Basic registration methods (sarpy.processing.registration.basic)

Basic image registration tools

sarpy.processing.registration.basic.best_physical_location_fit(structs: Sequence[SICDType | SIDDType | SIDDType], locs: ndarray | list | tuple, **minimization_args) → Tuple[ndarray, float, Any]

Given a collection of SICD and/or SIDDs and a collection of image coordinates, each of which identifies the pixel location of the same feature in the respective image, determine the (best fit) geophysical location of this feature.

This assumes that any adjustable parameters used for the SICD/SIDD projection model have already been applied (via define_coa_projection()).

Parameters:

  • structs (Sequence[SICDType|SIDDType1|SIDDType2]) – The collection of sicds/sidds, of length N

  • locs (numpy.ndarray|list|tuple) – The image coordinate collection, of shape (N, 2)

  • minimization_args – The keyword arguments (after args argument) passed through to scipy.optimize.minimize(). This will default to ‘Powell’ optimization, which seems generally much more reliable for this problem than the steepest descent based approaches.

Returns:

  • ecf_location (numpy.ndarray) – The location in question, in ECF coordinates

  • residue (float) – The mean square residue of the physical distance between the given location and the image locations projected into the surface of given HAE value

  • result – The minimization result object

sarpy.processing.registration.basic.find_best_adjustable_parameters(struct: SICDType | SIDDType | SIDDType, ecf_coords: ndarray, img_coords: ndarray, **minimization_args) → Tuple[ndarray, ndarray, float, float, Any]

Find the best projection model adjustable parameters (in ‘ECF’ coordinate frame) to fit the geophysical coordinate locations to the image coordinate locations.

Parameters:

  • struct (SICDType|SIDDType1|SIDDType2) –

  • ecf_coords (numpy.ndarray) – The geophysical coordinates of shape (N, 3) for the identified features

  • img_coords (numpy.ndarray) – The image coordinates of shape (N, 2) for the identified features

  • minimization_args – The keyword arguments (after args argument) passed through to scipy.optimize.minimize(). This will default to ‘Powell’ optimization, which seems generally much more reliable for this problem than the steepest descent based approaches.

Returns:

  • delta_arp (numpy.ndarray)

  • delta_varp (numpy.ndarray)

  • delta_range (float)

  • residue (float) – Average pixel coordinate distance across the features between projected and observed pixel locations

  • result – The minimization result