Dear Kinjal Prajapati ,

For Matlab, you could look into these lines to read the vv polarized file in .grd or .mlc format or any other format. The "multibandread" function reads the binary GRD file of UAVSAR quad-pol data.

% File path to your GRD file

file_path = 'datapath.grd';

% Define the data dimensions (modify or set it in accordance to your dataset) for example

rows = ; % Number of rows

cols = ; % Number of columns

bands = ; % Number of polarizations (4 for quad-pol)

% Then, define the data type of each band (modify it according to your data)

data_type = 'single'; % Assuming single precision floating point data

% Open the file for reading

fid = fopen(file_path, 'r');

% Read the data

sar_data = multibandread(file_path, [rows, cols, bands], data_type, 0, 'bsq', 'ieee-le');

% Close the file

fclose(fid);

% Display the size of the data array

disp(size(sar_data));

The rest of the simulation can be coded as you did. I am not sure of the same approach in Python. I hope this Matlab version helps.

Shafik

import pygmt

def read_surfer_grd(filename):

grid = pygmt.load_dataarray(filename)

return grid

# Example usage

filename = 'yourfile.grd'

grid_data = read_surfer_grd(filename)

print(grid_data)

from osgeo import gdal

def read_grd(filename):

dataset = gdal.Open(filename)

if dataset is None:

print("Could not open", filename)

return None

band = dataset.GetRasterBand(1)

data = band.ReadAsArray()

projection = dataset.GetProjection()

geotransform = dataset.GetGeoTransform()

return data, projection, geotransform

def basic_radiometric_correction(data):

# Perform basic radiometric correction here

# This can include calibration, normalization, etc.

# Example: data = (data - data.min()) / (data.max() - data.min())

return data

# Example usage

filename = "your_file.grd"

data, projection, geotransform = read_grd(filename)

if data is not None:

# Perform basic radiometric correction

data_corrected = basic_radiometric_correction(data)

# Now you can use the corrected data for further analysis or visualization

import numpy as np # Replace with your file pathfilename = "path/to/uavsar.grd" # Read data based on file format (e.g., data type, number of elements) with open(filename, "rb") as f: data = np.fromfile(f, dtype=np.float32) # Adjust data type as needed # Reshape data based on number of rows, columns, and polarization channels # (Information on these might be in a separate file or require manual parsing)data = data.reshape((rows, columns, num_channels)) # Access specific polarization channel (e.g., VV)vv_channel = data[:, :, 0] # Assuming VV is the first channel (adjust indexing) # Further processing and analysis of the data