demcompare.coregistration.nuth_kaab_internal

This module contains functions associated to the Nuth and Kaab universal co-registration (Correcting elevation data for glacier change detection 2011).

Based on the work of geoutils project https://github.com/GeoUtils/geoutils/blob/master/geoutils/dem_coregistration.py Authors : Amaury Dehecq, Andrew Tedstone Date : June 2015 License : MIT

Module Contents

Classes

NuthKaabInternal

NuthKaab class, allows to perform a Nuth & Kaab coregistration

class demcompare.coregistration.nuth_kaab_internal.NuthKaabInternal(cfg: demcompare.internal_typing.ConfigType = None)

Bases: demcompare.coregistration.coregistration_template.CoregistrationTemplate

NuthKaab class, allows to perform a Nuth & Kaab coregistration from authors above and adapted in demcompare

DEFAULT_ITERATIONS = 6

method_name = 'nuth_kaab_internal'

fill_conf_and_schema(cfg: demcompare.internal_typing.ConfigType = None) → demcompare.internal_typing.ConfigType

Add default values to the dictionary if there are missing elements and define the configuration schema

Parameters:

cfg (ConfigType) – coregistration configuration

Return cfg:

coregistration configuration updated

Return type:

ConfigType

_coregister_dems_algorithm(sec: xarray.Dataset, ref: xarray.Dataset) → Tuple[demcompare.transformation.Transformation, xarray.Dataset, xarray.Dataset]

Coregister_dems_algorithm, computes coregistration transformation and reprojected coregistered DEMs with Nuth et kaab algorithm Plots might be saved if save_optional_outputs is set.

Parameters:

• sec (xarray Dataset) –

  sec xr.DataSet containing :

  • image : 2D (row, col) xr.DataArray float32

  • georef_transform: 1D (trans_len) xr.DataArray

  • classification_layer_masks : 3D (row, col, indicator) xr.DataArray

• ref (xarray Dataset) –

  ref xr.DataSet containing :

  • image : 2D (row, col) xr.DataArray float32

  • georef_transform: 1D (trans_len) xr.DataArray

  • classification_layer_masks : 3D (row, col, indicator) xr.DataArray

Returns:

transformation, reproj_coreg_sec xr.DataSet,

reproj_coreg_ref xr.DataSet. The xr.Datasets containing :

• image : 2D (row, col) xr.DataArray float32

• georef_transform: 1D (trans_len) xr.DataArray

• classification_layer_masks : 3D (row, col, indicator) xr.DataArray

Return type:

Tuple[Transformation, xr.Dataset, xr.Dataset]

static interpolate_dem_on_grid(interp_dem: numpy.ndarray, xgrid: numpy.ndarray, ygrid: numpy.ndarray) → Tuple[scipy.interpolate.RectBivariateSpline, scipy.interpolate.RectBivariateSpline]

interpolate_dem_on_grid, returns the RectBivariateSpline function to do Bivariate spline approximation over a rectangular mesh.

Parameters:

• interp_dem (np.ndarray) – input dem image

• xgrid (np.ndarray) – x axis grid

• ygrid (np.ndarray) – x axis grid

Returns:

spline_1, spline_2,

Return type:

Tuple[RectBivariateSpline, RectBivariateSpline]

interpolate_classif_layers(dem_classif: xarray.DataArray, xgrid: numpy.ndarray, ygrid: numpy.ndarray, x_offset: float, y_offset: float)

interpolates the classification layers on the input grids with the input offsets.

Parameters:

• dem_classif (xr.Dataarray) – input dem image

• xgrid (np.ndarray) – x axis grid

• ygrid (np.ndarray) – x axis grid

• x_offset (float) – x offset

• y_offset (float) – y offset

Returns:

interpolated classification layers

Return type:

xr.Dataarray

static crop_dem_with_offset(dem: numpy.ndarray, x_offset: float, y_offset: float) → numpy.ndarray

Crops the input dem with the given offsets.

Parameters:

• dem (np.ndarray) – input dem image

• x_offset (float) – x offset

• y_offset (float) – y offset

Returns:

cropped dem

Return type:

np.ndarray

crop_classif_layers(dem_classif: xarray.DataArray, x_offset, y_offset) → xarray.DataArray

crop_classif_layers crops and updates the input classification layers with the input offsets.

Parameters:

• dem_classif (xr.Dataarray) – classification layers

• x_offset (float) – x offset

• y_offset (float) – y offset

Returns:

cropped classification layers

Return type:

xr.Dataarray

static _grad2d(dem: numpy.ndarray) → Tuple[numpy.ndarray, numpy.ndarray]

Computes input DEM’s slope and aspect

Parameters:

dem (np.ndarray) – input dem

Returns:

slope (fast forward style) and aspect

Return type:

np.ndarray, np.ndarray

_nuth_kaab_single_iter(dh: numpy.ndarray, slope: numpy.ndarray, aspect: numpy.ndarray, plot_file: str | bool = None) → Tuple[float, float, float]

Computes the horizontal shift between 2 DEMs using the method presented in Nuth & Kaab 2011

Parameters:

• dh (np.ndarray) – elevation difference sec - ref

• slope (np.ndarray) – slope for the same locations as the dh

• aspect (np.ndarray) – aspect for the same locations as the dh

• plot_file (str or bool) – file to where store plot. Set to None if plot is to be printed. Set to False for no plot at all.

Returns:

east, north, c

Return type:

float, float, float

_filter_target(aspect: numpy.ndarray, target: numpy.ndarray) → Tuple[numpy.ndarray, numpy.ndarray]

Filter target slice outliers of an input array by a 3*sigma filtering to improve Nuth et kaab fit.

Parameters:

• aspect (np.ndarray) – elevation difference sec - ref

• target (np.ndarray) – slope for the same locations as the dh

Returns:

slice_filt_median, target_filt

Return type:

Tuple[List]

_save_fit_plots(plot_file: str, aspect: numpy.ndarray, target: numpy.ndarray, target_filt: numpy.ndarray, slice_filt_median: numpy.ndarray, yfit: numpy.ndarray) → None

Compute and save the Nuth et Kaab fit plots of each iteration

Parameters:

• plot_file (str) – file to where store plot

• aspect (np.ndarray) – terrain aspect

• target (np.ndarray) – nuth et kaab target

• target_filt (np.ndarray) – filtered target

• slice_filt_median (np.ndarray) – filtered target median

• yfit (np.ndarray) – fit polynome of target_filt

Returns:

None

save_results_dict()

Save the coregistration results on a Dict The altimetric and coregistration results are saved. Logging of the altimetric results is done in this function.

Returns:

None