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

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX Pub Date : 2025-03-04 DOI:10.1016/j.mex.2025.103253

Hansanee Fernando , Kwabena Nketia , Thuan Ha , Sarah van Steenbergen , Heather McNairn , Steve Shirtliffe

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引用次数: 0

Abstract

Processing Sentinel-1 (S1) Single Look Complex (SLC) data is time-consuming, even with software like SNAP or PolSARpro. Command line processing on Windows provides an automated alternative, enabling R-based processing of multiple S1-SLC files without manual interaction. Here we demonstrate a user friendly automated process, to process an unlimited number of S1-SLC images, tailored for users with minimal SAR or programming competence. The proposed workflow integrates RStudio, SNAP, and PolSARpro software libraries to implement the same processes a user can achieve via the corresponding graphic user interfaces (GUI). The workflow includes bulk S1-SLC imagery downloads, installation and configuration of dependent software applications. Within the SNAP GUI, a base-graph was constructed, encompassing crucial processing steps such as data import, sub-swath extraction, orbit determination, calibration, speckle filtering, debursting, and terrain correction, which acts as a template for generating customized SNAP graphs for individual S1 imagery. These graphs are batch processed with R, using parallel computing to run multiple graphs simultaneously. In the subsequent PolSARpro processing phase, outputs from the SNAP processing pipeline are made interoperable with PolSARpro tools for onward post-processing. Similarly, we leverage the parallelization mechanisms of R for user specific parameter extraction, which maximizes resource utilization while maintaining computational performance.

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Automated Workflow for SAR Processing: Introduces an automated, user-friendly framework combining RStudio, SNAP, and PolSARpro to process unlimited Sentinel-1 Single Look Complex (S1-SLC) images, eliminating manual interaction and catering to users with minimal programming or SAR expertise.
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Customizable and Scalable Processing: Leverages SNAP's base-graph templates for essential SAR processing steps (e.g., orbit determination, calibration, speckle filtering, and terrain correction) to enable batch processing and parallel computing for efficient handling of large datasets.
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Interoperability and Enhanced Performance: Integrates outputs from SNAP into PolSARpro for advanced post-processing, employing R-based parallelization to optimize resource utilization and ensure efficient user-specific parameter extraction.

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自动化sentinel-1 SLC产品加工：高效极化参数提取的并行化和优化

处理Sentinel-1 (S1) Single Look Complex （SLC）数据非常耗时，即使使用SNAP或PolSARpro等软件也是如此。Windows上的命令行处理提供了一种自动化的替代方案，支持基于r的多个S1-SLC文件的处理，而无需手动交互。在这里，我们演示了一个用户友好的自动化过程，可以处理无限数量的S1-SLC图像，为具有最低SAR或编程能力的用户量身定制。提出的工作流集成了RStudio， SNAP和PolSARpro软件库，以实现用户可以通过相应的图形用户界面（GUI）实现的相同过程。工作流程包括批量的S1-SLC图像下载、安装和配置依赖的软件应用程序。在SNAP GUI中，构建了一个基本图，包括数据导入、子条提取、轨道确定、校准、散斑滤波、去破裂和地形校正等关键处理步骤，作为为单个S1图像生成定制SNAP图的模板。这些图是用R批处理的，使用并行计算同时运行多个图。在随后的PolSARpro处理阶段，SNAP处理管道的输出与PolSARpro工具可互操作，以进行后续处理。类似地，我们利用R的并行化机制来提取用户特定的参数，这在保持计算性能的同时最大化了资源利用率。•SAR处理的自动化工作流程：引入了一个自动化的，用户友好的框架，结合RStudio， SNAP和PolSARpro来处理无限的Sentinel-1 Single Look Complex （S1-SLC）图像，消除了手动交互，并以最少的编程或SAR专业知识迎合用户。•可定制和可扩展的处理：利用SNAP的基本图模板进行基本的SAR处理步骤（例如，轨道确定，校准，斑点滤波和地形校正），以实现批量处理和并行计算，以有效处理大型数据集。•互操作性和增强的性能：将SNAP的输出集成到PolSARpro中进行高级后处理，采用基于r的并行化来优化资源利用率，并确保高效的用户特定参数提取。

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