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
[docs]
@time_it
def dprvi(infolder, window_size=1, outType="tif", cog_flag=False,
cog_overviews = [2, 4, 8, 16], write_flag=True,
max_workers=None,block_size=(512, 512),
progress_callback=None, # for QGIS plugin
):
"""Compute dual-pol Radar Vegetation Index (DpRVI) from C2 matrix data.
This function processes dual-polarization SAR data to generate the DpRVI, which is useful
for vegetation monitoring and biomass estimation. The processing is done in parallel
blocks for improved performance.
Examples
--------
>>> # Basic usage with default parameters
>>> dprvi("/path/to/c2_data")
>>> # Advanced usage with custom parameters
>>> dprvi(
... infolder="/path/to/c2_data",
... window_size=3,
... outType="tif",
... cog_flag=True,
... block_size=(1024, 1024)
... )
Parameters
----------
infolder : str
Path to the input folder containing C2 matrix files.
window_size : int, default=1
Size of the spatial averaging window. Larger windows reduce speckle noise
but decrease spatial resolution.
outType : {'tif', 'bin'}, default='tif'
Output file format:
- 'tif': GeoTIFF format with georeferencing information
- 'bin': Raw binary format
cog_flag : bool, default=False
If True, creates a Cloud Optimized GeoTIFF (COG) with internal tiling
and overviews for efficient web access.
cog_overviews : list[int], default=[2, 4, 8, 16]
Overview levels for COG creation. Each number represents the
decimation factor for that overview level.
write_flag : bool, default=True
If True, writes results to disk. If False, only processes data in memory.
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 'dprvi.tif' or 'dprvi.bin'
in the input folder.
"""
input_filepaths = dxpc2files(infolder)
output_filepaths = []
if outType == "bin":
output_filepaths.append(os.path.join(infolder, "dprvi.bin"))
else:
output_filepaths.append(os.path.join(infolder, "dprvi.tif"))
process_chunks_parallel(input_filepaths, list(output_filepaths), window_size=window_size, write_flag=write_flag,
processing_func=process_chunk_dprvi,block_size=block_size, max_workers=max_workers, num_outputs=1,
cog_flag=cog_flag,
cog_overviews=cog_overviews,
progress_callback=progress_callback
)
def process_chunk_dprvi(chunks, window_size,*args):
kernel = np.ones((window_size,window_size),np.float32)/(window_size*window_size)
c11_T1 = np.array(chunks[0])
c12_T1 = np.array(chunks[1])+1j*np.array(chunks[2])
c21_T1 = np.conj(c12_T1)
c22_T1 = np.array(chunks[3])
if window_size>1:
c11s = conv2d(np.real(c11_T1),kernel)+1j*conv2d(np.imag(c11_T1),kernel)
c12s = conv2d(np.real(c12_T1),kernel)+1j*conv2d(np.imag(c12_T1),kernel)
c21s = conv2d(np.real(c21_T1),kernel)+1j*conv2d(np.imag(c21_T1),kernel)
c22s = conv2d(np.real(c22_T1),kernel)+1j*conv2d(np.imag(c22_T1),kernel)
c2_det = (c11s*c22s-c12s*c21s)
c2_trace = c11s+c22s
# t2_span = t11s*t22s
m = (np.sqrt(1.0-(4.0*c2_det/np.power(c2_trace,2))))
egv1,egv2 = eig22(np.dstack([c11s,c12s,c21s,c22s]))
egf = np.vstack([egv1,egv2])
egfmax = egf.max(axis=0)#.reshape(np.shape(C2_stack[:,:,0]))
beta = (egfmax/(egv1+egv2)).reshape(np.shape(c11s))
dprvi = np.real(1-(m*beta))
else:
c2_det = (c11_T1*c22_T1-c12_T1*c21_T1)
c2_trace = c11_T1+c22_T1
m = np.real(np.sqrt(1.0-(4.0*c2_det/np.power(c2_trace,2))))
egv1,egv2 = eig22(np.dstack([c11_T1,c12_T1,c21_T1,c22_T1]))
egf = np.vstack([egv1,egv2])
egfmax = egf.max(axis=0)#.reshape(np.shape(C2_stack[:,:,0]))
beta = (egfmax/(egv1+egv2)).reshape(np.shape(c11_T1))
dprvi = np.real(1-(m*beta))
return dprvi.astype(np.float32)
