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Distributed Scatterer Interferometry Tailored to the Analysis of Big InSAR Data

机译:分布式散射仪干涉测量法定制了大于大于INSAR数据的分析

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Wide-swath satellite missions with short revisit times, such as Sentinel-1 and NISAR, provide an unprecedented wealth of interferometric time series and open new opportunities for systematic monitoring of the Earth surface. The processing of the emerging Big Data with the state-of-the-art InSAR time series analysis techniques is, however, computationally challenging. A new demand has emerged for the analysis of the fast growing data volumes specifically for systematic near real-time (NRT) monitoring of the Earth surface. We have addressed this demand by the proposal of two efficient alternative estimators for NRT processing of the emerging Big Data in [1, 2]. In this contribution, a hybrid approach based on the proposed estimators is introduced and applied in efficient wide area processing of two-year archive of Sentinel-1 data over eastern part of the Trans-Mexican Volcanic Belt.
机译:宽带卫星任务具有短暂的Revisit时代,如Sentinel-1和Nisar,提供了前所未有的干涉时间序列,并开辟了地球表面的系统监测的新机会。然而,利用最先进的INSAR时间序列分析技术处理新兴大数据是计算上的挑战性。为分析了对地球表面的近实时(NRT)监测的系统近实时的快速增长数据量来分析了新的需求。我们通过对[1,2]的新兴大数据的NRT处理的两个有效的替代估计的提案来解决了这一需求。在这一贡献中,基于所提出的估计器的混合方法被引入并应用于跨墨西哥火山海岸东部的Sentinel-1数据的两年档案的高效广域加工。

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