polsartools.polsar.fp.shannon_h_fp — PolSARtools 0.9.1 documentation

Skip to main content
`Ctrl`+`K`

# Source code for polsartools.polsar.fp.shannon_h_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 shannon_h_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
              ):
        """Calculate Shannon Entropy parameters from full-pol SAR data.

        This function computes three Shannon Entropy-based parameters from full-polarimetric
        SAR data: total Shannon Entropy (H), intensity contribution (HI), and polarimetric
        contribution (HP). These parameters provide information about the complexity and
        disorder of the scattered wave field.

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

        >>> # Advanced usage with custom parameters
        >>> shannon_h_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.
        window_size : int, default=1
            Size of the spatial averaging window. Larger windows provide better
            entropy estimation 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 three output files to disk:

            1. H_Shannon: Total Shannon Entropy
            2. HI_Shannon: Intensity contribution
            3. HP_Shannon: Polarimetric contribution

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

        output_filepaths = []
        if fmt == "bin":
            output_filepaths.append(os.path.join(in_dir, "H_Shannon.bin"))
            output_filepaths.append(os.path.join(in_dir, "HI_Shannon.bin"))
            output_filepaths.append(os.path.join(in_dir, "HP_Shannon.bin"))
        else:
            output_filepaths.append(os.path.join(in_dir, "H_Shannon.tif"))
            output_filepaths.append(os.path.join(in_dir, "HI_Shannon.tif"))
            output_filepaths.append(os.path.join(in_dir, "HP_Shannon.tif"))

        process_chunks_parallel(input_filepaths, list(output_filepaths),
                                window_size=win, write_flag=write_flag,
                            processing_func=proc_shannon_h_fp,block_size=block_size,
                            max_workers=max_workers,  num_outputs=len(output_filepaths),
                            cog=cog, ovr=ovr, comp=comp,
                            progress_callback=progress_callback
                            )

    def proc_shannon_h_fp(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]])
            # T_T1 = T3_C3_mat(T3)

        elif '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]])

        else:
            raise ValueError("Invalid input matrices. Ensure the input is either T3 or C3 matrix folder.")

        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))

        T_T1 = np.dstack((T_T1[0,:,:],T_T1[1,:,:],T_T1[2,:,:],
                          T_T1[3,:,:],T_T1[4,:,:],T_T1[5,:,:],T_T1[6,:,:],T_T1[7,:,:],T_T1[8,:,:]))

        data = T_T1.reshape( T_T1.shape[0]*T_T1.shape[1], T_T1.shape[2]).reshape((-1,3,3))
        # infinity, nan handling
        data = np.nan_to_num(data, nan=0.0, posinf=0, neginf=0)
        # data = np.nan_to_num(data, nan=np.nan, posinf=np.nan, neginf=np.nan)
        evals_, evecs_ = np.linalg.eig(data.reshape(-1, 3, 3))

        # Sort eigenvalues for each pixel in descending order;
        sorted_indices = np.argsort(evals_, axis=-1)[:, ::-1]

        # Reorder eigenvalues and eigenvectors based on sorted indices
        evals = np.take_along_axis(evals_, sorted_indices, axis=-1)  # Reorder eigenvalues

        eps  = 1e-8
        D = evals[:,0]*evals[:,1]*evals[:,2]
        I = evals[:,0]+evals[:,1]+evals[:,2]
        #Barakat Degree of Polarization
        DoP = np.ones(rows*cols).astype(np.float32) - 27* D / (I*I*I + eps)

        HSP = np.zeros(rows*cols).astype(np.float32)
        # HSI = np.zeros(rows*cols).astype(np.float32)
        # HS = np.zeros(rows*cols).astype(np.float32)

        condition = (np.ones(rows*cols) - DoP) < eps
        HSP = np.where(condition, 0, np.log(np.abs(np.ones(rows*cols) - DoP)))
        HSP[np.isinf(HSP)] = np.nan
        HSP[HSP==0] = np.nan

        with np.errstate(divide='ignore', invalid='ignore'):
            HSI = 3 * np.log(np.exp(1) * np.pi * I / 3)
            HSI[np.isinf(HSI)] = np.nan
            HSI[HSI==0] = np.nan

        HS = np.nansum(np.dstack((HSP, HSI)), 2)

        return np.real(HS).reshape(rows,cols).astype(np.float32),\
            np.real(HSI).reshape(rows,cols).astype(np.float32) ,\
            np.real(HSP).reshape(rows,cols).astype(np.float32)#,np.real(HS_norm).reshape(rows,cols),np.real(HSI_norm).reshape(rows,cols),np.real(HSP_norm).reshape(rows,cols)