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 rvi_fp(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
):
"""Calculate Radar Vegetation Index (RVI) from full-pol SAR data.
This function computes the Radar Vegetation Index (RVI) using full-polarimetric
SAR data. RVI is one of the earliest and most widely used polarimetric vegetation
indices, providing a measure of vegetation density and randomness of scattering.
Examples
--------
>>> # Basic usage with default parameters
>>> rvi_fp("/path/to/fullpol_data")
>>> # Advanced usage with custom parameters
>>> rvi_fp(
... 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 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, 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 one output file to disk:
- 'rvifp.tif' or 'rvifp.bin': RVI values
"""
write_flag=True
input_filepaths = fp_c3t3files(in_dir)
output_filepaths = []
if fmt == "bin":
output_filepaths.append(os.path.join(in_dir, "rvifp.bin"))
else:
output_filepaths.append(os.path.join(in_dir, "rvifp.tif"))
process_chunks_parallel(input_filepaths, list(output_filepaths),
window_size=win, write_flag=write_flag,
processing_func=process_chunk_rvifp,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_rvifp(chunks, window_size,input_filepaths,*args):
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)
eigenvalues = np.linalg.eigvals(reshaped_arr)
sorted_eigenvalues = np.sort(eigenvalues, axis=1)[:, ::-1]
sorted_eigenvalues = sorted_eigenvalues.reshape(T_T1.shape[2], T_T1.shape[3], 3)
p1 = sorted_eigenvalues[:,:,0]/(sorted_eigenvalues[:,:,0] + sorted_eigenvalues[:,:,1] + sorted_eigenvalues[:,:,2])
p2 = sorted_eigenvalues[:,:,1]/(sorted_eigenvalues[:,:,0] + sorted_eigenvalues[:,:,1] + sorted_eigenvalues[:,:,2])
p3 = sorted_eigenvalues[:,:,2]/(sorted_eigenvalues[:,:,0] + sorted_eigenvalues[:,:,1] + sorted_eigenvalues[:,:,2])
p1[p1<0] = 0
p2[p2<0] = 0
p3[p3<0] = 0
p1[p1>1] = 1
p2[p2>1] = 1
p3[p3>1] = 1
rvi = np.real((4*p3)/(p1 + p2 + p3))
# idx = np.argwhere(rvi>1)
# rvi[idx] = (3/4)*rvi[idx]
# rvi[~idx] = rvi[~idx]
# rvi[rvi==0] = np.nan
# rvi = np.real(rvi)
rvi[rvi > 1] *= 0.75
rvi[rvi == 0] = np.nan
rvi = np.real(rvi)
return rvi.astype(np.float32)
