Angular Consistency Adaptive Filtering and Complex Generalized Gaussian-Based Phase Linking for DS-InSAR

Distributed scatterer InSAR (DS-InSAR) processing is essential for retrieving surface deformation in areas lacking strong point targets. Conventional workflows typically involve selecting statistically homogeneous pixels (SHPs) based on amplitude similarity, followed by phase estimation under the complex circular Gaussian (CCG) 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 be unable to distinguish between them. Moreover, CCG-based SHP selection methods and phase estimators may lack robustness and suffer performance degradation under non-Rayleigh amplitude fluctuations. Focusing on the scale-invariant second-order statistics, we propose the Shape-to-Scale approach for DS-InSAR processing. The method first identifies pixels sharing a common angular scattering structure, which implies having a similar interferometric coherence structure (or equivalently, a similar decorrelation behavior). These pixels are referred to as shape statistically homogeneous pixels (SSHP) and are extracted using an angular consistency adaptive filter (ACAF) based on the complex angular central Gaussian (CACG) distribution. A phase estimation approach (phase-linking) derived from the complex generalized Gaussian (CGG) model is then applied, enhancing robustness to non-Rayleigh scattering. Experiments on both simulated and real SAR datasets showed that the proposed approach yielded more consistent coherence-structure clustering than the considered baselines, and improved the robustness of phase linking while preserving spatial details in heterogeneous and low-coherence areas. By explicitly separating shape (closely related to coherence structure) from scale (amplitude distribution), this work establishes a unified statistical framework that facilitates reliable and interpretable DS-InSAR time-series analysis.