Refined-Lee filter (rlee)#

polsartools.rlee(infolder, window_size=3, outType='tif', sub_dir=True, cog_flag=False, cog_overviews=[2, 4, 8, 16], write_flag=True, max_workers=None, block_size=(512, 512), progress_callback=None)[source]

Apply Refined Lee (RLee) speckle filter to polarimetric SAR data.

The Refined Lee filter is an adaptive speckle filter that preserves edges and structural details while reducing noise. Unlike Boxcar, RLee dynamically adjusts based on local statistics, making it more suitable for classification, segmentation, and biophysical retrieval tasks.

Examples

>>> # Basic usage with default parameters
>>> rlee("/path/to/polSAR_data")

>>> # Custom usage with large window and tiled COG output
>>> rlee("/path/to/polSAR_data", window_size=7, cog_flag=True)
Parameters:

  • infolder (str) – Path to the folder with C4, T4, C3, T3, C2, or T2 matrix files.

  • window_size (int, default=3) – Size of the adaptive filtering window.

  • outType ({'tif', 'bin'}, default='tif') – Desired output file format.

  • sub_dir (bool, default=True) – If True, creates a subdirectory for the output files based on filter type and window size in the input folder.

  • cog_flag (bool, default=False) – Create Cloud Optimized GeoTIFF with overviews and internal tiling.

  • cog_overviews (list[int], default=[2, 4, 8, 16]) – Overview pyramid levels for zoomable image access.

  • write_flag (bool, default=True) – Write the output to disk if True.

  • max_workers (int | None, default=None) – Number of threads for parallel processing.

  • block_size (tuple[int, int], default=(512, 512)) – Block size used during chunked filtering.

Returns:

Output files are written to disk with the applied RLee filter.

Return type:

None

Boxcar filter (boxcar)#

polsartools.boxcar(infolder, window_size=3, outType='tif', sub_dir=True, cog_flag=False, cog_overviews=[2, 4, 8, 16], write_flag=True, max_workers=None, block_size=(512, 512), progress_callback=None)[source]

Apply a Boxcar speckle filter to polarimetric SAR data to reduce speckle noise.

The Boxcar filter performs a uniform mean filter over a square window, effectively smoothing the polarimetric matrix to enhance interpretability. It is a fast, straightforward speckle reduction technique suitable for preprocessing and visualization.

Examples

>>> # Basic usage
>>> boxcar("/path/to/polSAR_data")

>>> # With custom window size and output as GeoTIFF
>>> boxcar("/path/to/polSAR_data", window_size=5, cog_flag=True)
Parameters:

  • infolder (str) – Input folder containing C4, T4, C3, T3, C2, or T2 matrix files (.bin or .tif).

  • window_size (int, default=3) – Size of the spatial smoothing window (e.g., 3x3).

  • outType ({'tif', 'bin'}, default='tif') – Output file format type.

  • cog_flag (bool, default=False) – If True, generates a Cloud Optimized GeoTIFF.

  • cog_overviews (list[int], default=[2, 4, 8, 16]) – Overview levels for COG pyramids.

  • write_flag (bool, default=True) – If False, processes data in memory without saving to disk.

  • max_workers (int | None, default=None) – Maximum number of parallel workers.

  • block_size (tuple[int, int], default=(512, 512)) – Block size for chunked processing.

Returns:

Writes filtered output matrix files to disk.

Return type:

None