polsartools.polsar.fp.rvi_fp — PolSARtools 0.9.1 documentation

Skip to main content
`Ctrl`+`K`

# Source code for polsartools.polsar.fp.rvi_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 C3_T3_mat
    from .fp_infiles import fp_c3t3files

    @time_it
    def rvi_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 Radar Vegetation Index (RVI) from full-pol SAR data.

        This function computes the Radar Vegetation Index (RVI) using full-polarimetric
        SAR data. RVI is one of the earliest and most widely used polarimetric vegetation
        indices, providing a measure of vegetation density and randomness of scattering.

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

        >>> # Advanced usage with custom parameters
        >>> rvi_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 a Cloud Optimized GeoTIFF (COG) 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 one output file to disk:
            - 'rvifp.tif' or 'rvifp.bin': RVI values

        """
        write_flag=True
        input_filepaths = fp_c3t3files(in_dir)
        output_filepaths = []
        if fmt == "bin":
            output_filepaths.append(os.path.join(in_dir, "rvifp.bin"))
        else:
            output_filepaths.append(os.path.join(in_dir, "rvifp.tif"))

        process_chunks_parallel(input_filepaths, list(output_filepaths),
                                window_size=win, write_flag=write_flag,
                            processing_func=process_chunk_rvifp,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_rvifp(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]])

        reshaped_arr = T_T1.reshape(3, 3, -1).transpose(2, 0, 1)
        eigenvalues = np.linalg.eigvals(reshaped_arr)
        sorted_eigenvalues = np.sort(eigenvalues, axis=1)[:, ::-1]
        sorted_eigenvalues = sorted_eigenvalues.reshape(T_T1.shape[2], T_T1.shape[3], 3)

        p1 = sorted_eigenvalues[:,:,0]/(sorted_eigenvalues[:,:,0] + sorted_eigenvalues[:,:,1] + sorted_eigenvalues[:,:,2])
        p2 = sorted_eigenvalues[:,:,1]/(sorted_eigenvalues[:,:,0] + sorted_eigenvalues[:,:,1] + sorted_eigenvalues[:,:,2])
        p3 = sorted_eigenvalues[:,:,2]/(sorted_eigenvalues[:,:,0] + sorted_eigenvalues[:,:,1] + sorted_eigenvalues[:,:,2])

        p1[p1<0] = 0
        p2[p2<0] = 0
        p3[p3<0] = 0

        p1[p1>1] = 1
        p2[p2>1] = 1
        p3[p3>1] = 1

        rvi = np.real((4*p3)/(p1 + p2 + p3))

        # idx = np.argwhere(rvi>1)

        # rvi[idx] = (3/4)*rvi[idx]
        # rvi[~idx] = rvi[~idx]
        # rvi[rvi==0] = np.nan

        # rvi = np.real(rvi)

        rvi[rvi > 1] *= 0.75
        rvi[rvi == 0] = np.nan
        rvi = np.real(rvi)

        return rvi.astype(np.float32)