import os
import numpy as np
from polsartools.utils.proc_utils import process_chunks_parallel
from polsartools.utils.utils import conv2d,time_it
from .cp_infiles import cpc2files
[docs]
@time_it
def dopcp(infolder, chi_in=45, psi_in=0, window_size=1, outType="tif", cog_flag=False,
cog_overviews = [2, 4, 8, 16], write_flag=True,
max_workers=None,block_size=(512, 512),
progress_callback=None, # for QGIS plugin
):
"""Compute Degree of Polarization (DoP) from compact-pol SAR data.
This function calculates the Degree of Polarization (DoP) from compact-polarimetric
SAR data, which quantifies the polarized portion of the scattered wave. DoP is a
useful parameter for characterizing surface properties and scattering mechanisms.
Examples
--------
>>> # Basic usage with default parameters (right circular transmission)
>>> dopcp("/path/to/cp_data")
>>> # Advanced usage with custom parameters
>>> dopcp(
... infolder="/path/to/cp_data",
... chi_in=-45, # left circular transmission
... window_size=5,
... outType="tif",
... cog_flag=True,
... block_size=(1024, 1024)
... )
Parameters
----------
infolder : str
Path to the input folder containing compact-pol C2 matrix files.
chi_in : float, default=45
Ellipticity angle chi of the transmitted wave in degrees.
For circular polarization, chi = 45° (right circular) or -45° (left circular).
psi_in : float, default=0
Orientation angle psi of the transmitted wave in degrees.
For circular polarization, typically 0°.
window_size : int, default=1
Size of the spatial averaging window. Larger windows provide better
estimation of DoP but reduce spatial resolution.
outType : {'tif', 'bin'}, default='tif'
Output file format:
- 'tif': GeoTIFF format with georeferencing information
- 'bin': Raw binary format
cog_flag : bool, default=False
If True, creates a Cloud Optimized GeoTIFF (COG) with internal tiling
and overviews for efficient web access.
cog_overviews : list[int], default=[2, 4, 8, 16]
Overview levels for COG creation. Each number represents the
decimation factor for that overview level.
write_flag : bool, default=True
If True, writes results to disk. If False, only processes data in memory.
max_workers : int | None, default=None
Maximum number of parallel processing workers. If None, uses
CPU count - 1 workers.
block_size : tuple[int, int], default=(512, 512)
Size of processing blocks (rows, cols) for parallel computation.
Larger blocks use more memory but may be more efficient.
Returns
-------
None
Results are written to disk as either 'dopcp.tif' or 'dopcp.bin'
in the input folder. The output DoP values range from 0 to 1, where:
- 0 indicates completely unpolarized scattered wave
- 1 indicates completely polarized scattered wave
Notes
-----
The Degree of Polarization (DoP) is calculated using the Stokes parameters
derived from the compact-pol coherency matrix. For a partially polarized wave,
DoP is given by:
DoP = sqrt(S₁² + S₂² + S₃²) / S₀
where S₀, S₁, S₂, S₃ are the Stokes parameters.
Key characteristics:
- DoP is invariant to the wave polarization basis
- Values are normalized between 0 and 1
- Higher values indicate stronger polarized scattering
- Lower values suggest depolarizing mechanisms
Common applications:
- Surface roughness estimation
- Vegetation density analysis
- Urban area characterization
- Sea ice classification
"""
input_filepaths = cpc2files(infolder)
output_filepaths = []
if outType == "bin":
output_filepaths.append(os.path.join(infolder, "dopcp.bin"))
else:
output_filepaths.append(os.path.join(infolder, "dopcp.tif"))
process_chunks_parallel(input_filepaths, list(output_filepaths),
window_size,
write_flag,
process_chunk_dopcp,
*[chi_in, psi_in],
block_size=block_size,
max_workers=max_workers,
num_outputs=len(output_filepaths),
cog_flag=cog_flag,
cog_overviews=cog_overviews,
progress_callback=progress_callback
)
def process_chunk_dopcp(chunks, window_size, *args, **kwargs):
chi_in=args[-2]
psi_in=args[-1]
kernel = np.ones((window_size,window_size),np.float32)/(window_size*window_size)
c11_T1 = np.array(chunks[0])
c12_T1 = np.array(chunks[1])+1j*np.array(chunks[2])
c21_T1 = np.conj(c12_T1)
c22_T1 = np.array(chunks[3])
ncols,nrows = np.shape(c11_T1)
if window_size>1:
c11_T1 = conv2d(np.real(c11_T1),kernel)+1j*conv2d(np.imag(c11_T1),kernel)
c12_T1 = conv2d(np.real(c12_T1),kernel)+1j*conv2d(np.imag(c12_T1),kernel)
c21_T1 = conv2d(np.real(c21_T1),kernel)+1j*conv2d(np.imag(c21_T1),kernel)
c22_T1 = conv2d(np.real(c22_T1),kernel)+1j*conv2d(np.imag(c22_T1),kernel)
# Compute Stokes parameters
s0 = c11_T1 + c22_T1
s1 = c11_T1 - c22_T1
s2 = c12_T1 + c21_T1
s3 = np.where(chi_in >= 0, 1j * (c12_T1 - c21_T1), -1j * (c12_T1 - c21_T1))
dop= np.sqrt(np.power(s1,2) + np.power(s2,2) + np.power(s3,2))/(s0);
return np.real(dop).astype(np.float32)
