import numpy as np
from osgeo import gdal
gdal.UseExceptions()
import os,tempfile,shutil
import xml.etree.ElementTree as ET
from polsartools.utils.utils import time_it
# from polsartools.utils.io_utils import write_T3, write_C3
from polsartools.preprocess.convert_S2 import convert_S
def read_rs2_tif(file):
ds = gdal.Open(file)
band1 = ds.GetRasterBand(1).ReadAsArray()
band2 = ds.GetRasterBand(2).ReadAsArray()
ds=None
return np.dstack((band1,band2))
# def write_s2_bin(file,wdata):
# [cols, rows] = wdata.shape
# driver = gdal.GetDriverByName("ENVI")
# outdata = driver.Create(file, rows, cols, 1, gdal.GDT_CFloat32)
# outdata.SetDescription(file)
# outdata.GetRasterBand(1).WriteArray(wdata)
# outdata.FlushCache()
def write_rst(out_file,data,
driver='GTiff', out_dtype=gdal.GDT_CFloat32,
mat=None,
cog=False, ovr=[2, 4, 8, 16], comp=False
):
if driver =='ENVI':
# Create GDAL dataset
driver = gdal.GetDriverByName(driver)
dataset = driver.Create(
out_file,
data.shape[1],
data.shape[0],
1,
out_dtype
)
else:
driver = gdal.GetDriverByName("GTiff")
options = ['BIGTIFF=IF_SAFER']
if comp:
# options += ['COMPRESS=DEFLATE', 'PREDICTOR=2', 'ZLEVEL=9']
options += ['COMPRESS=LZW']
if cog:
options += ['TILED=YES', 'BLOCKXSIZE=512', 'BLOCKYSIZE=512']
dataset = driver.Create(
out_file,
data.shape[1],
data.shape[0],
1,
out_dtype,
options
)
dataset.GetRasterBand(1).WriteArray(data)
# outdata.GetRasterBand(1).SetNoDataValue(0)##if you want these values transparent
dataset.FlushCache() ##saves to disk!!
if cog:
dataset.BuildOverviews("NEAREST", ovr)
dataset = None
if mat == 'S2' or mat == 'Sxy':
print(f"Saved file: {out_file}")
# def write_rst(file,wdata,dtype,cog=False, ovr=[2, 4, 8, 16], comp=False):
# [cols, rows] = wdata.shape
# if '.bin' in file:
# driver = gdal.GetDriverByName("ENVI")
# outdata = driver.Create(file, rows, cols, 1, dtype)
# else:
# driver = gdal.GetDriverByName("GTiff")
# options = ['BIGTIFF=IF_SAFER']
# if comp:
# # options += ['COMPRESS=DEFLATE', 'PREDICTOR=2', 'ZLEVEL=9']
# options += ['COMPRESS=LZW']
# if cog:
# options += ['TILED=YES', 'BLOCKXSIZE=512', 'BLOCKYSIZE=512']
# outdata = driver.Create(file, rows, cols, 1, dtype, options)
# outdata.SetDescription(file)
# outdata.GetRasterBand(1).WriteArray(wdata)
# outdata.FlushCache()
# outdata=None
[docs]
@time_it
def rs2_fp(in_dir,mat='T3',
azlks=8,rglks=2,fmt='tif',
cog=False,ovr = [2, 4, 8, 16],comp=False,
bsc='sigma0', out_dir = None,
recip=False,
):
"""
Process radarsat-2 image data and generate the specified matrix (S2, T3, or C3) from the input imagery files.
This function reads radarsat-2 image data in the form of .tif files (HH, HV, VH, VV) from the input folder (`in_dir`)
and calculates either the S2, T3, or C3 matrix. The resulting matrix is then saved in a corresponding directory
(`S2`, `T3`, or `C3`). The function uses lookup tables (`lutSigma.xml`, `lutGamma.xml`, `lutBeta.xml`) for scaling
the data based on the chosen backscatter coefficient `bsc` (sigma0, gamma0, or beta0). The processed data is written into binary files
in the output folder.
Example Usage:
--------------
To process imagery and generate a T3 matrix:
.. code-block:: python
rs2_fp("/path/to/data", mat="T3", bsc="sigma0")
To process imagery and generate a C3 matrix:
.. code-block:: python
rs2_fp("/path/to/data", mat="C3", bsc="beta0", azlks=10, rglks=3)
Parameters:
-----------
in_dir : str
Path to the folder containing the Radarsat-2 files and the lookup tables (`lutSigma.xml`, `lutGamma.xml`, `lutBeta.xml`).
mat : str, optional (default='T3')
Type of matrix to extract. Valid options: 'S2', 'C4, 'C3', 'T4',
'T3', 'C2HX', 'C2VX', 'C2HV','T2HV'
azlks : int, optional (default=8)
The number of azimuth looks to apply during the C/T processing.
rglks : int, optional (default=2)
The number of range looks to apply during the C/Tprocessing.
fmt : {'tif', 'bin'}, optional (default='tif')
Output format:
- 'tif': GeoTIFF
- 'bin': Raw binary format
cog : bool, optional (default=False)
If True, outputs will be saved as Cloud Optimized GeoTIFFs with internal tiling and overviews.
ovr : list of int, optional (default=[2, 4, 8, 16])
Overview levels for COG generation. Ignored if cog=False.
comp : bool, optional (default=False)
If True, applies LZW compression to GeoTIFF outputs.
bsc : str, optional (default='sigma0')
The type of radar cross-section to use for scaling. Available options:
- 'sigma0' : Uses `lutSigma.xml` for scaling.
- 'gamma0' : Uses `lutGamma.xml` for scaling.
- 'beta0' : Uses `lutBeta.xml` for scaling.
out_dir : str or None, optional (default=None)
Directory to save output files. If None, a folder named after the matrix type will be created
in the same location as the input file.
recip : bool, optional (default=False)
If True, scattering matrix reciprocal symmetry is applied, i.e, S_HV = S_VH.
"""
valid_full_pol = ['S2', 'C4', 'C3', 'T4', 'T3', 'C2HX', 'C2VX', 'C2HV', 'T2HV']
# valid_dual_pol = ['Sxy', 'C2', 'T2']
valid_matrices = valid_full_pol
if mat not in valid_matrices:
raise ValueError(f"Invalid matrix type '{mat}'. \n Supported types are:\n"
f" Full-pol: {sorted(valid_full_pol)}\n")
if bsc == 'sigma0':
tree = ET.parse(os.path.join(in_dir,"lutSigma.xml"))
root = tree.getroot()
lut = root.find('gains').text.strip()
lut = np.fromstring(lut, sep=' ')
elif bsc == 'gamma0':
tree = ET.parse(os.path.join(in_dir,"lutGamma.xml"))
root = tree.getroot()
lut = root.find('gains').text.strip()
lut = np.fromstring(lut, sep=' ')
elif bsc=='beta0':
tree = ET.parse(os.path.join(in_dir,"lutBeta.xml"))
root = tree.getroot()
lut = root.find('gains').text.strip()
lut = np.fromstring(lut, sep=' ')
else:
raise ValueError(f'Unknown type {bsc} \n Available bsc: sigma0,gamma0,beta0')
temp_dir = None
ext = 'bin' if fmt == 'bin' else 'tif'
driver = 'ENVI' if fmt == 'bin' else None
# Final output directory
if out_dir is None:
final_out_dir = os.path.join(in_dir, mat)
else:
final_out_dir = os.path.join(out_dir, mat)
os.makedirs(final_out_dir, exist_ok=True)
# Intermediate output directory
if mat in ['S2', 'Sxy']:
base_out_dir = final_out_dir
else:
temp_dir = tempfile.mkdtemp(prefix='temp_S2_')
base_out_dir = temp_dir
inFile = os.path.join(in_dir,"imagery_HH.tif")
S11 = read_rs2_tif(inFile)
write_rst(os.path.join(base_out_dir, f's11.{ext}'),
S11[:,:,0]/lut+1j*(S11[:,:,1]/lut),
driver=driver, mat=mat, cog=cog, ovr=ovr, comp=comp)
del S11
inFile = os.path.join(in_dir,"imagery_HV.tif")
S12 = read_rs2_tif(inFile)
inFile = os.path.join(in_dir,"imagery_VH.tif")
S21 = read_rs2_tif(inFile)
if recip:
S12 = (S12 + S21)/2
S21 = S12
write_rst(os.path.join(base_out_dir, f's12.{ext}'),
S12[:,:,0]/lut+1j*(S12[:,:,1]/lut),
driver=driver, mat=mat, cog=cog, ovr=ovr, comp=comp)
del S12
write_rst(os.path.join(base_out_dir, f's21.{ext}'),
S21[:,:,0]/lut+1j*(S21[:,:,1]/lut),
driver=driver, mat=mat, cog=cog, ovr=ovr, comp=comp)
del S21
inFile = os.path.join(in_dir,"imagery_VV.tif")
S22 = read_rs2_tif(inFile)
write_rst(os.path.join(base_out_dir, f's22.{ext}'),
S22[:,:,0]/lut+1j*(S22[:,:,1]/lut),
driver=driver, mat=mat, cog=cog, ovr=ovr, comp=comp)
del S22
# Matrix conversion if needed
if mat not in ['S2', 'Sxy']:
convert_S(base_out_dir, mat=mat, azlks=azlks, rglks=rglks, cf=1,
fmt=fmt, out_dir=final_out_dir, cog=cog, ovr=ovr, comp=comp)
# Clean up temp directory
if temp_dir:
try:
shutil.rmtree(temp_dir)
except Exception as e:
print(f"Warning: Could not delete temporary directory {temp_dir}: {e}")
# if mat == 'S2':
# if out_dir is None:
# out_dir = os.path.join(in_dir,"S2")
# else:
# out_dir = os.path.join(out_dir,"S2")
# os.makedirs(out_dir,exist_ok=True)
# print("Considering S12 = S21")
# inFile = os.path.join(in_dir,"imagery_HH.tif")
# data = read_rs2_tif(inFile)
# if fmt=='bin':
# out_file = os.path.join(out_dir,'s11.bin')
# write_rst(out_file,data[:,:,0]/lut+1j*(data[:,:,1]/lut),gdal.GDT_CFloat32)
# else:
# out_file = os.path.join(out_dir,'s11.tif')
# write_rst(out_file,data[:,:,0]/lut+1j*(data[:,:,1]/lut),gdal.GDT_CFloat32,cog= cog, ovr=ovr, comp=comp)
# print("Saved file "+out_file)
# rows,cols,_ = data.shape
# inFile = os.path.join(in_dir,"imagery_HV.tif")
# data_xy = read_rs2_tif(inFile)
# inFile = os.path.join(in_dir,"imagery_VH.tif")
# data_yx = read_rs2_tif(inFile)
# if recip:
# data = (data_xy+data_yx)*0.5
# del data_xy,data_yx
# if fmt=='bin':
# out_file = os.path.join(out_dir,'s12.bin')
# write_rst(out_file,data[:,:,0]/lut+1j*(data[:,:,1]/lut),gdal.GDT_CFloat32)
# print("Saved file "+out_file)
# out_file = os.path.join(out_dir,'s21.bin')
# write_rst(out_file,data[:,:,0]/lut+1j*(data[:,:,1]/lut),gdal.GDT_CFloat32,cog= cog, ovr=ovr, comp=comp)
# print("Saved file "+out_file)
# else:
# out_file = os.path.join(out_dir,'s12.tif')
# write_rst(out_file,data[:,:,0]/lut+1j*(data[:,:,1]/lut),gdal.GDT_CFloat32)
# print("Saved file "+out_file)
# out_file = os.path.join(out_dir,'s21.tif')
# write_rst(out_file,data[:,:,0]/lut+1j*(data[:,:,1]/lut),gdal.GDT_CFloat32,cog= cog, ovr=ovr, comp=comp)
# print("Saved file "+out_file)
# else:
# if fmt=='bin':
# out_file = os.path.join(out_dir,'s12.bin')
# write_rst(out_file,data_xy[:,:,0]/lut+1j*(data_xy[:,:,1]/lut),gdal.GDT_CFloat32)
# print("Saved file "+out_file)
# out_file = os.path.join(out_dir,'s21.bin')
# write_rst(out_file,data_yx[:,:,0]/lut+1j*(data_yx[:,:,1]/lut),gdal.GDT_CFloat32,cog= cog, ovr=ovr, comp=comp)
# print("Saved file "+out_file)
# else:
# out_file = os.path.join(out_dir,'s12.tif')
# write_rst(out_file,data_xy[:,:,0]/lut+1j*(data_xy[:,:,1]/lut),gdal.GDT_CFloat32)
# print("Saved file "+out_file)
# out_file = os.path.join(out_dir,'s21.tif')
# write_rst(out_file,data_yx[:,:,0]/lut+1j*(data_yx[:,:,1]/lut),gdal.GDT_CFloat32,cog= cog, ovr=ovr, comp=comp)
# print("Saved file "+out_file)
# inFile = os.path.join(in_dir,"imagery_VV.tif")
# data = read_rs2_tif(inFile)
# if fmt=='bin':
# out_file = os.path.join(out_dir,'s22.bin')
# write_rst(out_file,data[:,:,0]/lut+1j*(data[:,:,1]/lut),gdal.GDT_CFloat32)
# print("Saved file "+out_file)
# else:
# out_file = os.path.join(out_dir,'s22.tif')
# write_rst(out_file,data[:,:,0]/lut+1j*(data[:,:,1]/lut),gdal.GDT_CFloat32,cog= cog, ovr=ovr, comp=comp)
# print("Saved file "+out_file)
# # out_file = os.path.join(out_dir,'s22.bin')
# # write_s2_bin(out_file,data[:,:,0]/lut+1j*(data[:,:,1]/lut))
# # print("Saved file "+out_file)
# file = open(out_dir +'/config.txt',"w+")
# file.write('Nrow\n%d\n---------\nNcol\n%d\n---------\nPolarCase\nmonostatic\n---------\nPolarType\nfull'%(rows,cols))
# file.close()
# elif mat == 'T3':
# # print("Considering S12 = S21")
# # Kp- 3-D Pauli feature vector
# # Kp = (1/np.sqrt(2))*np.array([S2[0,0]+S2[1,1], S2[0,0]-S2[1,1], S2[1,0]])
# # Kp = (1/np.sqrt(2))*np.array([S2[0,0]+S2[1,1], S2[0,0]-S2[1,1], S2[0,1]])
# inFile = os.path.join(in_dir,"imagery_HH.tif")
# data = read_rs2_tif(inFile)
# s11 = data[:,:,0]/lut+1j*(data[:,:,1]/lut)
# inFile = os.path.join(in_dir,"imagery_HV.tif")
# data_xy = read_rs2_tif(inFile)
# inFile = os.path.join(in_dir,"imagery_VH.tif")
# data_yx = read_rs2_tif(inFile)
# # Symmetry assumption
# data = (data_xy+data_yx)*0.5
# del data_xy,data_yx
# s12 = data[:,:,0]/lut+1j*(data[:,:,1]/lut)
# inFile = os.path.join(in_dir,"imagery_VV.tif")
# data = read_rs2_tif(inFile)
# s22 = data[:,:,0]/lut+1j*(data[:,:,1]/lut)
# Kp = (1/np.sqrt(2))*np.array([s11+s22, s11-s22, 2*s12])
# del s11,s12,s22
# # 3x3 Pauli Coherency Matrix elements
# T11 = mlook_arr(np.abs(Kp[0])**2,azlks,rglks).astype(np.float32)
# T22 = mlook_arr(np.abs(Kp[1])**2,azlks,rglks).astype(np.float32)
# T33 = mlook_arr(np.abs(Kp[2])**2,azlks,rglks).astype(np.float32)
# T12 = mlook_arr(Kp[0]*np.conj(Kp[1]),azlks,rglks).astype(np.complex64)
# T13 = mlook_arr(Kp[0]*np.conj(Kp[2]),azlks,rglks).astype(np.complex64)
# T23 = mlook_arr(Kp[1]*np.conj(Kp[2]),azlks,rglks).astype(np.complex64)
# del Kp
# if out_dir is None:
# out_dir = os.path.join(in_dir,"T3")
# else:
# out_dir = os.path.join(out_dir,"T3")
# os.makedirs(out_dir,exist_ok=True)
# # T3Folder = os.path.join(in_dir,'T3')
# # if not os.path.isdir(T3Folder):
# # print("T3 folder does not exist. \nCreating folder {}".format(T3Folder))
# # os.mkdir(T3Folder)
# # write_T3(np.dstack([T11,T12,T13,np.conjugate(T12),T22,T23,np.conjugate(T13),np.conjugate(T23),T33]),T3Folder)
# write_T3([np.real(T11),np.real(T12),np.imag(T12),np.real(T13),np.imag(T13),
# np.real(T22),np.real(T23),np.imag(T23),
# np.real(T33)],out_dir)
# elif mat == 'C3':
# # print("Considering S12 = S21")
# inFile = os.path.join(in_dir,"imagery_HH.tif")
# data = read_rs2_tif(inFile)
# s11 = data[:,:,0]/lut+1j*(data[:,:,1]/lut)
# inFile = os.path.join(in_dir,"imagery_HV.tif")
# data_xy = read_rs2_tif(inFile)
# inFile = os.path.join(in_dir,"imagery_VH.tif")
# data_yx = read_rs2_tif(inFile)
# # Symmetry assumption
# data = (data_xy+data_yx)*0.5
# del data_xy,data_yx
# s12 = data[:,:,0]/lut+1j*(data[:,:,1]/lut)
# inFile = os.path.join(in_dir,"imagery_VV.tif")
# data = read_rs2_tif(inFile)
# s22 = data[:,:,0]/lut+1j*(data[:,:,1]/lut)
# # Kl- 3-D Lexicographic feature vector
# Kl = np.array([s11, np.sqrt(2)*s12, s22])
# del s11,s12,s22
# # 3x3 COVARIANCE Matrix elements
# C11 = mlook_arr(np.abs(Kl[0])**2,azlks,rglks).astype(np.float32)
# C22 = mlook_arr(np.abs(Kl[1])**2,azlks,rglks).astype(np.float32)
# C33 = mlook_arr(np.abs(Kl[2])**2,azlks,rglks).astype(np.float32)
# C12 = mlook_arr(Kl[0]*np.conj(Kl[1]),azlks,rglks).astype(np.complex64)
# C13 = mlook_arr(Kl[0]*np.conj(Kl[2]),azlks,rglks).astype(np.complex64)
# C23 = mlook_arr(Kl[1]*np.conj(Kl[2]),azlks,rglks).astype(np.complex64)
# if out_dir is None:
# out_dir = os.path.join(in_dir,"C3")
# else:
# out_dir = os.path.join(out_dir,"C3")
# os.makedirs(out_dir,exist_ok=True)
# # C3Folder = os.path.join(in_dir,'C3')
# # if not os.path.isdir(C3Folder):
# # print("C3 folder does not exist. \nCreating folder {}".format(C3Folder))
# # os.mkdir(C3Folder)
# # write_C3(np.dstack([C11,C12,C13,np.conjugate(C12),C22,C23,np.conjugate(C13),np.conjugate(C23),C33]),C3Folder)
# write_C3([np.real(C11),np.real(C12),np.imag(C12),np.real(C13),np.imag(C13),
# np.real(C22),np.real(C23),np.imag(C23),
# np.real(C33)],out_dir)
# else:
# raise ValueError('Invalid matrix type. Valid types are "S2", "T3" and "C3"')
