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 mf3cf(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 Model-Free 3-Component Decomposition for full-pol SAR data.
This function implements the model-free three-component decomposition for
full-polarimetric SAR data, decomposing the total scattered power into surface (Ps),
double-bounce (Pd), and volume (Pv) scattering components, along with the
scattering type parameter (Theta_FP). Unlike model-based decompositions, this
approach doesn't assume specific scattering models.
Examples
--------
>>> # Basic usage with default parameters
>>> mf3cf("/path/to/fullpol_data")
>>> # Advanced usage with custom parameters
>>> mf3cf(
... 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. Ps_mf3cf: Surface scattering power component
2. Pd_mf3cf: Double-bounce scattering power component
3. Pv_mf3cf: Volume scattering power component
4. Theta_FP_mf3cf: Scattering type parameter
"""
write_flag=True
input_filepaths = fp_c3t3files(in_dir)
output_filepaths = []
if fmt == "bin":
output_filepaths.append(os.path.join(in_dir, "Ps_mf3cf.bin"))
output_filepaths.append(os.path.join(in_dir, "Pd_mf3cf.bin"))
output_filepaths.append(os.path.join(in_dir, "Pv_mf3cf.bin"))
output_filepaths.append(os.path.join(in_dir, "Theta_FP_mf3cf.bin"))
else:
output_filepaths.append(os.path.join(in_dir, "Ps_mf3cf.tif"))
output_filepaths.append(os.path.join(in_dir, "Pd_mf3cf.tif"))
output_filepaths.append(os.path.join(in_dir, "Pv_mf3cf.tif"))
output_filepaths.append(os.path.join(in_dir, "Theta_FP_mf3cf.tif"))
process_chunks_parallel(input_filepaths, list(output_filepaths),
window_size=win, write_flag=write_flag,
processing_func=process_chunk_mf3cf,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_mf3cf(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]])
reshaped_arr = T_T1.reshape(3, 3, -1).transpose(2, 0, 1)
det_T3 = np.linalg.det(reshaped_arr)
# del reshaped_arr
det_T3 = det_T3.reshape(T_T1.shape[2], T_T1.shape[3])
trace_T3 = T_T1[0,0,:,:] + T_T1[1,1,:,:] + T_T1[2,2,:,:]
m1 = np.real(np.sqrt(1-(27*(det_T3/(trace_T3**3)))))
h = (T_T1[0,0,:,:] - T_T1[1,1,:,:] - T_T1[2,2,:,:])
g = (T_T1[1,1,:,:] + T_T1[2,2,:,:])
span = T_T1[0,0,:,:] + T_T1[1,1,:,:] + T_T1[2,2,:,:]
val = (m1*span*h)/(T_T1[0,0,:,:]*g+m1**2*span**2)
thet = np.real(np.arctan(val))
theta_FP = np.rad2deg(thet).astype(np.float32)
Ps_FP = np.nan_to_num(np.real(((m1*(span)*(1+np.sin(2*thet))/2)))).astype(np.float32)
Pd_FP = np.nan_to_num(np.real(((m1*(span)*(1-np.sin(2*thet))/2)))).astype(np.float32)
Pv_FP = np.nan_to_num(np.real(span*(1-m1))).astype(np.float32)
return Ps_FP.astype(np.float32), Pd_FP.astype(np.float32), Pv_FP.astype(np.float32),theta_FP.astype(np.float32)
