A time series processing chain for geological disasters based on a GPU-assisted sentinel-1 InSAR processor

Yongsheng Li, Wenliang Jiang () and Jingfa Zhang
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Yongsheng Li: Ministry of Emergency Management of China
Wenliang Jiang: Ministry of Emergency Management of China
Jingfa Zhang: Ministry of Emergency Management of China

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 111, issue 1, No 35, 803-815

Abstract: Abstract Interferometric synthetic aperture radar (InSAR) technology has the potential to reveal ground surface deformation at high temporal and spatial resolutions, and the InSAR image processing field is progressing toward big data analytics. In this era of big data, as InSAR is usually employed for natural hazard research, processing large InSAR images under time constraints is a fundamental challenge. Graphics processing units (GPUs) have been widely adopted for high-performance computing (HPC) due to their parallel computing capability and low power consumption. Accordingly, we explore the interconnection between InSAR time series processing and GPU hardware for parallel Sentinel-1 InSAR processing. We build a rapid GPU-assisted InSAR processing chain and apply this chain to accelerate the time-consuming optimization steps in Sentinel-1 InSAR processing. This processing chain plays a pivotal role in rapidly calculating InSAR deformation maps using large quantities of Sentinel-1 SAR data and thus can be used in practical applications. We also perform some experiments to demonstrate that the processing chain is appropriate for the deformation monitoring of various natural disasters and is suitable for emergency monitoring and wide-area investigations of potential hazards involving geological disasters.

Keywords: Sentinel-1; GPU-assisted processing; Geological disasters; InSAR time series (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-021-05079-9

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