xdem.coreg.ICP#

class xdem.coreg.ICP(max_iterations=100, tolerance=0.05, rejection_scale=2.5, num_levels=6, subsample=500000.0)[source]#

Iterative Closest Point DEM coregistration. Based on 3D registration of Besl and McKay (1992), https://doi.org/10.1117/12.57955.

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

__init__(max_iterations=100, tolerance=0.05, rejection_scale=2.5, num_levels=6, subsample=500000.0)[source]#

Instantiate an ICP coregistration object.

Parameters:

max_iterations (int) – The maximum allowed iterations before stopping.
tolerance (float) – The residual change threshold after which to stop the iterations.
rejection_scale (float) – The threshold (std * rejection_scale) to consider points as outliers.
num_levels (int) – Number of octree levels to consider. A higher number is faster but may be more inaccurate.
subsample (float | int) – Subsample the input for speed-up. <1 is parsed as a fraction. >1 is a pixel count.

Methods

`__init__`([max_iterations, tolerance, ...])	Instantiate an ICP coregistration object.
`apply`(elev[, bias_vars, resample, ...])	Apply the estimated transform to a DEM.
`centroid`()	Get the centroid of the coregistration, if defined.
`copy`()	Return an identical copy of the class.
`error`(reference_elev, to_be_aligned_elev[, ...])	Calculate the error of a coregistration approach.
`fit`(reference_elev, to_be_aligned_elev[, ...])	Estimate the coregistration transform on the given DEMs.
`from_matrix`(matrix)	Instantiate a generic Coreg class from a transformation matrix.
`from_translation`([x_off, y_off, z_off])	Instantiate a generic Coreg class from a X/Y/Z translation.
`residuals`(reference_elev, to_be_aligned_elev)	Calculate the residual offsets (the difference) between two DEMs after applying the transformation.
`to_matrix`()	Convert the transform to a 4x4 transformation matrix.

Attributes

is_affine

Check if the transform be explained by a 3D affine transform.

xdem.coreg.ICP