Identification and Correction of Phase Unwrapping Errors in Multi-Temporal Small Baseline DInSAR Interferograms: a Three-Step Cascade Algorithm

We present an innovative solution to identify and correct possible Phase Unwrapping (PhU) errors, to improve the results obtained from a redundant sequence of multi-temporal, small baseline Differential SAR Interferometry (DInSAR) interferograms. In particular, the proposed algorithm is based on the cascade of three main steps: the first one consists in reducing the amount of pixels and interferograms to be analyzed for the errors identification by generating a novel parameter for the evaluation of the best possible corrections. To do this, we extend the temporal coherence parameter, originally defined as a single value point-like quality factor of the PhU operation, by sampling it into a series of values, that we refer to as Local Temporal Coherence (LTC) time series. This allows us to effectively select, for each identified pixel, a subset of interferograms to be evaluated as possible PhU errors candidates. The second step benefits from the Compressive Sensing theory and is based on a L1-norm inversion, to sharply identify the interferograms of the pixels selected through the previous step, which are more likely affected by the PhU errors. Once identified them, the third step consists in a guided search of the possible PhU corrections, for the examined pixels, by means of a Genetic Algorithm which maximizes the LTC time series. To evaluate the performance of the proposed algorithm we carry out a comparative analysis between the corrected and uncorrected results, obtained from simulated data relevant to different deformation regimes and a Sentinel-1 SAR dataset relevant to descending orbits over the Mt. Etna volcano (Sicily, southern Italy). The obtained results clearly demonstrate the effectiveness of the presented approach.