@inproceedings{c74e3badd459493ca0bc7b985ed7cc5d,
title = "Insar Phase Reduction Using the Remove-Compute-Restore Method",
abstract = "Satellite InSAR time series are used to estimate the displacements of radar scatterers. This estimation problem includes the estimation of integer phase ambiguities, which is an ill-posed problem. Consequently, InSAR displacement estimation cannot yield unique solutions and may therefore be significantly biased. Here we show that phase reduction, using a priori information and the remove-compute-restore (RCR) methodology, is a viable way to solve this problem, as it reduces the likelihood of ambiguity errors. We found that application of this methodology to pastures on peat soils leads to a significant improvement in the estimated displacements. We assert that InSAR displacement estimation should always include an explicit statement on the first-order approximations and included assumptions on expected signal smoothness. We anticipate that a more systematic inclusion of the RCR method in standard processing algorithms will lead to more reliable and repeatable results of InSAR analyses.",
keywords = "Functional Model, InSAR, Phase Unwrapping, PS, Radar Interferometry, SBAS Ambiguity Resolution, Time Series",
author = "Heuff, {Floris M.G.} and Hanssen, {Ramon F.}",
year = "2020",
doi = "10.1109/IGARSS39084.2020.9323720",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "IEEE",
pages = "786--789",
booktitle = "2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings",
address = "United States",
note = "2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 ; Conference date: 26-09-2020 Through 02-10-2020",
}