Blind Source Separation for MT-InSAR Analysis With Structural Health Monitoring Applications
Blind Source Separation for MT-InSAR Analysis With Structural Health Monitoring Applications
复制标题
用于结构健康监测应用的 MT-InSAR 分析的盲源分离
DOI：
10.1109/jstars.2022.3190027
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发表时间：
2022
影响因子：
5.5
通讯作者：
Martin G
中科院分区：
文献类型：
--
作者：
Martin G
由于可以通过大量绕地球运行的不同卫星获得遥感数据，因此可以监测地球表面的大面积区域。具体来说，多时相干涉合成孔径雷达 (MT-InSAR) 技术为结构群体提供由于热膨胀、收缩和地形变形等原因引起的地形或结构（例如桥梁或建筑物）的视线位移的时间序列。为了识别导致变形的不同现象，对观察到的不同变形信号进行分析至关重要。在本文中，我们探讨了将盲源分离算法应用于 MT-InSAR 的可能性，旨在开发自动识别建筑物和结构中不同变形模式的方法。我们使用综合生成的数据集和真实的 MT-InSAR 数据验证了所提出的方法。我们还提供了与其他类似方法的比较。我们的结果表明，InSAR 时间序列分析可以受益于所提出的盲源分离方法的使用。此外，所提出的技术对于 MT-InSAR 数据中通常存在的噪声具有鲁棒性。这为大规模监控大面积基础设施打开了大门，有助于监控民用基础设施，并为资产所有者提供有关此类结构随时间推移的性能的相关见解。
Monitoring large areas of the Earth's surface is possible thanks to the availability of remote sensing data obtained by a large collection of diverse satellites orbiting the Earth. Specifically, multitemporal interferometric synthetic aperture radar (MT-InSAR) techniques supply the structural community with time series of line-of-sight displacements of terrain or structures, such as bridges or buildings, resulting from causes such as thermal expansion, contraction, and terrain deformation. The analysis of the different deformation signals observed is crucial in order to identify the different phenomena that cause the deformation. In this article, we explore the possibility of applying blind source separation algorithms to MT-InSAR, with the aim of developing methods towards automatic identification of different deformation patterns both in buildings and structures. We validate the proposed methodology using both synthetically generated datasets and real MT-InSAR data. We also provide a comparison with other similar methods. Our results demonstrate that InSAR time-series analysis can benefit from the use of the proposed blind source separation approach. Furthermore, the proposed technique is robust to the noise which is usually present in MT-InSAR data. This opens the door for monitoring infrastructure at scale over very large areas, helping to monitor civil infrastructures, and providing relevant insights to asset owners regarding the performance of such structures over time.
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期刊：
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