Abstract
The offset tracking approach has been widely used to measure large ground deformation as a complement to Interferometric Synthetic Aperture Radar (InSAR) when its coherence is poor and/or the deformation gradient is large. The standard offset tracking procedures estimate deformation of tie points, which are uniformly distributed over two SAR images, resulting in many unsatisfactory measurements. In this paper, we propose a feature point offset tracking (FPOT) procedure to overcome the limitation of the standard method. First, we identify feature points using the Speeded Up Robust Feature (SURF) algorithm. Improper feature points are masked using external land coverage information like water coverages. Then, we use the standard cross-correlation algorithm to find offsets of the remaining feature points between reference and secondary images. The offset outliers are removed using a quadtree filtering. Finally, the resultant deformation field is generated by removing systematic offsets estimated with far-field feature points. We assess the effectiveness of our proposed procedure using the 2016 Mw 7.8 Kaikōura earthquake in New Zealand. In far-field where deformation is expected to be negligible, histograms of offset distribution show that the root-mean-square error (RMSE) is decreased from 0.07 pixels to 0.02–0.03 pixels for regular points and feature points, respectively, after quadtree filtering. The RMSE between our FPOT-derived offsets and GPS measurements are 0.14 and 0.48 m for range and azimuth offsets, respectively. The results show that our proposed procedure can significantly improve the efficiency, accuracy, and reliability with respect to the standard regular point offset tracking (RPOT).
Highlights
Interferometric Synthetic Aperture Radar (InSAR) is widely used in natural disaster monitoring because of its large coverage, all-day time, high accuracy, and unaffected by clouds (Elliott et al, 2016; Hooper et al, 2012; Wang et al, 2019; Wright et al, 2013)
We propose a feature point offset tracking (FPOT) procedure to improve the spatial resolution, reliability, and computing efficiency of the conventional method
To test the feasibility of feature points for routine InSAR coregistration, we evaluate the accuracy of the coregistration affine matrix fitted using feature and regular points, respectively
Summary
Interferometric Synthetic Aperture Radar (InSAR) is widely used in natural disaster monitoring because of its large coverage, all-day time, high accuracy, and unaffected by clouds (Elliott et al, 2016; Hooper et al, 2012; Wang et al, 2019; Wright et al, 2013). When the deformation gradient is up to half wavelength within a single pixel, aliasing phenomenon will occur during the phase unwrapping process (Goldstein et al, 2016; Massonnet and Feigl, 1998). InSAR is limited for measuring deformation in an area with strong decorrelation noise (Michel et al, 1999). This usually occurs in the near field of coseismic rupturing (Lasserre et al, 2005; Wang et al, 2014), crater of volcanic eruptions (Pagli et al, 2007; Sturkell et al, 2006), and sinking holes of mining subsidence (Ng et al, 2017). Instead of measuring deformation via interferometric phase, the pixel offset tracking (POT) method extracts surface displacements by estimating pixel
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