SARXarray and STMtools

Open-Source Python Libraries for InSAR Data Processing and Analysis

Conference Paper (2024)
Author(s)

O. Ku (The Netherlands eScience Center)

F. Alidoost (The Netherlands eScience Center)

P. Chandramouli (The Netherlands eScience Center)

T. van Lankveld (The Netherlands eScience Center)

F. Nattino (The Netherlands eScience Center)

M. W. Grootes (The Netherlands eScience Center)

F. J. van Leijen (TU Delft - Civil Engineering & Geosciences)

R. F. Hanssen (TU Delft - Civil Engineering & Geosciences)

Research Group
Digital Technologies
DOI related publication
https://doi.org/10.1109/IGARSS53475.2024.10642762 Final published version
More Info
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Publication Year
2024
Language
English
Research Group
Digital Technologies
Pages (from-to)
11036-11038
Event
2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024 (2024-07-07 - 2024-07-12), Athens, Greece
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Abstract

We introduce SARXarray and STMtools, two python libraries designed to support the exploitation of modern Interferometric Synthetic Aperture Radar (InSAR) data by enabling handling of larger-than memory datasets and the incorporation and fusion with relevant contextual information.The libraries are developed upon two innovative and well-established open-source Python libraries:Xarray [5] and Dask [6]. They are implemented as Xarray extensions. SARXarray is designed to manipulate and operate on larger-than-memory coregistered raster stacks such as Single-look Complex images (SLC) or interferograms, performing scatterer selection and producing STM objects. STMtools leverage the Space-Time Matrix (STM) concept [3,4] and provides functionalities to process STM and perform enrichment/data fusion with other data sources.Both libraries are built on the Xarray library, providing support for a wide range of data formats, and utilize Dask for parallel computation, making them scalable for distributed computation infrastructures. By enabling InSAR data analysis incorporating contextual information, the two libraries enhance the potential to uncover underlying mechanisms driving deformation phenomena.

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