import os
import numpy as np
from polsartools.utils.proc_utils import process_chunks_parallel
from polsartools.utils.utils import conv2d,time_it,eig22
from .dxp_infiles import dxpc2files, S_norm
[docs]
@time_it
def powers_dp_grd(cpFile,xpFile, method=1, 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
):
"""
This function computes the scattering power components for GRD (only intensity no phase) dual-pol SAR data (decomposition/factorization based approach)
Examples
--------
>>> # Basic usage with default parameters
>>> powers_dp_grd("/path/to/copol_file.tif", "/path/to/crosspol_file.tif")
>>> # Advanced usage with custom parameters
>>> powers_dp_grd(
... cpFile="/path/to/copol_file.tif",
... xpFile="/path/to/crosspol_file.tif",
... method=2,
... win=3,
... fmt="tif",
... cog=True,
... block_size=(1024, 1024)
... )
Parameters
----------
cpFile : str
Path to the co-polarized backscatter (linear) SAR raster file.
xpFile : str
Path to the cross-polarized backscatter (linear) SAR raster file.
method : int
1: Decomposition based powers
2: Factorisation based powers
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 a Cloud Optimized GeoTIFF (COG) 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
"""
write_flag=True
input_filepaths = [cpFile,xpFile]
output_filepaths = []
if fmt == "bin":
if method==1:
output_filepaths.append(os.path.join(os.path.dirname(cpFile), "Alpha_dp_grd.bin"))
output_filepaths.append(os.path.join(os.path.dirname(cpFile), "Pdl_dcmp_grd.bin"))
output_filepaths.append(os.path.join(os.path.dirname(cpFile), "Psl_dcmp_grd.bin"))
output_filepaths.append(os.path.join(os.path.dirname(cpFile), "Pu_dcmp_grd.bin"))
else:
output_filepaths.append(os.path.join(os.path.dirname(cpFile), "Pdl_fact_grd.bin"))
output_filepaths.append(os.path.join(os.path.dirname(cpFile), "Psl_fact_grd.bin"))
output_filepaths.append(os.path.join(os.path.dirname(cpFile), "Pr_fact_grd.bin"))
else:
if method==1:
output_filepaths.append(os.path.join(os.path.dirname(cpFile), "Alpha_dp_grd.tif"))
output_filepaths.append(os.path.join(os.path.dirname(cpFile), "Pdl_dcmp_grd.tif"))
output_filepaths.append(os.path.join(os.path.dirname(cpFile), "Psl_dcmp_grd.tif"))
output_filepaths.append(os.path.join(os.path.dirname(cpFile), "Pu_dcmp_grd.tif"))
else:
output_filepaths.append(os.path.join(os.path.dirname(cpFile), "Pdl_fact_grd.tif"))
output_filepaths.append(os.path.join(os.path.dirname(cpFile), "Psl_fact_grd.tif"))
output_filepaths.append(os.path.join(os.path.dirname(cpFile), "Pr_fact_grd.tif"))
process_chunks_parallel(input_filepaths, list(output_filepaths), win, write_flag,
process_chunk_dp_powers,
*[method],
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_dp_powers(chunks, window_size,*args, **kwargs):
method = int(args[-1])
kernel = np.ones((window_size,window_size),np.float32)/(window_size*window_size)
c11 = np.array(chunks[0])
c22 = np.array(chunks[1])
# def S_norm(S_array):
# S_5 = np.nanpercentile(S_array, 2)
# S_95 = np.nanpercentile(S_array, 98)
# S_cln = np.where(S_array > S_95, S_95, S_array)
# S_cln = np.where(S_cln < S_5, S_5, S_cln)
# S_cln_max = np.nanmax(S_cln)
# S_norm_array = np.divide(S_cln,S_cln_max)
# return S_norm_array
if window_size>1:
c11 = conv2d(c11,kernel)
c22 = conv2d(c22,kernel)
s0 = np.abs(c11 + c22)
s1 = np.abs(c11 - c22)
C11_av_db = 10*np.log10(c11)
prob1 = c11/(c11 + c22)
prob2 = c22/(c11 + c22)
ent = -prob1*np.log2(prob1) - prob2*np.log2(prob2)
s1_s_norm = S_norm(s1) #This is S1 normalzied for DpRSI, does not include slope mask
C11_norm = S_norm(c11)
C22_norm = S_norm(c22)
dop = (c11 - c22)/(c11 + c22)
dop = np.abs(dop)
beta = prob1
##### Power Calculation
dprbi = np.sqrt(np.square(C11_norm) + np.square(C22_norm))/np.sqrt(2)
dprbi = dprbi*s1_s_norm
dprsi_con1 = (1 - ent)*np.sqrt(1 - np.square(s1_s_norm)) # For Valid pixels
dprsi_con2 = np.sqrt(1 - np.square(s1_s_norm)) # For Noise pixels
NESZ = -16 ## For Sentinel-1
dprsi = np.where(C11_av_db > NESZ, dprsi_con1, dprsi_con2)
shp = np.shape(dprbi)
if method==1:
alpha1 = np.arctan2(dprbi, 1 - dprbi)
alpha1 = np.degrees(alpha1)
alpha2 = np.arctan2(1-dprsi, dprsi)
alpha2 = np.degrees(alpha2)
alpha_dp = (alpha1 + alpha2)/2; #Dual-pol target characteristic parameter proposed in Verma et al. 2024
## Alpha as geomteric factor
alpha_dp_rad = np.radians(2*alpha_dp)
cos_a = np.cos(alpha_dp_rad)
## Power components for valid pixels (VV > NESZ)
Pu_v = (1 - dop)*s0
Pd_v = (1/2)*dop*s0*(1 - cos_a)
Ps_v = (1/2)*dop*s0*(1 + cos_a)
## Power components for noise pixels (VV < NESZ)
Pu_n = (1 - beta)*s0
Pd_n = (1/2)*beta*s0*(1 - cos_a)
Ps_n = (1/2)*beta*s0*(1 + cos_a)
## Dual-pol scattering power
Pu = np.where(C11_av_db > NESZ, Pu_v, Pu_n) # Unpolized power
Pd = np.where(C11_av_db > NESZ, Pd_v, Pd_n) # "Dihedral-like" power
Ps = np.where(C11_av_db > NESZ, Ps_v, Ps_n) # "Surface-like" power
return alpha_dp.astype(np.float32), Pd.astype(np.float32), Ps.astype(np.float32), Pu.astype(np.float32)
else:
dprbi_flt = dprbi.flatten()
dprsi_flt = dprsi.flatten()
shp_flt = np.shape(dprbi_flt)
indices_vec = np.array([dprsi_flt, dprbi_flt]).transpose()
indices_vec_sort = np.array([[max(row), min(row)] for row in indices_vec])
y1 = indices_vec_sort[:,0] #First dominant
y2 = (1 - indices_vec_sort[:,0])*indices_vec_sort[:,1] #Second dominant
residue = 1 - (y1 + y2)
dprsi_dom = np.where(dprsi_flt > dprbi_flt)[0] #Keeps the tuple where dprsi is dominant
dprbi_dom = np.where(dprsi_flt < dprbi_flt)[0] #Keeps the tuple where dprbi is dominant
## Surface-like power component
Ps = np.zeros(shp_flt)
##dprsi_dom and dprbi_dom are not dprsi and dprbi values, they just indicate tuples (pixel) for which they are greater
Ps[dprsi_dom] = y1[dprsi_dom] #In these tuples dprsi was dominant, hence taking y1 (first dominant)
Ps[dprbi_dom] = y2[dprbi_dom] #In these tuples dprbi was dominant, hence taking y2 (Second domiant)
Ps = Ps.reshape(shp[0],shp[1])
Ps = np.multiply(s0,Ps)
## Dihedral-like power component
Pd = np.zeros(shp_flt)
Pd[dprbi_dom] = y1[dprbi_dom]
Pd[dprsi_dom] = y2[dprsi_dom]
Pd = Pd.reshape(shp[0],shp[1])
Pd = np.multiply(s0,Pd)
## Residue (diffused) power component
Pr = residue.reshape(shp[0],shp[1])
Pr = np.multiply(s0,Pr)
return Pd.astype(np.float32), Ps.astype(np.float32), Pr.astype(np.float32)
