Abstract
Ground-based radar interferometry (GBSAR) is a useful method to control the stability of engineering objects and elements of geographical spaces at risk of deformation or displacement. To secure accurate and credible measurement results, it is crucial to consider atmospheric conditions as they influence the corrections to distance measurements. These conditions are especially important considering the radar bandwidth used. Measurements for the stability of engineering objects are not always performed in locations where meteorological monitoring is prevalent; however, information about the range of variability in atmospheric corrections is always welcome. The authors present a hybrid method to estimate the probable need of atmospheric corrections, which allows partly eliminating false positive alarms of deformations as caused by atmospheric fluctuations. Unlike the numerous publications on atmospheric reductions focused on the current state of the atmosphere, the proposed solution is based on applying a classic machine learning algorithm designed for the SARIMAX (Seasonal Autoregressive Integrated Moving Average with covariate at time) time series data model for satellite data shared by NASA (National Aeronautics and Space Administration) during the Landsat MODIS (Moderate Resolution Imaging Spectroradiometer) mission before performing residual estimation during the monitoring phase. Example calculations (proof of concept) were made for ten-year satellite data covering a region for experimental flood bank stability observations as performed using the IBIS-L (Image by Interferometric Survey—Landslide) radar and for target monitoring data (ground measurements).
Highlights
Ground-based radar interferometry (GBSAR) technology has been intensely developed in recent decades and is used to monitor displacements or deformations of buildings and engineering objects [1,2,3], as well as the movement of mass landslides on earthen and rocky mountainsides as caused by different factors [4,5]
Another approach appeared in Iglesias [16], where GBSAR observations in the X-series were adjusted based on permanent scatters and meteorological data and a distribution of atmospheric corrections based on a model for manifold regression using coherence (APS-MRM—atmospheric phase screen-multipleregression model)
The results show that the classic method of machine learning characteristic for a low calculation complexity can successfully lower the risk of false alarms in GBSAR monitoring
Summary
Ground-based radar interferometry (GBSAR) technology has been intensely developed in recent decades and is used to monitor displacements or deformations of buildings and engineering objects [1,2,3], as well as the movement of mass landslides on earthen and rocky mountainsides as caused by different factors [4,5]. Non-continuous measurements are for relatively slow movements in a measuring scene and where the designation does not need to be obtained continuously but based on observations made from multiple installations of a radar unit in a particular fixed place In this context, Wang [11] issued geometric corrections to GBSAR data due to unit reposition, which appears to be very important. Huang [10] offered a method of coherent point detection based on an analysis that included entropy modeling for the atmospheric correction field by applying the Delaney algorithm Another approach appeared in Iglesias [16], where GBSAR observations in the X-series were adjusted based on permanent scatters and meteorological data and a distribution of atmospheric corrections based on a model for manifold regression using coherence (APS-MRM—atmospheric phase screen-multipleregression model).
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