Conference Agenda

This contribution presents an ongoing effort to modernize selected components of the PolSARpro toolbox by re-implementing its core algorithms in Python. The work follows recommendations from PolInSAR 2021 and aims to provide a more accessible environment for both research and education while preserving numerical consistency with the original C implementation. The overarching goal is to offer a clean and sustainable foundation for future algorithmic development, teaching, and reproducible experimentation.

Python was chosen as the new language for the PolSARPro routines because it is strongly adopted in the remote sensing and open-source communities The new design relies on Xarray with a Dask backend to overcome two major limitations of NumPy: the absence of metadata and the lack of native parallel execution. This framework enables explicit polarimetric data structures (S, C3, T3, T4 matrices, etc.) whose elements are accessed through intuitive labels such as S.vh or C3.m11. Lazy evaluation plays a central role by delaying actual computations until explicitly requested, which helps limit memory pressure, allows the construction of complex workflows without allocating intermediate arrays, and provides opportunities for Dask to fuse tasks and optimize execution. Combined with Dask’s automatic blockwise scheduling, this setup enables parallel processing without writing explicit tiled or multithreaded code.

Input data adhere to the NetCDF-BEAM format used in SNAP. A dedicated loader interprets the structural metadata and constructs a PolSARpro-compatible dataset, automatically identifying the polarimetric type. If the source dataset has been processed with Range–Doppler terrain correction in SNAP, geolocation information is preserved throughout the workflow, allowing decomposition outputs to be exported as GeoTIFFs and viewed directly in GIS software.

The software is fully open-source under the Apache-2.0 license and hosted on GitHub, with extensive documentation on Read the Docs. The site includes installation instructions, reproducible tutorials linked to executable notebooks, an API reference, and a concise theory section. The package is distributed through conda, and availability on conda-forge is planned to simplify deployment and enable integration into ESA’s MAAP platform. A small ALOS-1 dataset over San Francisco will be released to support hands-on practice.

The current release provides Boxcar and Refined Lee filters, H/A/Alpha, Freeman decompositions, the Yamaguchi three- and four-component decompositions as well as the PWF (Polarimetric Whitening Filter). Additional decompositions, classification tools, and further polarimetric modules are planned. Systematic validation tests ensure continued numerical alignment with the original PolSARpro algorithms.