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, C3_T3_mat
from .fp_infiles import fp_c3t3files
from polsartools.yam4cpp import process_chunk_yam4cpp
[docs]
@time_it
def yamaguchi_4c(in_dir, model="", 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 Yamaguchi 4-Component Decomposition for full-pol SAR data.
This function implements the Yamaguchi 4-component decomposition with three
different model options: original (Y4O), rotation-corrected (Y4R), and
extended volume scattering model (Y4S). The decomposition separates the total
power into surface, double-bounce, volume, and helix scattering components.
Examples
--------
>>> # Original Yamaguchi decomposition
>>> yamaguchi_4c("/path/to/fullpol_data")
>>> # Rotation-corrected decomposition
>>> yamaguchi_4c(
... in_dir="/path/to/fullpol_data",
... model="y4cr",
... win=5,
... fmt="tif",
... cog=True
... )
>>> # Extended volume model decomposition
>>> yamaguchi_4c(
... in_dir="/path/to/fullpol_data",
... model="y4cs",
... win=5
... )
Parameters
----------
in_dir : str
Path to the input folder containing full-pol T3 or C3 matrix files.
model : {'', 'y4cr', 'y4cs'}, default=''
Decomposition model to use:
- '': Original Yamaguchi 4-component (Y4O)
- 'y4cr': Rotation-corrected Yamaguchi (Y4R)
- 'y4cs': Extended volume scattering model (Y4S)
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 for the selected model:
1. {prefix}_odd: Surface scattering power
2. {prefix}_dbl: Double-bounce scattering power
3. {prefix}_vol: Volume scattering power
4. {prefix}_hlx: Helix scattering power
where prefix is 'Yam4co', 'Yam4cr', or 'Yam4csr' based on model choice.
"""
write_flag=True
input_filepaths = fp_c3t3files(in_dir)
output_filepaths = []
if fmt == "bin":
if not model or model=="y4co":
output_filepaths.append(os.path.join(in_dir, "Yam4co_odd.bin"))
output_filepaths.append(os.path.join(in_dir, "Yam4co_dbl.bin"))
output_filepaths.append(os.path.join(in_dir, "Yam4co_vol.bin"))
output_filepaths.append(os.path.join(in_dir, "Yam4co_hlx.bin"))
elif model=="y4cr":
output_filepaths.append(os.path.join(in_dir, "Yam4cr_odd.bin"))
output_filepaths.append(os.path.join(in_dir, "Yam4cr_dbl.bin"))
output_filepaths.append(os.path.join(in_dir, "Yam4cr_vol.bin"))
output_filepaths.append(os.path.join(in_dir, "Yam4cr_hlx.bin"))
elif model=="y4cs":
output_filepaths.append(os.path.join(in_dir, "Yam4csr_odd.bin"))
output_filepaths.append(os.path.join(in_dir, "Yam4csr_dbl.bin"))
output_filepaths.append(os.path.join(in_dir, "Yam4csr_vol.bin"))
output_filepaths.append(os.path.join(in_dir, "Yam4csr_hlx.bin"))
else:
raise(f"Invalid model!! \n model type argument must be either '' for default (y4co) or Y4R or S4R")
else:
if not model or model=="y4co":
output_filepaths.append(os.path.join(in_dir, "Yam4co_odd.tif"))
output_filepaths.append(os.path.join(in_dir, "Yam4co_dbl.tif"))
output_filepaths.append(os.path.join(in_dir, "Yam4co_vol.tif"))
output_filepaths.append(os.path.join(in_dir, "Yam4co_hlx.tif"))
elif model=="y4cr":
output_filepaths.append(os.path.join(in_dir, "Yam4cr_odd.tif"))
output_filepaths.append(os.path.join(in_dir, "Yam4cr_dbl.tif"))
output_filepaths.append(os.path.join(in_dir, "Yam4cr_vol.tif"))
output_filepaths.append(os.path.join(in_dir, "Yam4cr_hlx.tif"))
elif model=="y4cs":
output_filepaths.append(os.path.join(in_dir, "Yam4csr_odd.tif"))
output_filepaths.append(os.path.join(in_dir, "Yam4csr_dbl.tif"))
output_filepaths.append(os.path.join(in_dir, "Yam4csr_vol.tif"))
output_filepaths.append(os.path.join(in_dir, "Yam4csr_hlx.tif"))
else:
raise(f"Invalid model!! \n model type argument must be either '' for default (y4co) or Y4R or S4R")
process_chunks_parallel(input_filepaths, list(output_filepaths),
win, write_flag,
process_chunk_yam4cfp,
*[model],
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_yam4cfp(chunks, window_size, input_filepaths, *args, **kwargs):
model = args[-1]
# 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]])
C3 = T3_C3_mat(T_T1)
span = C3[0,0].real+C3[1,1].real+C3[2,2].real
del C3
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]])
span = C3[0,0].real+C3[1,1].real+C3[2,2].real
T_T1 = C3_T3_mat(C3)
del 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)
SpanMax = np.nanmax(span)
SpanMin = np.nanmin(span)
eps = 1e-6
SpanMin = np.nanmax([SpanMin, eps])
T_T1 = T_T1.reshape(9, rows, cols)
chunk_arrays = [T_T1[0,:,:],T_T1[1,:,:],T_T1[2,:,:],
T_T1[3,:,:],T_T1[4,:,:],T_T1[5,:,:],T_T1[6,:,:],T_T1[7,:,:],T_T1[8,:,:]]
vi_c_raw = process_chunk_yam4cpp(chunk_arrays, window_size, model,SpanMin, SpanMax)
proc_chunks=[]
for chunk in vi_c_raw:
filt_data = np.array(chunk)
# filt_data[filt_data == 0] = np.nan
proc_chunks.append(filt_data)
# print(np.shape(proc_chunks))
return proc_chunks[0].astype(np.float32), \
proc_chunks[1].astype(np.float32), \
proc_chunks[2].astype(np.float32), \
proc_chunks[3].astype(np.float32),
