Robust Two-Dimensional Phase Unwrapping for Multibaseline SAR Interferograms: A Two-Stage Programming Approach

Multibaseline 2-D phase unwrapping (PU) is a critical step for the multibaseline synthetic aperture radar interferometry. Compared with the single-baseline PU, the multibaseline PU does not need to obey the phase continuity assumption, i.e., it is applicable to the terrain with the violent change. H...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2016-09, Vol.54 (9), p.5217-5225
Main Authors: Yu, Hanwen, Lan, Yang
Format: Article
Language:English
Subjects:
2-D phase unwrapping (PU)
Integer programming
Linear programming
Multibaseline
Noise
Noise levels
Noise robustness
Optimization
Phase estimation
Programming
Residues
Robustness
single-baseline
Synthetic aperture radar
synthetic aperture radar interferometry
unimodular
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Summary:Multibaseline 2-D phase unwrapping (PU) is a critical step for the multibaseline synthetic aperture radar interferometry. Compared with the single-baseline PU, the multibaseline PU does not need to obey the phase continuity assumption, i.e., it is applicable to the terrain with the violent change. However, the performance of the multibaseline PU is directly related to noise level. In order to improve the noise robustness of the multibaseline PU, in this paper, we transplant the framework of the single-baseline PU into the multibaseline PU and propose a two-stage programming approach, referred to as TSPA, which makes use of the gradient information of the interferogram similar to how the conventional single-baseline PU method does. Fortunately, although the proposed method belongs to the integer programming (usually, the integer programming is an NP-hard problem which is hard to solve), the constraint of the optimization model of the TSPA method is unimodular, so it can be efficiently solved. Furthermore, interestingly, some useful and important concepts of the single-baseline PU, for example, residue and branch cut, are also transplanted into the multibaseline PU in this paper, and we discuss the potential of extending most of the representative single-baseline PU methods into the multibaseline domain as well. Finally, the experiment results show the effectiveness and noise robustness of the TSPA multibaseline PU method.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2016.2558541