Freeman-Durden decomposition¶

This notebook shows how to apply the Freeman-Durden 3-component decomposition to a PolSARpro NetCDF dataset.

Import packages and set directories¶

In [1]:

import os 
from pathlib import Path
import xarray as xr
from polsarpro.io import open_netcdf_beam
from polsarpro.decompositions import freeman

# optional import for progress bar
from dask.diagnostics import ProgressBar

# change to your data paths
# original dataset
input_alos_data = Path("/data/psp/test_files/SAN_FRANCISCO_ALOS1_slc.nc")
output_dir = Path("/data/psp/res")

import os from pathlib import Path import xarray as xr from polsarpro.io import open_netcdf_beam from polsarpro.decompositions import freeman # optional import for progress bar from dask.diagnostics import ProgressBar # change to your data paths # original dataset input_alos_data = Path("/data/psp/test_files/SAN_FRANCISCO_ALOS1_slc.nc") output_dir = Path("/data/psp/res")

Load data¶

We load the SNAP NetCDF-BEAM dataset using the open_netcdf_beam function. To obtain such a dataset, please refer to the "Getting Started" tutorial or the quickstart-tutorial.ipynb notebook.

In [2]:

# uncomment to test on S matrix made with SNAP
S = open_netcdf_beam(input_alos_data)

# uncomment to test on S matrix made with SNAP S = open_netcdf_beam(input_alos_data)

Apply the decomposition¶

Let's apply the decomposition and write the result to a NetCDF file. Optionally we can use a progress bar to monitor the progress of the computation.

In [3]:

# change to the name of your liking
file_out = output_dir / "psp_freeman.nc"

# netcdf writer cannot overwrite
if os.path.isfile(file_out):
    os.remove(file_out)

with ProgressBar():
    freeman(S, boxcar_size=[7, 7]).to_netcdf(file_out)

# change to the name of your liking file_out = output_dir / "psp_freeman.nc" # netcdf writer cannot overwrite if os.path.isfile(file_out): os.remove(file_out) with ProgressBar(): freeman(S, boxcar_size=[7, 7]).to_netcdf(file_out)

[########################################] | 100% Completed | 15.82 s

Display outputs¶

We open the previously saved dataset:

In [4]:

res = xr.open_dataset(file_out)

res = xr.open_dataset(file_out)

Then we may plot individual parameters:

In [ ]:

# odd bounce / surface component
res.odd.plot.imshow(vmin=0, vmax=1)

# odd bounce / surface component res.odd.plot.imshow(vmin=0, vmax=1)

Out[ ]:

<matplotlib.image.AxesImage at 0x783e191d34d0>

In [6]:

# double bounce
res.double.plot.imshow(vmin=0, vmax=1)

# double bounce res.double.plot.imshow(vmin=0, vmax=1)

Out[6]:

<matplotlib.image.AxesImage at 0x783e17c60c80>

In [7]:

# volume component
res.volume.plot.imshow(vmin=0, vmax=1)

# volume component res.volume.plot.imshow(vmin=0, vmax=1)

Out[7]:

<matplotlib.image.AxesImage at 0x783e180616d0>