Statistically-Based Trend Analysis of MTInSAR Displacement Time Series

Current multi-temporal interferometric Synthetic Aperture Radar (MTInSAR) datasets cover long time periods with regular temporal sampling. This allows high-rate and non-linear trends to be observed, which typically characterize pre-failure warning signals. In order fully exploit the content of MTInSAR products, methods are needed for automatic identification relevant changes along displacement series classification targets on ground according their kinematic regime. work reviews some classical procedures model ranking, based statistical indices, applied characterization series, introduces a new quality index Fisher distribution. Then, we propose procedure recognize automatically minimum number parameters given reliably within predefined confidence level. The method, though general, is explored here polynomial models, can used in particular approximate satisfactorily computational efficiency piecewise linear that generally signals preceding failure natural artificial structures. algorithm performance evaluated under simulated scenarios. Finally, proposed also demonstrated derived by processing Sentinel-1 data.