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Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements

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Automating sentinel-1 SLC product processing: Parallelization and optimization for efficient polarimetric parameter

Hansanee Fernando¹, Kwabena Nketia¹, Thuan Ha¹

¹Department of Plant Sciences, College of Agriculture and Bioresources, University of Saskatchewan, Canada.

|March 31, 2025

Summary

This summary is machine-generated.

This study introduces an automated workflow for processing Sentinel-1 Single Look Complex (S1-SLC) data using RStudio, SNAP, and PolSARpro. The user-friendly process enables efficient, large-scale SAR data analysis with minimal programming expertise.

Keywords:

Automation Automation of processing S1 SLC images Batch-processing Parallelization PolSARpro RStudio SNAP Sentinel-1 Single Look Complex

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Area of Science:

Remote Sensing
Geospatial Analysis
Computational Science

Background:

Processing Sentinel-1 (S1) Single Look Complex (SLC) data is computationally intensive and requires specialized software like SNAP or PolSARpro.
Manual processing of multiple S1-SLC files is time-consuming and prone to errors, hindering large-scale analysis.

Purpose of the Study:

To develop an automated, user-friendly workflow for processing an unlimited number of S1-SLC images.
To enable efficient SAR data processing for users with limited programming or SAR expertise.
To integrate RStudio, SNAP, and PolSARpro for a seamless and scalable processing pipeline.

Main Methods:

Developed a workflow integrating RStudio, SNAP, and PolSARpro for command-line processing on Windows.
Utilized SNAP's graphical user interface (GUI) to create a base-graph for essential SAR processing steps.
Implemented R-based batch processing with parallel computing for simultaneous graph execution and efficient parameter extraction.

Main Results:

Successfully automated the processing of multiple S1-SLC images, significantly reducing manual interaction.
Achieved interoperability between SNAP and PolSARpro outputs for advanced post-processing.
Demonstrated efficient resource utilization and computational performance through R-based parallelization.

Conclusions:

The proposed automated workflow streamlines S1-SLC data processing, making advanced SAR analysis accessible to a wider user base.
The integration of RStudio, SNAP, and PolSARpro offers a scalable and efficient solution for handling large volumes of SAR data.
This approach enhances SAR data processing capabilities, particularly for users with minimal programming or SAR expertise.