_be_aligned_elev)
from_matrix(matrix)	Instantiate a generic Coreg class from a transformation matrix.
from_rotations([x_rot, y_rot, z_rot, ...])	Instantiate a generic Coreg class from a X/Y/Z rotation.
from_translations([x_off, y_off, z_off])	Instantiate a generic Coreg class from a X/Y/Z translation.
info([as_str])
to_matrix()	Convert the transform to a 4x4 transformation matrix.
to_rotations([return_degrees])	Extract X/Y/Z euler rotations (extrinsic convention) from the affine transformation matrix.
to_translations()	Extract X/Y/Z translations from the affine transformation matrix.

Attributes

is_affine	Check if the transform be explained by a 3D affine transform.
is_translation
meta	Metadata dictionary of the coregistration.