Iterative Filtering Based on Adaptive Chebyshev Kernel Functions for Noise Supression in Differential SAR Interferograms

Differential SAR Interferometry (DInSAR) is a powerful remote sensing technique employed to monitor surface displacements, such as ground subsidence or strong deformations caused by geological activity. The quality of the interferometric phase between two combined SAR images is essential for the estimation of the surface deformation. Multi-pIe decorrelation factors may degrade the quality of the measurements and, then, the development of filtering methods for noise suppression is mandatory. In this work, we propose a new strategy to improve noise reduction while preserving the original phase structure. The new method consists in an iterative filter in which noise reduction is achieved progressively. The original phase is filtered with adaptive kernels based on Chebyshev interpolation functions. The filter is especially useful for DInSAR geophysical applications, such as earthquakes or volcanic eruptions monitoring. The performance of the proposed method has been tested with both simulated data and recently acquired Sentinel-1 SAR data which mapped the August 2016 Central Italy earthquake.This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO), the State Agency of Research (AEI) and the European Funds for Regional Development (FEDER) under Projects TIN2014-55413-C2-2-P and TEC2017-85244-C2-1-P. This work was partially supported by the UK Natural Environmental Research Council (NERC) through the “Looking Inside the Continents (LiCS)” (NE/K011006/1), the “Rapid deployment of a seismic array in Ecuador following the April 16th 2016 M7.8 Pedernales earthquake” (NE/P008828/1), and the Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET, GA/13/M/031, http://comet.nerc.ac.uk) projects