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Probabilistic Estimation of InSAR Displacement Phase Guided by Contextual Information and Artificial Intelligence
LOSS researchers Philip Conroy, Gilles Erkens, and Ramon Hanssen published a paper in IEEE Transactions on Geoscience and Remote Sensing.
The abstract reads as follows:
Phase unwrapping, also known as ambiguity resolution, is an underdetermined problem in which assumptions must be made in order to obtain a result in SAR interferometry (InSAR) time series analysis. This problem is particularly acute for distributed scatterer InSAR, in which noise levels can be so large that they are comparable in magnitude to the signal of investigation. Additionally, deformation rates can be highly nonlinear and orders of magnitude larger than neighboring point scatterers, which may be part of a more stable object. The combination of these factors has often proven too challenging for conventional InSAR processing methods to successfully monitor these regions. We present a methodology which allows for additional environmental information to be integrated into the phase unwrapping procedure, thereby alleviating the problems described above. We show how problematic epochs that cause errors in the temporal phase unwrapping process can be anticipated by machine learning algorithms which can create categorical predictions about the relative ambiguity level based on readily-available meteorological data. These predictions significantly assist in the interpretation of large changes in the wrapped interferometric phase and enable the monitoring of environments not previously possible using standard minimum-gradient phase unwrapping techniques.
You can read more about Philip’s work in the section on WP1.1.