import os
import numpy as np
from polsartools.utils.proc_utils import process_chunks_parallel
from polsartools.utils.utils import conv2d,time_it
from polsartools.utils.convert_matrices import T3_C3_mat
from .fp_infiles import fp_c3t3files
[docs]
@time_it
def freeman_3c(in_dir, 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 Freeman-Durdan 3-Component Decomposition for full-pol SAR data.
This function implements the three-component decomposition for
full-polarimetric SAR data, decomposing the total scattered power into surface (Ps),
double-bounce (Pd), and volume (Pv) scattering components.
Examples
--------
>>> # Basic usage with default parameters
>>> freeman_3c("/path/to/fullpol_data")
>>> # Advanced usage with custom parameters
>>> freeman_3c(
... in_dir="/path/to/fullpol_data",
... win=5,
... fmt="tif",
... cog=True,
... block_size=(1024, 1024)
... )
Parameters
----------
in_dir : str
Path to the input folder containing full-pol T3 or C3 matrix files.
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, uses LZW compression for GeoTIFF outputs.
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. Freeman_3c_odd: Surface scattering power component
2. Freeman_3c_dbl: Double-bounce scattering power component
3. Freeman_3c_vol: Volume scattering power component
"""
write_flag=True
input_filepaths = fp_c3t3files(in_dir)
output_filepaths = []
if fmt == "bin":
output_filepaths.append(os.path.join(in_dir, "Freeman_3c_odd.bin"))
output_filepaths.append(os.path.join(in_dir, "Freeman_3c_dbl.bin"))
output_filepaths.append(os.path.join(in_dir, "Freeman_3c_vol.bin"))
else:
output_filepaths.append(os.path.join(in_dir, "Freeman_3c_odd.tif"))
output_filepaths.append(os.path.join(in_dir, "Freeman_3c_dbl.tif"))
output_filepaths.append(os.path.join(in_dir, "Freeman_3c_vol.tif"))
process_chunks_parallel(input_filepaths, list(output_filepaths),
window_size=win, write_flag=write_flag,
processing_func=process_chunk_free3c,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_free3c(chunks, window_size, input_filepaths, *args):
if 'T11' in input_filepaths[0] and 'T22' in input_filepaths[5] and 'T33' in input_filepaths[8]:
t11_T1 = np.array(chunks[0])
t12_T1 = np.array(chunks[1])+1j*np.array(chunks[2])
t13_T1 = np.array(chunks[3])+1j*np.array(chunks[4])
t21_T1 = np.conj(t12_T1)
t22_T1 = np.array(chunks[5])
t23_T1 = np.array(chunks[6])+1j*np.array(chunks[7])
t31_T1 = np.conj(t13_T1)
t32_T1 = np.conj(t23_T1)
t33_T1 = np.array(chunks[8])
T3 = np.array([[t11_T1, t12_T1, t13_T1],
[t21_T1, t22_T1, t23_T1],
[t31_T1, t32_T1, t33_T1]])
T_T1 = T3_C3_mat(T3)
if 'C11' in input_filepaths[0] and 'C22' in input_filepaths[5] and 'C33' in input_filepaths[8]:
C11 = np.array(chunks[0])
C12 = np.array(chunks[1])+1j*np.array(chunks[2])
C13 = np.array(chunks[3])+1j*np.array(chunks[4])
C21 = np.conj(C12)
C22 = np.array(chunks[5])
C23 = np.array(chunks[6])+1j*np.array(chunks[7])
C31 = np.conj(C13)
C32 = np.conj(C23)
C33 = np.array(chunks[8])
T_T1 = np.array([[C11, C12, C13],
[C21, C22, C23],
[C31, C32, C33]])
# print("Window size: ",window_size)
if window_size>1:
kernel = np.ones((window_size,window_size),np.float32)/(window_size*window_size)
t11f = conv2d(T_T1[0,0,:,:],kernel)
t12f = conv2d(np.real(T_T1[0,1,:,:]),kernel)+1j*conv2d(np.imag(T_T1[0,1,:,:]),kernel)
t13f = conv2d(np.real(T_T1[0,2,:,:]),kernel)+1j*conv2d(np.imag(T_T1[0,2,:,:]),kernel)
t21f = np.conj(t12f)
t22f = conv2d(T_T1[1,1,:,:],kernel)
t23f = conv2d(np.real(T_T1[1,2,:,:]),kernel)+1j*conv2d(np.imag(T_T1[1,2,:,:]),kernel)
t31f = np.conj(t13f)
t32f = np.conj(t23f)
t33f = conv2d(T_T1[2,2,:,:],kernel)
T_T1 = np.array([[t11f, t12f, t13f], [t21f, t22f, t23f], [t31f, t32f, t33f]])
_,_,rows,cols = np.shape(T_T1)
T_T1 = T_T1.reshape(9, rows, cols)
C11 = np.real(T_T1[0,:,:])
C22 = np.real(T_T1[4,:,:])
C33 = np.real(T_T1[8,:,:])
C13_re = np.real(T_T1[2,:,:])
C13_im = np.imag(T_T1[2,:,:])
del T_T1
Span = C11+C22+C33
SpanMax = np.nanmax(Span)
eps = 1e-10
# Copy inputs to avoid in-place corruption
C11_ = C11.copy()
C33_ = C33.copy()
C13_re_ = C13_re.copy()
FV = 3. * C22 / 2.
# Subtract volume component
C11_ -= FV
C33_ -= FV
C13_re_ -= FV / 3.
# Initialize outputs
FD = np.zeros_like(C11_)
FS = np.zeros_like(C11_)
ALP = np.zeros_like(C11_)
BET = np.zeros_like(C11_)
odd = np.zeros_like(C11_)
dbl = np.zeros_like(C11_)
vol = np.zeros_like(C11_)
# Mask for low power (volume dominates)
mask_eps = (C11_ <= eps) | (C33_ <= eps)
FV_eps = 3. * (C11_[mask_eps] + C22[mask_eps] + C33_[mask_eps] + 2 * FV[mask_eps]) / 8.
FV[mask_eps] = FV_eps # Update FV only for masked pixels
# Data conditioning
rtemp = C13_re_**2 + C13_im**2
mask_non_realizable = rtemp > (C11_ * C33_)
scale = np.sqrt((C11_ * C33_) / rtemp)
scale[~mask_non_realizable] = 1.0 # Avoid modifying valid pixels
C13_re_ *= scale
C13_im *= scale
# Bounce classification
mask_odd = (C13_re_ >= 0.) & ~mask_eps
mask_even = (C13_re_ < 0.) & ~mask_eps
# Odd Bounce
FD[mask_odd] = (C11_[mask_odd] * C33_[mask_odd] - C13_re_[mask_odd]**2 - C13_im[mask_odd]**2) / \
(C11_[mask_odd] + C33_[mask_odd] + 2 * C13_re_[mask_odd])
FS[mask_odd] = C33_[mask_odd] - FD[mask_odd]
ALP[mask_odd] = -1.
BET[mask_odd] = np.sqrt((FD[mask_odd] + C13_re_[mask_odd])**2 + C13_im[mask_odd]**2) / FS[mask_odd]
# Even Bounce
FS[mask_even] = (C11_[mask_even] * C33_[mask_even] - C13_re_[mask_even]**2 - C13_im[mask_even]**2) / \
(C11_[mask_even] + C33_[mask_even] - 2 * C13_re_[mask_even])
FD[mask_even] = C33_[mask_even] - FS[mask_even]
BET[mask_even] = 1.
FD_safe = np.where(FD[mask_even] <= eps, eps, FD[mask_even])
ALP[mask_even] = np.sqrt((FS[mask_even] - C13_re_[mask_even])**2 + C13_im[mask_even]**2) / FD_safe
odd = FS * (1 + BET**2)
dbl = FD * (1 + ALP**2)
vol = 8. * FV / 3.
odd = np.clip(odd, 0, SpanMax)
dbl = np.clip(dbl, 0, SpanMax)
vol = np.clip(vol, 0, SpanMax)
zero_mask = (odd == 0) & (dbl == 0) & (vol == 0)
odd = odd.astype(np.float32)
dbl = dbl.astype(np.float32)
vol = vol.astype(np.float32)
odd[zero_mask] = np.nan
dbl[zero_mask] = np.nan
vol[zero_mask] = np.nan
return odd, dbl, vol
