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A Phase-Decomposition-Based PSInSAR Processing Method

A phase-decomposition-based persistent scatterer (PS) InSAR (PD-PSInSAR) method is developed in this paper to improve coherence and spatial density of measurement points (MPs). In order to improve PS network density, a distributed scatterer (DS) has been utilized in some advanced PSInSAR process, such as SqueeSAR. In addition to the conventional DS that consists of independent small scatterers with a uniform scattering mechanism, processing the DSs dominated by two or more scattering mechanisms is a promising way to improve MP density. Estimating phases from DS with multiple scattering mechanisms is difficult for many DS algorithms because of the interference between different scattering mechanisms. Recently, a covariance-matrix-decomposition-based method, which is named Component extrAction and sElection SAR (CAESAR), is proposed to extract different scattering components from the analysis of the covariance matrix. Instead of using a covariance matrix, the PD-PSInSAR in this study is developed to perform eigendecomposition on a coherence matrix, in order to estimate the phases corresponding to the different scattering mechanisms, and then to implement these estimated phases in a conventional PSInSAR process. The major benefit of using a coherence matrix rather than a covariance matrix is to compensate the amplitude unbalances among SAR images. A detailed study of comparison among SqueeSAR, CAESAR, and PD-PSInSAR is also performed in this study. It has been found that these three methods share very similar mathematic forms with different weight values. The PD-PSInSAR method is implemented to estimate land deformation over the greater Houston area using Envisat ASAR images, which verifies that the proposed method can detect more MPs and provide better coherences.