xdem.coreg.BiasCorr

xdem.coreg.BiasCorr#

class xdem.coreg.BiasCorr(fit_or_bin='fit', fit_func='norder_polynomial', fit_optimizer=<function curve_fit>, bin_sizes=10, bin_statistic=<function nanmedian>, bin_apply_method='linear', bias_var_names=None, subsample=1.0)[source]#

Bias-correction (non-rigid alignment) simultaneously with any number and type of variables.

Variables for bias-correction can include the elevation coordinates (deramping, directional biases), terrain attributes (terrain corrections), or any other user-input variable (quality metrics, land cover).

__init__(fit_or_bin='fit', fit_func='norder_polynomial', fit_optimizer=<function curve_fit>, bin_sizes=10, bin_statistic=<function nanmedian>, bin_apply_method='linear', bias_var_names=None, subsample=1.0)[source]#

Instantiate a bias correction object.

Methods

__init__([fit_or_bin, fit_func, ...])

Instantiate a bias correction object.

apply(elev[, bias_vars, resample, ...])

Apply the estimated transform to a DEM.

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.

fit_and_apply(reference_elev, to_be_aligned_elev)

Estimate and apply the coregistration to a pair of elevation data.

residuals(reference_elev, to_be_aligned_elev)

Calculate the residual offsets (the difference) between two DEMs after applying the transformation.

Attributes

is_affine

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

meta

Metadata dictionary of the coregistration.