Shape-to-Scale InSAR Adaptive Filtering and Phase Linking under Complex Elliptical Models

Abstract

Distributed scatterers in InSAR (DS-InSAR) processing are essential for retrieving surface deformation in areas lacking strong point targets. Conventional workflows typically involve selecting statistically homogeneous pixels based on amplitude similarity, followed by phase estimation under the complex circular Gaussian model. However, amplitude statistics primarily reflect the backscattering strength of surface targets and may not sufficiently capture differences in decorrelation behavior. For example, when distinct scatterers exhibit similar backscatter strength but differ in coherence, amplitude-based selection methods may fail to differentiate them. Moreover, CCG-based phase estimators may lack robustness and suffer performance degradation under non-Rayleigh amplitude fluctuations. Centered around scale-invariant second-order statistics, we propose ``Shape-to-Scale,'' a novel DS-InSAR framework. We first identify pixels that share a common angular scattering structure (``shape statistically homogeneous pixels'') with an angular consistency adaptive filter: a parametric selection method based on the complex angular central Gaussian distribution. Then, we introduce a complex generalized Gaussian-based phase estimation approach that is robust to potential non-Rayleigh scattering. Experiments on both simulated and SAR datasets show that the proposed framework improves coherence structure clustering and enhances phase estimation robustness. This work provides a unified and physically interpretable strategy for DS-InSAR processing and offers new insights for high-resolution SAR time series analysis.