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 dprbic(cpFile,xpFile, 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
):
"""Compute dual-pol Radar Built-up Index (DpRBIc) from Dual-pol GRD data.
This function calculates the DpRBIc using co-polarized (`cpFile`) and cross-polarized (`xpFile`) SAR raster files.
Examples
--------
>>> # Basic usage with default parameters
>>> dprbic("/path/to/copol_file.tif", "/path/to/crosspol_file.tif")
>>> # Advanced usage with custom parameters
>>> dprbic(
... cpFile="/path/to/copol_file.tif",
... xpFile="/path/to/crosspol_file.tif",
... 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.
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
Results are written to disk as either 'DpRBIc.tif' or 'DpRBIc.bin'
in the input folder.
"""
write_flag=True
input_filepaths = [cpFile,xpFile]
output_filepaths = []
if fmt == "bin":
output_filepaths.append(os.path.join(os.path.dirname(cpFile), "DpRBIc.bin"))
else:
output_filepaths.append(os.path.join(os.path.dirname(cpFile), "DpRBIc.tif"))
process_chunks_parallel(input_filepaths, list(output_filepaths), window_size=win, write_flag=write_flag,
processing_func=process_chunk_dprbic,block_size=block_size, max_workers=max_workers, num_outputs=1,
cog=cog,ovr=ovr, comp=comp,
progress_callback=progress_callback
)
def process_chunk_dprbic(chunks, window_size,*args):
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)
# prob1 = c11/(c11 + c22)
# prob2 = c22/(c11 + c22)
# ent = -prob1*np.log2(prob1) - prob2*np.log2(prob2)
s1 = np.abs(c11-c22)
C11_norm = S_norm(c11)
C22_norm = S_norm(c22)
s1_norm = S_norm(s1)
dprbic = np.sqrt(np.square(C11_norm) + np.square(C22_norm))/np.sqrt(2)
dprbic = dprbic*s1_norm
return dprbic.astype(np.float32)
