Abstract
In this work, the statistical fundaments of the recently proposed enhanced, multi-temporal interferometric synthetic aperture radar (InSAR) noise-filtering (E-MTInSAR) technique is addressed. The adopted noise-filtering algorithm is incorporated into the improved extended Minimum Cost Flow (EMCF) Small Baseline Subset (SBAS) differential interferometric SAR (InSAR) processing chain, which has extensively been used for the generation of Earth’s surface displacement time-series in several different contexts. Originally, the input of the InSAR EMCF-SBAS processing toolbox consisted of a sequence of multi-looked, small baseline interferograms, which were unwrapped using the space-time EMCF phase unwrapping algorithm. Subsequently, the unwrapped interferograms were inverted through the SBAS algorithm to retrieve the expected InSAR deformation products. The improved processing chain has complemented the original codes with two additional steps. In particular, a new multi-temporal noise-filtering algorithm for sequences of time-redundant multi-looked DInSAR interferograms, followed by a proper interferogram selection step, has been proposed. This research study is aimed at primarily assessing the performance of the E-MTInSAR noise-filtering algorithm from a theoretical perspective. To this aim, the principles of directional statistics and errors propagation are exploited. Experimental results, carried out by applying the E-MTInSAR algorithm to a sequence of SAR data collected over the Los Angeles bay area, have been used to corroborate the academic outcome of this research.
Highlights
Multi-temporal Interferometric synthetic aperture radar (MTInSAR) techniques [1,2,3,4,5,6,7,8,9,10,11] are nowadays well recognized as valuable and essential tools for the detection and monitoring of temporal changes of Earth’s surface
This research provided new insights into the theory at the base of the enhanced multi-temporal noise-filtering method, which was initially proposed as a further improvement of the extended minimum cost flow (EMCF)-based Small Baseline Subset (SBAS) processing chain [26]
This research permitted studying the statistical behavior of the adopted estimator for the unknown phases related to every SAR acquisitions
Summary
Multi-temporal Interferometric synthetic aperture radar (MTInSAR) techniques [1,2,3,4,5,6,7,8,9,10,11] are nowadays well recognized as valuable and essential tools for the detection and monitoring of temporal changes of Earth’s surface. For what attains the SB methods, they are concentrated on detecting and monitoring the ground displacement signals related to distributed targets (DS) on the ground, which are more prone to be corrupted by spatial and temporal decorrelation phenomena [12,13] To cope with this issue, efficiently, multiple-master InSAR data pairs, characterized by small temporal and perpendicular baselines, are selected. It complements the extended minimum cost flow (EMCF) space-time phase unwrapping operations [28] with two innovative additional processing steps The former is the E-MTInSAR noise filtering technique, which exploits the inherent temporal relationships among a sequence of time-redundant, multi-looked InSAR interferograms. The identified interferograms are generated and exploited by the subsequent phase unwrapping operations that are carried out by applying the efficient multi-temporal EMCF PhU technique [28]
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