Knowledge-Aided InSAR Phase Unwrapping Approach
Hanwen Yu, Xie Hu
Abstract
2-D phase unwrapping (PU) is one of the biggest challenges in synthetic aperture radar (SAR) interferometry (InSAR) processing. As an ill-posed problem, the performance of the traditional algorithmic model-based 2-D PU algorithms is not guaranteed to be correct with rapid ground deformation or topographic changes. An increasing number of remote sensing observations collected by different sensors (e.g., LiDAR and GPS) provides new opportunities to assist the traditional 2-D InSAR PU by reducing the nondeterminacy. In this article, we propose a novel knowledge-aided PU (KAPU) approach. KAPU compiles different prior knowledge from different sources with InSAR observations simultaneously through an integer programming model. More importantly, the mathematical proof demonstrates that the constraint of the optimization model of KAPU is totally unimodular, so KAPU can be efficiently solved without having to have the constraint that the ambiguity number is an integer. Theoretical analysis and extensive experimental results illustrate that KAPU outperforms the existing model-based 2-D InSAR PU algorithms on digital elevation model (DEM) generation and surface deformation estimation.