[POC]: Add an initial shift for coregistration

[adebardo]

Context

The CNES wants to integrate the ability to instantiate an initial offset for the co-registration calculation. Because the dem_coregistration function allows for easy addition of this feature and we will recommend its use to CNES members, we propose simple modifications at this level.

Implementation

1. Using the translate function from Geoutils:

   The translate function from Geoutils allows for translating a Digital Elevation Model (DEM). We will call this function in the dem_coregsitration method .

def dem_coregistration(
    src_dem_path: str | RasterType,
    ref_dem_path: str | RasterType,
    out_dem_path: str | None = None,
    coreg_method: Coreg | None = NuthKaab() + VerticalShift(),
    grid: str = "ref",
    resample: bool = False,
    resampling: rio.warp.Resampling | None = rio.warp.Resampling.bilinear,
    shp_list: list[str | gu.Vector] | tuple[str | gu.Vector] | tuple[()] = (),
    inout: list[int] | tuple[int] | tuple[()] = (),
    filtering: bool = True,
    dh_max: Number = None,
    nmad_factor: Number = 5,
    slope_lim: list[Number] | tuple[Number, Number] = (0.1, 40),
    plot: bool = False,
    out_fig: str = None,
+   estimated_initial_shift : list = [0, 0] 
) -> tuple[DEM, Coreg, pd.DataFrame, NDArrayf]:
+	"""
+	explain estimated_initial_shift + affine only
+	"""

	    if isinstance(ref_dem_path, str):
	        if grid == "ref":
	            ref_dem, src_dem = gu.raster.load_multiple_rasters([ref_dem_path, src_dem_path], ref_grid=0)
	        elif grid == "src":
	            ref_dem, src_dem = gu.raster.load_multiple_rasters([ref_dem_path, src_dem_path], ref_grid=1)
	    else:
	        ref_dem = ref_dem_path
	        src_dem = src_dem_path
	        if grid == "ref":
	            src_dem = src_dem.reproject(ref_dem, silent=True)
	        elif grid == "src":
	            ref_dem = ref_dem.reproject(src_dem, silent=True)
+      if estimated_initial_shift != [0, 0] :
+          logging.warning("Initial shift in affine mode only")
+          estimated_initial_shift = [estimated_initial_shift[0] * pixel_resolution_x, estimated_initial_shift[0] * pixel_resolution_y]
+          src_dem.translate(estimated_initial_shift[0], estimated_initial_shift[1], inplace=True)
	.
	.
	.
    # Coregister to reference - Note: this will spread NaN
    coreg_method.fit(ref_dem, src_dem, inlier_mask)
    dem_coreg = coreg_method.apply(src_dem, resample=resample, resampling=resampling)
+   if estimated_initial_shift != [0, 0] :
+   	calculated_shift_x = dem_coreg.meta["shift_x"]
+       calculated_shift_y = dem_coreg.meta["shift_y"]

+		dem_coreg.meta["shift_x"] = calculated_shift_x + estimated_initial_shift[0] 
+		dem_coreg.meta["shift_y"] = calculated_shift_x + estimated_initial_shift[1]

Tests

• Set up a test for the dem_coregistration function with an initial offset value different from 0.
  Here, a new paragraph should be added to test this updated version.

Documentation

• Update the documentation to include examples on using the new estimated_initial_shift

/estimation 3d

[rhugonnet]

Looks good! 🙂

So the "explain affine only" would essentially be a check like coreg_method.is_affine for a Coreg object or all(c.is_affine for c in coreg_method) for a CoregPipeline object?
I'm not sure of the behaviour of the is_affine property on a pipeline right now (it might fail), we could modify it so it derives the proper attribute directly here:

xdem/xdem/coreg/base.py

Line 1602 in 595acb9

def is_affine(self) -> bool:

(either by finding a solution for it to run on all subclasses, or simply by overridding the property in CoregPipeline).