polsartools.polsar.dxp.dprsic — PolSARtools 0.10.1 documentation

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# Source code for polsartools.polsar.dxp.dprsic

    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, S_norm

    @time_it
    def dprsic(cpFile,xpFile,  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
               ):
        """
        This function computes the Dual-pol Radar Surface Index (DpRSI) for GRD (only intensity no phase).

        Examples
        --------
        >>> # Basic usage with default parameters
        >>> dprsic("/path/to/copol_file.tif", "/path/to/crosspol_file.tif")

        >>> # Advanced usage with custom parameters
        >>> dprsic(
        ...     cpFile="/path/to/copol_file.tif",
        ...     xpFile="/path/to/crosspol_file.tif",
        ...     win=3,
        ...     fmt="tif",
        ...     cog=True,
        ...     block_size=(1024, 1024)
        ... )

        Parameters
        ----------
        cpFile : str
            Path to the co-polarized backscatter (linear) SAR raster file.
        xpFile : str
            Path to the cross-polarized backscatter (linear) SAR raster file.
        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 'DpRSIc.tif' or 'DpRSIc.bin'
            in the input folder.

        """
        write_flag=True
        input_filepaths = [cpFile,xpFile]
        output_filepaths = []

        if fmt == "bin":
            output_filepaths.append(os.path.join(os.path.dirname(cpFile), "DpRSIc.bin"))
        else:
            output_filepaths.append(os.path.join(os.path.dirname(cpFile), "DpRSIc.tif"))

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

    def process_chunk_dprsic(chunks, window_size,*args):
        kernel = np.ones((window_size,window_size),np.float32)/(window_size*window_size)
        c11 = np.array(chunks[0])
        c22 = np.array(chunks[1])

        # def S_norm(S_array):
        #     S_5 = np.nanpercentile(S_array, 2)
        #     S_95 = np.nanpercentile(S_array, 98)
        #     S_cln = np.where(S_array > S_95, S_95, S_array)
        #     S_cln = np.where(S_cln < S_5, S_5, S_cln)
        #     S_cln_max = np.nanmax(S_cln)
        #     S_norm_array = np.divide(S_cln,S_cln_max)

        #     return S_norm_array

        if window_size>1:
            c11 = conv2d(c11,kernel)
            c22 = conv2d(c22,kernel)

        s0 = np.abs(c11 + c22)
        s1 = np.abs(c11 - c22)

        C11_av_db = 10*np.log10(c11)

        prob1 = c11/(c11 + c22)
        prob2 = c22/(c11 + c22)

        ent = -prob1*np.log2(prob1) - prob2*np.log2(prob2)
        s1_s_norm = S_norm(s1) #This is S1 normalzied for DpRSI, does not include slope mask

        dprsi_con1 = (1 - ent)*np.sqrt(1 - np.square(s1_s_norm)); # For Valid pixels
        dprsi_con2 = np.sqrt(1 - np.square(s1_s_norm)); # For Noise pixels

        NESZ = -16 ## For Sentinel-1
        dprsic = np.where(C11_av_db > NESZ, dprsi_con1, dprsi_con2)

        return dprsic.astype(np.float32)