Combination of Persistent Scatterer Interferometry and Single-Baseline Polarimetric Coherence Optimisation to Estimate Deformation Rates with Application to Tehran Basin

Abstract

This study reports on the monitoring of land subsidence in a rural area located in the southwest of the Tehran basin, Iran, by combining a persistent scatterer interferometry (PSI) method with a single-baseline polarimetric coherence optimisation. Owing to vegetation coverage in this rural area, coherence level experiences a decline and the performance and coverage of conventional interferometry to estimate deformation rate reduces concomitantly. Since the launch of satellites with polarimetric information, the polarimetric InSAR (PolInSAR) technique, which is vector interferometry with different polarimetric channels, has been introduced to optimise the coherence level. One of the most common criteria to select PS pixels is coherence and maximising the coherence can lead to an increased number of selected PS pixels and enhanced PSI performance. The single-baseline polarimetric coherence optimisation method assumes equal polarisation states at the end of each baseline. In order to apply this technique in our study, two different multi-look windows for coherence calculation and also two TerraSAR-X data sets with different numbers of images are used to assess their effect on the polarimetric PSI. Combi- nation of the single-baseline coherence optimisation method with PSI shows significant improvements (more than 50%) in terms of the number of selected PS pixels in the case study even using a data set with a small number of images. A 15 × 15 multi-look window selects a greater number of PS pixels compared to a 9 × 9 multi-look window, although this entails reducing spatial resolution. The most effective PSI approach in terms of the density of the selected PS turned out to be polarimetric PSI using a data set with a large number of images and a selection of a 15 × 15 multi-look window. Validation of the PSI methods using a large number of images with 9 × 9 and 15 × 15 multi-look windows via levelling measurements confirms the accuracy and reliability of the results obtained.

SMF-POLOPT: An Adaptive Multitemporal Pol(DIn)SAR Filtering and Phase Optimization Algorithm for PSI Applications

“…Conventional PolPSI techniques improve interferograms through either the optimization of an amplitude-based criterion (e.g. D A , suitable for PSs) [13]- [15], [23], [24] or the maximization of the coherence stability (γ, suitable for DSs) [13]- [15], [20]- [22], [25]- [28]. Another phase quality metric, the temporal sublook coherence (TSC) [29], [30], has also been proposed to optimize interferograms [30], and it is effective mainly for point-like scatterers (i.e.…”

Three dimensional temporal-spatial model based SAR algorithms for monitoring large gradient deformations in mining area

“…This noise harms the observation of distributed deformation and is not able to be detected by SNR indicators. On the other hand, as more satellite sensors with polarimetric capabilities have been launched, pixel selection of PSI is improved by Polarimetric Data based Phase Optimizing Methods (PDPOM) and Polarimetric Data based Amplitude Optimizing Methods (PDAOM) [29][30][31][32][33][34][35][36][37][38][39][40]. By searching the optimal solution from multidimensional space constructed from polarization data, noise can be reduced and PS number is increased.…”

“…For PSI, polarimetric data based amplitude and phase optimizing methods improve the performance of deformation detection and characterization by increasing the density and quality of valid pixels with respect to the single polarization case [29][30][31][32][34][35][36][37][38][39][40][74][75][76][77][78][79]. The main idea of polarimetric optimization is to construct a polarization space and find the optimal projection of the polar vector in this space.…”