Abstract
Accurate phase unwrapping (PU) is a precondition and key for using synthetic aperture radar interferometry (InSAR) technology to successfully invert topography and monitor surface deformations. However, most interferograms are seriously polluted by noise in the low-quality regions, which poses difficulties for PU. Therefore, using the strategy of leveling network adjustment, this paper proposes an improved PU method based on hierarchical networking and constrained adjustment. This method not only limits the phase error transfer of low-quality points, but also takes the PU results of high-quality points as control points and uses the network adjustment method with constraints to unwrap low-quality points, which effectively inhibits the influence of noise and improves the accuracy of unwrapping. Regardless of the unwrapping method used for high-quality points, the unwrapping accuracy of low-quality points can always be improved. Compared with other traditional two-dimensional phase unwrapping workflows, this method can more accurately recover the phase of low-coherence regions only through the interferogram. A simulation experiment showed that the local noise of the interferogram was effectively inhibited, and the PU accuracy of the low-quality regions was improved by 16–46% compared with different traditional methods. For a real-data experiment of mining area with low coherence, the PU result of our proposed method had fewer residues and lower phase standard deviation than traditional methods, further indicating the practicability and robustness of the proposed method. The work in this paper has considerable practical significance for recovering the decoherence phase with serious local noise such as mining centers and groundwater subsidence centers.
Highlights
Interferometric synthetic aperture radar (InSAR) is the combination of synthetic aperture radar (SAR) and interferometry, which obtains two single look complex (SLC) images of the same area on the ground through two antennas or at different times, which are nearly parallel observations, to obtain ground elevation according to the orbit parameters and interferometric phase information of the sensor during flight [1]
On the basis of existing phase unwrapping (PU) theory, aiming at the problem of large PU error in lowquality areas, this paper proposes a PU method based on hierarchical networking and constrained adjustment (HNCA)
In order to verify the PU ability and anti-noise performance of the proposed algorithm, the Peaks function was used to construct the simulated phase data, the noise calculated by the real coherence [37,38] was added to the simulated phase, and the simulated interferogram was obtained after the rewrapping operation
Summary
Interferometric synthetic aperture radar (InSAR) is the combination of synthetic aperture radar (SAR) and interferometry, which obtains two single look complex (SLC) images of the same area on the ground through two antennas or at different times, which are nearly parallel observations, to obtain ground elevation according to the orbit parameters and interferometric phase information of the sensor during flight [1]. The interference phase obtained by the interferometric processing of the SAR image is wrapped between (−π, π ), and the entire cycle information is lost. To restore the real phase representing the actual deformation or topography, it is necessary to add or subtract the integer multiple of 2π on the basis of the wrapped phase to restore its real phase, which is called phase unwrapping (PU) [14]. Further work is required to determine how to accurately and efficiently perform PU, being a difficult problem and a hot topic in research
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