Potential of Using Phase Correlation in Distributed Scatterer InSAR Applied to Built Scenarios

Abstract

The improved spatial resolution of Synthetic Aperture Radar (SAR) images from newly launched sensors has promoted a more frequent use of distributed scatterer (DS) interferometry (DSI) in urban monitoring, pursuing sufficient and detailed measurements. However, the commonly used statistical methods for homogeneous pixel clustering by exploring amplitude information are firstly, computationally intensive; furthermore, their necessity when applied to high-coherent built scenarios is little discussed in the literature. This paper explores the potential of using phase information for the detection of homogeneous pixels on built surfaces. We propose a simple phase-correlated pixel (PCP) clustering and introduce a coherence-weighted phase link (WPL), i.e., PCPWPL, to pursue a faster processing of interferogram phase denoising. Rather than relying on the statistical tests of amplitude characteristics, we exploit vector correlation in the complex domain to identify PCPs with similar phase observations, thus, avoiding the intensive hypothesis test. A coherence-weighted phase linking is applied for DS phase reconstruction. The estimation of geophysical parameters, e.g., deformation, is completed using an integrated network of persistent scatterers (PS) and DS. Efficiency of the proposed method is fairly illustrated by both synthetic and real data experiments. Pros and cons of the proposed PCPWPL were analyzed with the comparison to a conventional amplitude-based strategy using an X-band CosmoSkyMed dataset. It is demonstrated that the use of phase correlation is sufficient for DS monitoring in built scenarios, with equivalent measurement quantity and cheaper computational cost.

Analysis of Groundwater Depletion/Inflation and Freeze–Thaw Cycles in the Northern Urumqi Region with the SBAS Technique and an Adjusted Network of Interferograms

“…The study of the Earth's surface displacements through differential SAR interferometry represents a consolidated practice [1][2][3][4] at present. In this framework, the last few decades have seen the flourishing of several multi-temporal InSAR (Mt-InSAR) algorithms for the generation of ground displacement time series [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. They can be broadly grouped into two main categories: persistent scatterer (PS) and small baseline (SB) methodologies.…”

“…The correct identification of DSs is required, and several techniques are currently available in the literature [19,20]. More recently, new methods for the identification and processing of statistically homogenous pixels (SHPs), based on the use of maximum likelihood (ML) estimators, have also been developed [13][14][15][16][17]. Moreover, some scholars have also presented hybrid Mt-InSAR techniques that jointly analyze PS and DS target ground deformations (e.g., [18]).…”