Improved DInSAR time-series reconstruction in the presence of phase unwrapping errors using Huber-norm

Time-series reconstruction in differential interferometric synthetic aperture radar (DInSAR) requires solving a regression problem and can be used to monitor surface deformation. In this study, the authors present an improved time-series reconstruction for DInSAR using Huber-norm minimisation. The Huber-norm combines l(2)-norm and l(1)-norm based on a threshold retaining features of both norms. Unlike existing techniques based on either l(2)-norm or l(1)-norm minimisation, their proposed technique has better reconstruction performance in the presence of both outlier-like phase unwrapping errors and additive Gaussian noise, as a Huber density can model these errors. They first validate the proposed technique using real European Remote Sensing satellite data. To further analyse the performance, they simulate phase data according to different deformation models with phase unwrapping errors and additive Gaussian noise. They show via Monte-Carlo simulations that their method has superior performance, compared to the existing approaches. They further verify the performance of the proposed technique with respect to the threshold parameter and for a varying percentage of phase unwrapping errors.