xdem.coreg.LZD

class xdem.coreg.LZD(only_translation=False, fit_minimizer=<function least_squares>, fit_loss_func='linear', max_iterations=200, tolerance=0.01, subsample=500000.0)

Least Z-difference coregistration.

See Rosenholm and Torlegård (1988), https://www.asprs.org/wp-content/uploads/pers/1988journal/oct/1988_oct_1385-1389.pdf.

Estimates a rigid transform (rotation + translation) between two elevation datasets.

The estimated transform is stored in the self.meta[“outputs”][“affine”] key “matrix”, with rotation centered on the coordinates in the key “centroid”. The translation parameters are also stored individually in the 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).

__init__(only_translation=False, fit_minimizer=<function least_squares>, fit_loss_func='linear', max_iterations=200, tolerance=0.01, subsample=500000.0)

Instantiate an LZD coregistration object.

Parameters:

• only_translation (bool) – Whether to solve only for a translation, otherwise solves for both translation and rotation as default.

• fit_minimizer (Callable[..., tuple[ndarray[tuple[Any, ...], dtype[floating[Any]]], Any]]) – Minimizer for the coregistration function.

• fit_loss_func (Callable[[ndarray[tuple[Any, ...], dtype[floating[Any]]]], floating[Any]] | str) – Loss function for the minimization of residuals.

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

• tolerance (float) – Residual change threshold after which to stop the iterations.

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

Methods

__init__([only_translation, fit_minimizer, ...])	Instantiate an LZD 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.