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Lookup NU author(s): Professor Zhenhong Li, Professor Jon MillsORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
Ground-Based Synthetic Aperture Radar (GBSAR) is a flexible field-based remote sensing technology that, together with interferometric SAR (InSAR), has proven to be a powerful and effective tool for deformation monitoring. The Small Baseline Subset (SBAS) algorithm represents a typical advanced InSAR technique that extracts distributed scatterers from a network of interferogramsfor the measurement of time series displacement. However, it is well known that coherent points are variable from one interferogram to another, which renders time series analysis complicated. This study therefore proposes an effective approach to selecting coherent pixels from a network of interferograms, aiming to maximize the density of selected pixels and optimize the reliability of GBSAR time series analysis. A pixel is selected for the entire analysis if its coherent phase is capable of forming a full-rank coefficient matrix in the network inversion. A full-rank matrix means the pixel-dependent subset network is connected. Combining with the accurate estimation of coherence and interferometric phase based on sibling pixels identified from non-local windows, the proposed approach enables the selection of not only qualified partially coherent pixels but also persistent scatterers. The proposed approach has been incorporated into a bespoke GBSAR time series analysis chain for deformation monitoring, from which a mean velocity map, displacement time series and atmospheric phase delays can be determined. To validate the approach, experiments on two GBSAR datasets were performed. In both studies, sufficient coherent pixels were selected, suggesting the feasibility of the proposed coherent pixel selection algorithm. Displacement time series at the level of a few sub-millimeters were observed for both datasets, indicating the feasibility of the newly-developed GBSAR time series analysis chain for deformation monitoring, which is believed to lead to a wide range of scientific and practical applications.
Author(s): Wang Z, Li Z, Mills JP
Publication type: Article
Publication status: Published
Journal: ISPRS Journal of Photogrammetry and Remote Sensing
Year: 2018
Volume: 144
Pages: 412-422
Print publication date: 01/10/2018
Online publication date: 17/08/2018
Acceptance date: 08/08/2018
Date deposited: 08/09/2018
ISSN (print): 0924-2716
ISSN (electronic): 1872-8235
Publisher: Elsevier BV
URL: https://doi.org/10.1016/j.isprsjprs.2018.08.008
DOI: 10.1016/j.isprsjprs.2018.08.008
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