Parallel Optimization for Large Scale Interferometric Synthetic Aperture Radar Data Processing
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Abstract:
Interferometric synthetic aperture radar (InSAR) has developed rapidly over the past years and is considered as an important method for surface deformation monitoring, benefiting from growing data quantities and improving data quality. However, the handing of SAR big data poses significant challenges for related algorithms and pipeline, particularly in large-scale SAR data processing. In addition, InSAR algorithms are highly complex, and their task dependencies are intricate. There is a lack of efficient optimization models and task scheduling for InSAR pipeline. In this paper, we design parallel time-series InSAR processing models based on multi-thread technology for high efficiency in processing InSAR big data. These models concentrate on parallelizing critical algorithms that have high complexity, with a focus on deconstructing two computationally intensive algorithms through loop unrolling. Our parallel models have shown a significant improvement of 10–20 times in performance. We have also developed a parallel optimization tool, Simultaneous Task Automatic Runtime (STAR), which utilizes a data flow optimization strategy with thread pool technology to address the problem of low CPU utilization resulting from multiple modules and task dependencies in the InSAR processing pipeline. STAR provides a data-driven pipeline and enables concurrent execution of multiple tasks, with greater flexibility to keep the CPU busy and further improve CPU utilization through predetermined task flow. Additionally, a supercomputing-based system has been constructed for processing massive InSAR scientific big data and providing technical support for nationwide surface deformation measurement, in accordance with the framework of time series InSAR data processing. Using this system, we processed InSAR data with the volumes of 500 TB and 700 TB in 5 and 7 days, respectively. Finally we generated two maps of land surface deformation all over China.Keywords:
Data Processing
InSAR(synthetic aperture radar interferometry) is a revolutionary advancement of earth observation technology,especially the applications in monitoring the ground subsidence by means of D-InSAR and PS-InSAR,which attract more and more attention.The theoretical models for the techniques of D-InSAR and PS-InSAR are introduced,and the different characteristics of them are extracted on the basis of analyzing their theoretical models,and then the suitably and unsuitably applied areas for them are systematically summed up according to their respective characteristics,which is of referenced significance for people to rightly realize and apply the techniques of D-InSAR and PS-InSAR in practice.
Ground subsidence
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InSAR 기법은 SAR영상의 간섭을 이용하여 지표의 변위를 측정하는 기술이다. InSAR를 이용한 대표적인 지표변위 관측기법에는 differential InSAR(D-InSAR)와 permanent scatter InSAR(PS-InSAR) 간섭기법이 있다. D-InSAR란 2장의 영상을 각각 master, slave 영상으로 놓고 간섭시키는 기법을 말한다. 이 기법에서는 간섭도로부터 지형의 위상을 제거함으로써 지형과 변위의 두 가지 위상 효과를 분리한다. 본 논문에서는 독일의 Terra SAR-X 위성을 사용하여, 광주~세종시에 이르는 호남선 구간을 6개월 간 촬영한 영상을 D-InSAR 기법으로 분석하였다. 그동안 수행된 D-InSAR 기법을 사용한 연구의 경우 통상적으로 2장의 영상을 사용하였지만, 본 연구에서는 6개월 간 촬영된 총 8장의 영상으로 분석해보고자 한다. 또한 다양한 분석기법을 통하여, 어느 기법이 가장 정밀한 값을 갖는지 비교하여 최적의 D-InSAR 결과물을 도출하고자 하였다.
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