polsartools.polsar.fp.neumann_parm — PolSARtools 0.10 documentation

Skip to main content
`Ctrl`+`K`

# Source code for polsartools.polsar.fp.neumann_parm

    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

    @time_it
    def neumann_parm(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
              ):
        """Perform Neumann Decomposition for full-pol SAR data.

        This function implements the Neumann decomposition for full-polarimetric SAR data,
        extracting four key parameters: polarization orientation angle (psi), degree of
        polarization (delta_mod), phase difference (delta_pha), and helicity (tau).
        This decomposition is particularly useful for characterizing complex scattering
        mechanisms in urban and natural environments.

        Examples
        --------
        >>> # Basic usage with default parameters
        >>> neumann_parm("/path/to/fullpol_data")

        >>> # Advanced usage with custom parameters
        >>> neumann_parm(
        ...     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 Cloud Optimized GeoTIFF (COG) outputs 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 four output files to disk:

            1. Neu_psi: Polarization orientation angle
            2. Neu_delta_mod: Degree of polarization
            3. Neu_delta_pha: Phase difference
            4. Neu_tau: Helicity parameter

        """
        write_flag=True
        input_filepaths = fp_c3t3files(in_dir)

        output_filepaths = []
        if fmt == "bin":
            output_filepaths.append(os.path.join(in_dir, "Neu_psi.bin"))
            output_filepaths.append(os.path.join(in_dir, "Neu_delta_mod.bin"))
            output_filepaths.append(os.path.join(in_dir, "Neu_delta_pha.bin"))
            output_filepaths.append(os.path.join(in_dir, "Neu_tau.bin"))
        else:
            output_filepaths.append(os.path.join(in_dir, "Neu_psi.tif"))
            output_filepaths.append(os.path.join(in_dir, "Neu_delta_mod.tif"))
            output_filepaths.append(os.path.join(in_dir, "Neu_delta_pha.tif"))
            output_filepaths.append(os.path.join(in_dir, "Neu_tau.tif"))

        process_chunks_parallel(input_filepaths, list(output_filepaths),
                                window_size=win, write_flag=write_flag,
                            processing_func=process_chunk_neufp,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_neufp(chunks, window_size, input_filepaths, *args):

        # additional_arg1 = args[0] if len(args) > 0 else None
        # additional_arg2 = args[1] if len(args) > 1 else None

        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]])

        _,_,rows,cols = np.shape(T_T1)
        T_T1 = T_T1.reshape(9, rows, cols)

        # Indices for vectorized access
        i, j = np.indices((rows, cols))

        # Extract components of T3_stack once
        T3_stack_real = np.real(T_T1[:,i, j ])
        T3_stack_imag = np.imag(T_T1[:, i, j])

        T_00_0 = T3_stack_real[0, :, :]
        T_01_0 = T3_stack_real[1, :, :]
        T_01_1 = T3_stack_imag[1, :, :]
        T_02_0 = T3_stack_real[2, :, :]
        T_02_1 = T3_stack_imag[2, :, :]
        T_11_0 = T3_stack_real[4, :, :]
        T_12_0 = T3_stack_real[5, :, :]
        T_22_0 = T3_stack_real[8, :, :]

        # Compute the Phi value using the given formula
        Phi = 0.25 * (np.pi + np.arctan2(-2. * T_12_0, T_22_0 - T_11_0))

        # Adjust Phi for the correct orientation
        Phi[Phi <= np.pi / 4.] = Phi[Phi <= np.pi / 4.]
        Phi[Phi > np.pi / 4.] -= np.pi / 2.

        # Convert Phi to degrees
        Neumann_psi = Phi * 180. / np.pi

        # Pre-compute trigonometric terms
        cos_2Phi = np.cos(2 * Phi)
        sin_2Phi = np.sin(2 * Phi)
        sin_4Phi = np.sin(4 * Phi)

        # Coherency Matrix de-orientation
        T110 = T_00_0
        T12re0 = T_01_0 * cos_2Phi - T_02_0 * sin_2Phi
        T12im0 = T_01_1 * cos_2Phi - T_02_1 * sin_2Phi
        T220 = T_11_0 * cos_2Phi**2 - T_12_0 * sin_4Phi + T_22_0 * sin_2Phi**2
        T330 = T_11_0 * sin_2Phi**2 - T_12_0 * sin_4Phi + T_22_0 * cos_2Phi**2

        # Compute Neumann_delta_mod, Neumann_delta_pha, and Neumann_tau
        Neumann_delta_mod = np.sqrt((T220 + T330) / (T110 + np.finfo(float).eps))
        Neumann_delta_pha = np.arctan2(T12im0, T12re0) * 180. / np.pi
        Neumann_tau = 1. - ((np.sqrt(T12re0 ** 2 + T12im0 ** 2) / T110) / (Neumann_delta_mod + np.finfo(float).eps))

        return Neumann_psi.astype(np.float32), Neumann_delta_mod.astype(np.float32), Neumann_delta_pha.astype(np.float32),Neumann_tau.astype(np.float32)