Efficient Phase Estimation for Interferogram Stacks

Abstract

Signal decorrelation poses a limitation to multipass SAR interferometry. In pursuit of overcoming this limitation to achieve high-precision deformation estimates, different techniques have been developed, with short baseline subset, SqueeSAR, and CAESAR as the overarching schemes. These different analysis approaches raise the question of their efficiency and limitation in phase and consequently deformation estimation. This contribution first addresses this question and then proposes a new estimator with improved performance, called Eigendecomposition-based Maximum-likelihood-estimator of Interferometric phase (EMI). The proposed estimator combines the advantages of the state-of-the-art techniques. Identical to CAESAR, EMI is solved using eigendecomposition; it is therefore computationally efficient and straightforward in implementation. Similar to SqueeSAR, EMI is a maximumlikelihood-estimator; hence, it retains estimation efficiency. The computational and estimation efficiency of EMI renders it as an optimum choice for phase estimation. A further marriage of EMI with the proposed Sequential Estimator by Ansari et al. provides an efficient processing scheme tailored to the analysis of Big InSAR Data. EMI is formulated and verified in relation to the state-of-the-art approaches via mathematical formulation, simulation analysis, and experiments with time series of Sentinel-1 data over the volcanic island of Vulcano, Italy.

EMI: Efficient Temporal Phase Estimation and its Impact on High-Precision InSAR Time Series Analysis

“…Recalling that temporal phase estimation retrieves the consistent common-master interferograms from the time series, it may be reformulated into the problem of modeling the SCM [3,2].…”

“…The PTA is however sensitive to the estimation performance of the coherence matrix Γ [7]. The latter is notoriously known to be sub-optimum for low coherence level and small l. PTA estimates the phases through a non-linear optimization scheme with subjective choice of initialization [2]. Therefore its computational expense poses a challenge to Big Data processing [7].…”

“…Therefore, they are computationally efficient and well suited to the processing of long SAR time series. However, the EVD's model inadequacy affects the estimation efficiency of these estimators as compared to MLEs and the CRLB [2]. EVDs therefore provide an approximate solution to temporal phase estimation.…”

“…It allows for the calibration of Γ or in other words relaxes the strict model proposed by PTA. Furthermore, in order to gain computational efficiency, the MLE based on this model is reformulated into a Lagrangian [2] and solved via the Eigendecomposition of the matrix product:…”

“…Different approaches to temporal phase estimation may be categorized to two groups, namely the Maximum Likelihood Estimators (MLE) and the approximate Eigendecompositionbased (EVD) estimators. Here we shed light on the underlying model and pros and cons of these approaches while introducing a new MLE which overcomes the disadvantages of the aforementioned approaches [2]. Called Eigendecomposition-based Maximum likelihood estimator of Interferometric phase (EMI), the proposed estimator is shown to be efficient both in terms of estimation precision and computational cost.…”