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Fully Integrated Temporary Persistent Scatterer Interferometry

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The persistent scatterer interferometry (PSI) is a powerful remote sensing technique to monitor displacements of the Earth’s surface. It is based on identifying and analyzing phase-stable scatterers undergoing marginal decorrelation… Click to show full abstract

The persistent scatterer interferometry (PSI) is a powerful remote sensing technique to monitor displacements of the Earth’s surface. It is based on identifying and analyzing phase-stable scatterers undergoing marginal decorrelation over time. Most PSI approaches constrain the analysis to scatterers which are coherent over the whole considered time series persistent scatterer (PS) and neglect scatterers which are only temporary persistent (TPS). Here, we propose a method to fully integrate TPS into an existing PSI approach which is characterized by an iterative parameter estimation with subsequent phase unwrapping. We build on a previously published method to identify TPS and estimate their coherent lifetime based on amplitude statistics. A phase-based likelihood ratio test is proposed to iteratively refine the appearing/fading dates of TPS during parameter estimation. Finally, we jointly unwrap the phase observations of PS and TPS to receive displacement time series. The temporal data of TPS are redefined if their lifetime does not cover the selected master scene. Experimental results based on Sentinel-1 data in the Vietnamese city of Ca Mau show that the change date refinement significantly increases the average coherence and number of identified TPS. The densification of the observation point network by incorporating TPS helps better detect and understand displacement phenomena. The displacement time series of TPS are beneficial to analyze the nonlinear motion in connection with urban development, like initial settlement of newly constructed buildings.

Keywords: time; scatterer interferometry; persistent scatterer; temporary persistent; scatterer

Journal Title: IEEE Transactions on Geoscience and Remote Sensing
Year Published: 2022

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