polsartools.polsar.dxp.dprvi — PolSARtools 0.9 documentation

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# Source code for polsartools.polsar.dxp.dprvi

    import os
    import numpy as np
    from polsartools.utils.proc_utils import process_chunks_parallel
    from polsartools.utils.utils import conv2d,time_it,eig22
    from .dxp_infiles import dxpc2files

    @time_it
    def dprvi(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
              ):
        """Compute dual-pol Radar Vegetation Index (DpRVI) from C2 matrix data.

        This function processes dual-polarization SAR data to generate the DpRVI, which is useful
        for vegetation monitoring and biomass estimation. The processing is done in parallel
        blocks for improved performance.

        Examples
        --------
        >>> # Basic usage with default parameters
        >>> dprvi("/path/to/c2_data")

        >>> # Advanced usage with custom parameters
        >>> dprvi(
        ...     in_dir="/path/to/c2_data",
        ...     win=3,
        ...     fmt="tif",
        ...     cog=True,
        ...     block_size=(1024, 1024)
        ... )

        Parameters
        ----------
        in_dir : str
            Path to the input folder containing C2 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, applies LZW compression to the output GeoTIFF files.
        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
            Results are written to disk as either 'dprvi.tif' or 'dprvi.bin'
            in the input folder.

        """
        write_flag=True
        input_filepaths = dxpc2files(in_dir)
        output_filepaths = []

        if fmt == "bin":
            output_filepaths.append(os.path.join(in_dir, "dprvi.bin"))
        else:
            output_filepaths.append(os.path.join(in_dir, "dprvi.tif"))

        process_chunks_parallel(input_filepaths, list(output_filepaths), window_size=win, write_flag=write_flag,
                                processing_func=process_chunk_dprvi,block_size=block_size, max_workers=max_workers,  num_outputs=1,
                                cog=cog,ovr=ovr, comp=comp,
                                progress_callback=progress_callback
                                )

    def process_chunk_dprvi(chunks, window_size,*args):
        kernel = np.ones((window_size,window_size),np.float32)/(window_size*window_size)
        c11_T1 = np.array(chunks[0])
        c12_T1 = np.array(chunks[1])+1j*np.array(chunks[2])
        c21_T1 = np.conj(c12_T1)
        c22_T1 = np.array(chunks[3])

        if window_size>1:
            c11s = conv2d(np.real(c11_T1),kernel)+1j*conv2d(np.imag(c11_T1),kernel)
            c12s = conv2d(np.real(c12_T1),kernel)+1j*conv2d(np.imag(c12_T1),kernel)
            c21s = conv2d(np.real(c21_T1),kernel)+1j*conv2d(np.imag(c21_T1),kernel)
            c22s = conv2d(np.real(c22_T1),kernel)+1j*conv2d(np.imag(c22_T1),kernel)

            c2_det = (c11s*c22s-c12s*c21s)
            c2_trace = c11s+c22s
            # t2_span = t11s*t22s
            m = (np.sqrt(1.0-(4.0*c2_det/np.power(c2_trace,2))))
            egv1,egv2 = eig22(np.dstack([c11s,c12s,c21s,c22s]))
            egf = np.vstack([egv1,egv2])
            egfmax = egf.max(axis=0)#.reshape(np.shape(C2_stack[:,:,0]))
            beta = (egfmax/(egv1+egv2)).reshape(np.shape(c11s))
            dprvi = np.real(1-(m*beta))
        else:
            c2_det = (c11_T1*c22_T1-c12_T1*c21_T1)
            c2_trace = c11_T1+c22_T1
            m = np.real(np.sqrt(1.0-(4.0*c2_det/np.power(c2_trace,2))))

            egv1,egv2 = eig22(np.dstack([c11_T1,c12_T1,c21_T1,c22_T1]))
            egf = np.vstack([egv1,egv2])
            egfmax = egf.max(axis=0)#.reshape(np.shape(C2_stack[:,:,0]))
            beta = (egfmax/(egv1+egv2)).reshape(np.shape(c11_T1))
            dprvi = np.real(1-(m*beta))

        return dprvi.astype(np.float32)