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 C3_T3_mat
from .fp_infiles import fp_c3t3files
[docs]
@time_it
def neumann_parm(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 Neumann Decomposition for full-pol SAR data.
This function implements the Neumann decomposition for full-polarimetric SAR data,
extracting four key parameters: polarization orientation angle (psi), degree of
polarization (delta_mod), phase difference (delta_pha), and helicity (tau).
This decomposition is particularly useful for characterizing complex scattering
mechanisms in urban and natural environments.
Examples
--------
>>> # Basic usage with default parameters
>>> neumann_parm("/path/to/fullpol_data")
>>> # Advanced usage with custom parameters
>>> neumann_parm(
... 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 four output files to disk:
1. Neu_psi: Polarization orientation angle
2. Neu_delta_mod: Degree of polarization
3. Neu_delta_pha: Phase difference
4. Neu_tau: Helicity parameter
"""
write_flag=True
input_filepaths = fp_c3t3files(in_dir)
output_filepaths = []
if fmt == "bin":
output_filepaths.append(os.path.join(in_dir, "Neu_psi.bin"))
output_filepaths.append(os.path.join(in_dir, "Neu_delta_mod.bin"))
output_filepaths.append(os.path.join(in_dir, "Neu_delta_pha.bin"))
output_filepaths.append(os.path.join(in_dir, "Neu_tau.bin"))
else:
output_filepaths.append(os.path.join(in_dir, "Neu_psi.tif"))
output_filepaths.append(os.path.join(in_dir, "Neu_delta_mod.tif"))
output_filepaths.append(os.path.join(in_dir, "Neu_delta_pha.tif"))
output_filepaths.append(os.path.join(in_dir, "Neu_tau.tif"))
process_chunks_parallel(input_filepaths, list(output_filepaths),
window_size=win, write_flag=write_flag,
processing_func=process_chunk_neufp,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_neufp(chunks, window_size, input_filepaths, *args):
# additional_arg1 = args[0] if len(args) > 0 else None
# additional_arg2 = args[1] if len(args) > 1 else None
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])
T_T1 = np.array([[t11_T1, t12_T1, t13_T1],
[t21_T1, t22_T1, t23_T1],
[t31_T1, t32_T1, t33_T1]])
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])
C3 = np.array([[C11, C12, C13],
[C21, C22, C23],
[C31, C32, C33]])
T_T1 = C3_T3_mat(C3)
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)
# Indices for vectorized access
i, j = np.indices((rows, cols))
# Extract components of T3_stack once
T3_stack_real = np.real(T_T1[:,i, j ])
T3_stack_imag = np.imag(T_T1[:, i, j])
T_00_0 = T3_stack_real[0, :, :]
T_01_0 = T3_stack_real[1, :, :]
T_01_1 = T3_stack_imag[1, :, :]
T_02_0 = T3_stack_real[2, :, :]
T_02_1 = T3_stack_imag[2, :, :]
T_11_0 = T3_stack_real[4, :, :]
T_12_0 = T3_stack_real[5, :, :]
T_22_0 = T3_stack_real[8, :, :]
# Compute the Phi value using the given formula
Phi = 0.25 * (np.pi + np.arctan2(-2. * T_12_0, T_22_0 - T_11_0))
# Adjust Phi for the correct orientation
Phi[Phi <= np.pi / 4.] = Phi[Phi <= np.pi / 4.]
Phi[Phi > np.pi / 4.] -= np.pi / 2.
# Convert Phi to degrees
Neumann_psi = Phi * 180. / np.pi
# Pre-compute trigonometric terms
cos_2Phi = np.cos(2 * Phi)
sin_2Phi = np.sin(2 * Phi)
sin_4Phi = np.sin(4 * Phi)
# Coherency Matrix de-orientation
T110 = T_00_0
T12re0 = T_01_0 * cos_2Phi - T_02_0 * sin_2Phi
T12im0 = T_01_1 * cos_2Phi - T_02_1 * sin_2Phi
T220 = T_11_0 * cos_2Phi**2 - T_12_0 * sin_4Phi + T_22_0 * sin_2Phi**2
T330 = T_11_0 * sin_2Phi**2 - T_12_0 * sin_4Phi + T_22_0 * cos_2Phi**2
# Compute Neumann_delta_mod, Neumann_delta_pha, and Neumann_tau
Neumann_delta_mod = np.sqrt((T220 + T330) / (T110 + np.finfo(float).eps))
Neumann_delta_pha = np.arctan2(T12im0, T12re0) * 180. / np.pi
Neumann_tau = 1. - ((np.sqrt(T12re0 ** 2 + T12im0 ** 2) / T110) / (Neumann_delta_mod + np.finfo(float).eps))
return Neumann_psi.astype(np.float32), Neumann_delta_mod.astype(np.float32), Neumann_delta_pha.astype(np.float32),Neumann_tau.astype(np.float32)
