polsartools.polsar.fp.praks_parm_fp — PolSARtools 0.9 documentation

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# Source code for polsartools.polsar.fp.praks_parm_fp

    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 T3_C3_mat
    from .fp_infiles import fp_c3t3files

    @time_it
    def praks_parm_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
              ):
        """Derives praks polarimetric parameters for full-pol SAR data.

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

        >>> # Advanced usage with custom parameters
        >>> praks_parm_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 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. FrobeniusNorm: Frobenius norm
            2. ScattPredominance: Scattering predominance
            3. ScatteringDiversity: Scattering diversity
            4. DegreePurity: Degrees of purity
            5. DepolarizationIndex: Depolarization index
            6. Praks_Alpha: Alpha parameter
            7. Praks_Entropy: Entropy parameter

        References
        ----------
        Praks, J., Koeniguer, E.C. and Hallikainen, M.T., 2009.
        Alternatives to target entropy and alpha angle in SAR polarimetry.
        IEEE Transactions on Geoscience and Remote Sensing, 47(7), pp.2262-2274.

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

        output_filepaths = []
        if fmt == "bin":
            output_filepaths.append(os.path.join(in_dir, "FrobeniusNorm.bin"))
            output_filepaths.append(os.path.join(in_dir, "ScattPredominance.bin"))
            output_filepaths.append(os.path.join(in_dir, "ScatteringDiversity.bin"))
            output_filepaths.append(os.path.join(in_dir, "DegreePurity.bin"))
            output_filepaths.append(os.path.join(in_dir, "DepolarizationIndex.bin"))
            output_filepaths.append(os.path.join(in_dir, "Praks_Alpha.bin"))
            output_filepaths.append(os.path.join(in_dir, "Praks_Entropy.bin"))
        else:
            output_filepaths.append(os.path.join(in_dir, "FrobeniusNorm.tif"))
            output_filepaths.append(os.path.join(in_dir, "ScattPredominance.tif"))
            output_filepaths.append(os.path.join(in_dir, "ScatteringDiversity.tif"))
            output_filepaths.append(os.path.join(in_dir, "DegreePurity.tif"))
            output_filepaths.append(os.path.join(in_dir, "DepolarizationIndex.tif"))
            output_filepaths.append(os.path.join(in_dir, "Praks_Alpha.tif"))
            output_filepaths.append(os.path.join(in_dir, "Praks_Entropy.tif"))

        process_chunks_parallel(input_filepaths, list(output_filepaths),
                                window_size=win, write_flag=write_flag,
                            processing_func=process_chunk_praks,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_praks(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])

            T3 = np.array([[t11_T1, t12_T1, t13_T1],
                         [t21_T1, t22_T1, t23_T1],
                         [t31_T1, t32_T1, t33_T1]])
            T_T1 = T3_C3_mat(T3)

        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])
            T_T1 = np.array([[C11, C12, C13],
                             [C21, C22, C23],
                             [C31, C32, C33]])

        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)

        # Span (total power)
        span = T_T1[0, 0] + T_T1[1, 1] + T_T1[2, 2]
        span_real = np.real(span) + 1e-12  # Avoid division by zero

        # Normalize M by span
        M_norm = T_T1 / span_real[None, None, :, :]

        # Frobenius norm
        FrobNorm = np.sum(np.abs(M_norm)**2, axis=(0, 1))

        # Scattering power (predominant)
        ScattPred = np.sqrt(FrobNorm)

        # Scattering diversity
        ScattDiv = 1.5 * (1. - FrobNorm)

        # Safe term for purity and depolarization index
        safe_term = np.maximum(FrobNorm - 0.25, 0)

        # Degree of polarization purity
        DegPur = 2.0 * np.sqrt(safe_term)

        # Depolarization index
        DepInd = 1. - 2.0 * np.sqrt(safe_term) / np.sqrt(3.)

        # Alpha angle (in degrees)
        Alpha = np.arccos(np.clip(np.real(M_norm[0, 0]), -1.0, 1.0)) * 180. / np.pi

        # Entropy (from determinant)
        M_adj = M_norm.copy()
        for i in range(3):
            M_adj[i, i] += 0.16

        # Move axes to compute determinant over (n, m)
        M_adj_moved = np.moveaxis(M_adj, [2, 3], [0, 1])  # shape: (n, m, 3, 3)
        det = np.linalg.det(M_adj_moved)

        Entropy = 2.52 + 0.78 * np.log(np.sqrt(np.real(det)**2 + np.imag(det)**2) + 1e-12) / np.log(3.)

        return FrobNorm.astype(np.float32),ScattPred.astype(np.float32),ScattDiv.astype(np.float32),\
            DegPur.astype(np.float32),DepInd.astype(np.float32),Alpha.astype(np.float32),Entropy.astype(np.float32)