polsartools.polsar.fp.mf4cf — PolSARtools 0.9 documentation

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# Source code for polsartools.polsar.fp.mf4cf

    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 mf4cf(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 Model-Free 4-Component Decomposition for full-pol SAR data.

        This function implements an advanced model-free four-component decomposition for
        full-polarimetric SAR data. It decomposes the total scattered power into surface (Ps),
        double-bounce (Pd), volume (Pv), and helix (Pc) scattering components, along with
        scattering type (Theta_FP) and helicity (Tau_FP) parameters.

        Examples
        --------
        >>> # Basic usage with default parameters
        >>> mf4cf("/path/to/fullpol_data")

        >>> # Advanced usage with custom parameters
        >>> mf4cf(
        ...     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 six output files to disk:

            1. Ps_mf4cf: Surface scattering power component
            2. Pd_mf4cf: Double-bounce scattering power component
            3. Pv_mf4cf: Volume scattering power component
            4. Pc_mf4cf: Helix scattering power component
            5. Theta_FP_mf4cf: Scattering type parameter
            6. Tau_FP_mf4cf: Helicity parameter

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

        output_filepaths = []
        if fmt == "bin":
            output_filepaths.append(os.path.join(in_dir, "Ps_mf4cf.bin"))
            output_filepaths.append(os.path.join(in_dir, "Pd_mf4cf.bin"))
            output_filepaths.append(os.path.join(in_dir, "Pv_mf4cf.bin"))
            output_filepaths.append(os.path.join(in_dir, "Pc_mf4cf.bin"))
            output_filepaths.append(os.path.join(in_dir, "Theta_FP_mf4cf.bin"))
            output_filepaths.append(os.path.join(in_dir, "Tau_FP_mf4cf.bin"))

        else:
            output_filepaths.append(os.path.join(in_dir, "Ps_mf4cf.tif"))
            output_filepaths.append(os.path.join(in_dir, "Pd_mf4cf.tif"))
            output_filepaths.append(os.path.join(in_dir, "Pv_mf4cf.tif"))
            output_filepaths.append(os.path.join(in_dir, "Pc_mf4cf.tif"))
            output_filepaths.append(os.path.join(in_dir, "Theta_FP_mf4cf.tif"))
            output_filepaths.append(os.path.join(in_dir, "Tau_FP_mf4cf.tif"))

        process_chunks_parallel(input_filepaths, list(output_filepaths),
                                window_size=win, write_flag=write_flag,
                            processing_func=process_chunk_mf4cf,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_mf4cf(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]])

        # det_T3 = t11s*(t22s*t33s-t23s*t32s)-t12s*(t21s*t33s-t23s*t31s)+t13s*(t21s*t32s-t22s*t31s)
        # trace_T3 = t11s + t22s + t33s
        # m1 = np.real(np.sqrt(1-(27*(det_T3/(trace_T3**3)))))

        reshaped_arr = T_T1.reshape(3, 3, -1).transpose(2, 0, 1)
        det_T3 = np.linalg.det(reshaped_arr)
        # del reshaped_arr
        det_T3 = det_T3.reshape(T_T1.shape[2], T_T1.shape[3])

        s0_f = T_T1[0,0,:,:] + T_T1[1,1,:,:] + T_T1[2,2,:,:] #trace_T3
        dop_f = np.real(np.sqrt(1-(27*(det_T3/(s0_f**3)))))

        k11_f = (T_T1[0,0,:,:] + T_T1[1,1,:,:] + T_T1[2,2,:,:])/2
        k44_f = (-T_T1[0,0,:,:] + T_T1[1,1,:,:] + T_T1[2,2,:,:])/2
        k14_f = np.imag(T_T1[1,2,:,:])

        # s0_f = trace_T3
        # dop_f = m1

        val1 = (4*dop_f*k11_f*k44_f)/(k44_f**2 - (1 + 4*dop_f**2)*k11_f**2)
        val2 = np.abs(k14_f)/(k11_f)

        theta_f = np.real(np.arctan(val1)) # separation for surface and dbl
        tau_f = np.real(np.arctan(val2)) # separation for helix
        # thet = np.rad2deg(thet)
        theta_FP = np.rad2deg(theta_f).astype(np.float32)
        tau_FP = np.rad2deg(tau_f).astype(np.float32)

        pc_f = (dop_f*s0_f*(np.sin(2*tau_f))).astype(np.float32)
        pv_f = ((1-dop_f)*s0_f).astype(np.float32)
        res_pow = s0_f - (pc_f + pv_f)
        ps_f = ((res_pow/2)*(1+np.sin((2*theta_f)))).astype(np.float32)
        pd_f = ((res_pow/2)*(1-np.sin((2*theta_f)))).astype(np.float32)

        return ps_f.astype(np.float32), pd_f.astype(np.float32), pv_f.astype(np.float32),pc_f.astype(np.float32),theta_FP.astype(np.float32),tau_FP.astype(np.float32)