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 s_omega(in_dir, chi=45, psi=0, win=1, fmt="tif", cog=False,
ovr = [2, 4, 8, 16], comp=False,
max_workers=None,block_size=(512, 512),
progress_callback=None, # for QGIS plugin
):
"""Perform Modified/Improved S-Omega Decomposition for compact-pol SAR data.
This function implements an enhanced version of the S-Omega decomposition
technique for compact-polarimetric SAR data. It decomposes the total
backscattered power into three components: surface scattering (Ps),
double-bounce scattering (Pd), and volume scattering (Pv), with improvements
over the traditional S-Omega method.
Examples
--------
>>> # Basic usage with default parameters
>>> s_omega("/path/to/cp_data")
>>> # Advanced usage with custom parameters
>>> s_omega(
... in_dir="/path/to/cp_data",
... chi=-45,
... win=5,
... fmt="tif",
... cog=True,
... block_size=(1024, 1024)
... )
Parameters
----------
in_dir : str
Path to the input folder containing compact-pol C2 matrix files.
chi : float, default=45
Ellipticity angle chi of the transmitted wave in degrees.
For circular polarization, chi = 45° (right circular) or -45° (left circular).
psi : float, default=0
Orientation angle psi of the transmitted wave in degrees.
For circular polarization, typically 0°.
win : int, default=1
Size of the spatial averaging window. Larger windows reduce speckle noise
but decrease spatial resolution.
fmt : {'tif', 'bin'}, default='tif'
Output file format:
- 'tif': GeoTIFF format with georeferencing information
- 'bin': Raw binary format
cog : bool, default=False
If True, creates Cloud Optimized GeoTIFF (COG) outputs with internal tiling
and overviews for efficient web access.
ovr : list[int], default=[2, 4, 8, 16]
Overview levels for COG creation. Each number represents the
decimation factor for that overview level.
comp : bool, default=False
If True, applies LZW compression to the output GeoTIFF files.
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
Writes three output files to disk:
1. Ps_miSOmega: Surface scattering power component
2. Pd_miSOmega: Double-bounce scattering power component
3. Pv_miSOmega: Volume scattering power component
"""
write_flag=True
input_filepaths = cpc2files(in_dir)
output_filepaths = []
if fmt == "bin":
output_filepaths.append(os.path.join(in_dir, "Ps_miSOmega.bin"))
output_filepaths.append(os.path.join(in_dir, "Pd_miSOmega.bin"))
output_filepaths.append(os.path.join(in_dir, "Pv_miSOmega.bin"))
else:
output_filepaths.append(os.path.join(in_dir, "Ps_miSOmega.tif"))
output_filepaths.append(os.path.join(in_dir, "Pd_miSOmega.tif"))
output_filepaths.append(os.path.join(in_dir, "Pv_miSOmega.tif"))
process_chunks_parallel(input_filepaths, list(output_filepaths),
win,
write_flag,
process_chunk_misomega,
*[chi, psi],
block_size=block_size,
max_workers=max_workers,
num_outputs=len(output_filepaths),
cog=cog, ovr=ovr, comp=comp,
progress_callback=progress_callback
)
def process_chunk_misomega(chunks, window_size, *args, **kwargs):
chi=args[-2]
psi=args[-1]
# print(chi,psi)
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 = np.abs(c11_T1 + c22_T1)
s1 = np.abs(c11_T1 - c22_T1)
s2 = np.abs(c12_T1 + c21_T1)
s3 = np.where(chi >= 0, 1j * (c12_T1 - c21_T1), -1j * (c12_T1 - c21_T1))
s3 = np.real(s3)
## Stokes child parameters
SC = ((s0)-(s3))/2;
OC = ((s0)+(s3))/2;
CPR = np.divide(SC,OC) ##SC/OC
# CPR = SC/OC
##scattered fields
dop= np.sqrt(np.power(s1,2) + np.power(s2,2) + np.power(s3,2))/(s0)
Psi = 0.5*((180/np.pi)*np.arctan2(s2,s1))
DOCP = (-s3)/(dop*s0);
Chi = 0.5*((180/np.pi)*np.arcsin(DOCP))
##---------------------------------
##---------------------------------
# Calculating Omega from S-Omega decomposition
x1 = np.cos(2*chi*np.pi/180)*np.cos(2*psi*np.pi/180)*np.cos(2*Chi*np.pi/180)*np.cos(2*Psi*np.pi/180)
x2 = np.cos(2*chi*np.pi/180)*np.sin(2*psi*np.pi/180)*np.cos(2*Chi*np.pi/180)*np.sin(2*Psi*np.pi/180)
x3 = np.abs(np.sin(2*chi*np.pi/180)*np.sin(2*Chi*np.pi/180))
Prec = dop*(1 + x1 + x2 + x3)
Prec1 = (1 - dop) + dop*(1 + x1 + x2 + x3)
omega = (Prec/Prec1)
# ## Improved S-Omega (i-SOmega powers
ind_g1 = (CPR>1).astype(int)
s_new_g1 = omega*(1 - omega)*OC
db_new_g1 = omega*s0 - omega*(1 - omega)*OC ##depolarized of OC x polarized of SC
ind_l1 = (CPR<1).astype(int)
s_new_l1 = omega*s0 - omega*(1 - omega)*SC
db_new_l1 = omega*(1 - omega)*SC ##depolarized of OC x polarized of SC
ind_e1 = (CPR==1).astype(int)
s_new_e1 = omega*OC
db_new_e1 = omega*SC
surface_new = s_new_g1*ind_g1+s_new_l1*ind_l1+s_new_e1*ind_e1
double_bounce_new = db_new_g1*ind_g1+db_new_l1*ind_l1+db_new_e1*ind_e1
diffused_new = (1 - omega)*s0; ##diffused scattering
surface_new[surface_new==0] = np.nan
double_bounce_new[double_bounce_new==0] = np.nan
diffused_new[diffused_new==0] = np.nan
return surface_new.astype(np.float32), double_bounce_new.astype(np.float32), diffused_new.astype(np.float32)
